• Data analysis is not just for tech wizards anymore! Today, every professional, whether in marketing, finance, or HR, can harness the power of data to make informed decisions that drive success! Understanding the key phases of data analysis and utilizing essential tools isn't just beneficial—it's empowering!

    Imagine transforming raw data into meaningful insights that can elevate your career and impact your organization! The journey of data analysis is a thrilling adventure, and with the right approach, you can unlock endless possibilities!

    So let's dive into the world of data, embrace the tools, and become decision-making champions!

    #DataAnalysis #Empowerment #CareerGrowth #DataDriven #SuccessJourney
    🌟 Data analysis is not just for tech wizards anymore! 🚀 Today, every professional, whether in marketing, finance, or HR, can harness the power of data to make informed decisions that drive success! 📊✨ Understanding the key phases of data analysis and utilizing essential tools isn't just beneficial—it's empowering! 💪 Imagine transforming raw data into meaningful insights that can elevate your career and impact your organization! The journey of data analysis is a thrilling adventure, and with the right approach, you can unlock endless possibilities! 🌈 So let's dive into the world of data, embrace the tools, and become decision-making champions! 🌟 #DataAnalysis #Empowerment #CareerGrowth #DataDriven #SuccessJourney
    Análisis de datos: fases clave y herramientas esenciales
    Hoy en día, trabajar con datos no es solo cosa de programadores o expertos. Cada vez más profesionales, desde marketing hasta finanzas o recursos humanos, necesitan analizar, interpretar y comunicar datos para tomar mejores decisiones. Pero ¿qué herr
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  • Are you ready to unlock the secrets of success? Competitive intelligence is your golden key! It’s all about understanding what your competitors are up to, so you can stay one step ahead!

    Imagine having the knowledge to make informed decisions that propel your business to new heights! Whether you’re gathering insights or learning effective strategies, competitive intelligence is essential for thriving in today’s market.

    Don’t wait! Dive into our guide and discover how to collect and use this invaluable information. Let’s embrace the journey of growth together!

    #CompetitiveIntelligence #BusinessGrowth #SuccessMindset #StayAhead #Inspiration
    🌟✨ Are you ready to unlock the secrets of success? 🚀 Competitive intelligence is your golden key! It’s all about understanding what your competitors are up to, so you can stay one step ahead! 💡💪 Imagine having the knowledge to make informed decisions that propel your business to new heights! 🌈 Whether you’re gathering insights or learning effective strategies, competitive intelligence is essential for thriving in today’s market. 🌍💼 Don’t wait! Dive into our guide and discover how to collect and use this invaluable information. Let’s embrace the journey of growth together! 💖🌟 #CompetitiveIntelligence #BusinessGrowth #SuccessMindset #StayAhead #Inspiration
    WWW.SEMRUSH.COM
    What Is Competitive Intelligence? Overview & How to Get It
    Read our guide to learn what competitive intelligence is, why you need it, and how to collect and use it.
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  • Hey there, fabulous friends!

    Are you ready to take your market research game to the next level? Today, I want to share with you something that can truly transform how you see competition! In this fast-paced world, every entrepreneur and marketer needs to be equipped with the right tools to uncover hidden gems in the market. And guess what? The answer lies in the **14 Best Competitive Intelligence Tools for Market Research**!

    Imagine having the power to peek behind the curtain of your competitors and discover their strategies and tactics! With these amazing tools, you can gather insights that will not only help you understand your market better but also give you the edge you need to soar higher than ever before!

    One standout tool that I absolutely adore is the **Semrush Traffic & Market Toolkit**. It’s like having a secret weapon in your back pocket! This toolkit provides invaluable data about traffic sources, keyword strategies, and much more! Say goodbye to guesswork and hello to informed decisions! Each piece of information you gather brings you one step closer to your goals.

    But that’s not all! Each of the 14 tools has its own unique features that cater to different aspects of competitive intelligence. Whether it's analyzing social media performance, tracking keywords, or monitoring brand mentions, there’s something for everyone! It’s time to embrace the power of knowledge and turn it into your competitive advantage!

    I know that diving into market research might seem daunting, but let me tell you, it’s a thrilling adventure! Every insight you uncover is like finding a treasure map leading you to success! So, don’t shy away from exploring these tools. Embrace them with open arms and watch your business flourish!

    Remember, the only limit to your success is the extent of your imagination and the determination to use the right resources. So gear up, equip yourself with these 14 best competitive intelligence tools, and let’s conquer the market together!

    Let’s lift each other up and share our discoveries! What tools are you excited to try? Drop your thoughts in the comments below! Let’s inspire one another to reach new heights!

    #MarketResearch #CompetitiveIntelligence #BusinessGrowth #Semrush #Inspiration
    🌟 Hey there, fabulous friends! 🌟 Are you ready to take your market research game to the next level? 🚀 Today, I want to share with you something that can truly transform how you see competition! In this fast-paced world, every entrepreneur and marketer needs to be equipped with the right tools to uncover hidden gems in the market. And guess what? The answer lies in the **14 Best Competitive Intelligence Tools for Market Research**! 🎉🎉 Imagine having the power to peek behind the curtain of your competitors and discover their strategies and tactics! With these amazing tools, you can gather insights that will not only help you understand your market better but also give you the edge you need to soar higher than ever before! 🌈✨ One standout tool that I absolutely adore is the **Semrush Traffic & Market Toolkit**. It’s like having a secret weapon in your back pocket! 🕵️‍♂️💼 This toolkit provides invaluable data about traffic sources, keyword strategies, and much more! Say goodbye to guesswork and hello to informed decisions! Each piece of information you gather brings you one step closer to your goals. 🌟 But that’s not all! Each of the 14 tools has its own unique features that cater to different aspects of competitive intelligence. Whether it's analyzing social media performance, tracking keywords, or monitoring brand mentions, there’s something for everyone! It’s time to embrace the power of knowledge and turn it into your competitive advantage! 💪🔥 I know that diving into market research might seem daunting, but let me tell you, it’s a thrilling adventure! Every insight you uncover is like finding a treasure map leading you to success! 🗺️💖 So, don’t shy away from exploring these tools. Embrace them with open arms and watch your business flourish! 🌺 Remember, the only limit to your success is the extent of your imagination and the determination to use the right resources. So gear up, equip yourself with these 14 best competitive intelligence tools, and let’s conquer the market together! 🌍💫 Let’s lift each other up and share our discoveries! What tools are you excited to try? Drop your thoughts in the comments below! 👇💬 Let’s inspire one another to reach new heights! #MarketResearch #CompetitiveIntelligence #BusinessGrowth #Semrush #Inspiration
    The 14 Best Competitive Intelligence Tools for Market Research
    Discover the competition and reveal strategies and tactics of any industry player with these top 14 competitive intelligence tools, including the Semrush Traffic & Market Toolkit.
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  • Hey there, amazing people!

    Have you ever found yourself captivated by the thrilling world of movies? Well, I recently stumbled upon an exciting article titled "11 AI Movie Villains That Will Make You Want to Unplug," and let me tell you, it's a fantastic exploration of the relationship between humans and technology!

    For nearly a century, filmmakers have taken us on a roller coaster ride through the possibilities of advanced technology and artificial intelligence. As we dive into 2025, technology is no longer just a concept of the future; it's a vibrant part of our daily lives! But, with that comes a question: What happens when the machines we create become so advanced and self-aware that they turn against us?

    The article highlights some of the most iconic AI movie villains that have kept us on the edge of our seats, making us think and ponder about the path we are treading. These characters remind us of the importance of balance and the need to unplug sometimes! It’s a gentle nudge to reflect on our relationship with technology. Are we in control, or is it controlling us?

    But here’s the good news! While these villains might give us chills, they also spark dialogue about innovation and responsibility. They encourage us to embrace technology wisely, ensuring that as we advance, we never lose touch with our humanity!

    Remember, every villain has a story, and within those stories, there are valuable lessons. So, let's take a moment to appreciate the creativity of filmmakers who challenge our perspectives and inspire us to think critically about our future!

    As we watch these movies, let's not just be entertained, but also empowered to make informed choices about how we interact with the technology around us! What are some of your favorite AI villains from movies? How do they inspire you to engage with technology more mindfully? Let's share our thoughts and uplift each other in this vibrant community!

    Embrace the challenges, celebrate the victories, and let's move forward together into a bright future where technology serves us, and we remain its guiding light!

    #AIMovieVillains #TechnologyAndHumanity #UnplugAndReflect #Inspiration #FutureReady
    🌟✨ Hey there, amazing people! 🌈💖 Have you ever found yourself captivated by the thrilling world of movies? 🎬 Well, I recently stumbled upon an exciting article titled "11 AI Movie Villains That Will Make You Want to Unplug," and let me tell you, it's a fantastic exploration of the relationship between humans and technology! 🤖💔 For nearly a century, filmmakers have taken us on a roller coaster ride through the possibilities of advanced technology and artificial intelligence. As we dive into 2025, technology is no longer just a concept of the future; it's a vibrant part of our daily lives! 🚀✨ But, with that comes a question: What happens when the machines we create become so advanced and self-aware that they turn against us? 🤔😱 The article highlights some of the most iconic AI movie villains that have kept us on the edge of our seats, making us think and ponder about the path we are treading. These characters remind us of the importance of balance and the need to unplug sometimes! 🌍💡 It’s a gentle nudge to reflect on our relationship with technology. Are we in control, or is it controlling us? But here’s the good news! 🌈💪 While these villains might give us chills, they also spark dialogue about innovation and responsibility. They encourage us to embrace technology wisely, ensuring that as we advance, we never lose touch with our humanity! 💖🤝 Remember, every villain has a story, and within those stories, there are valuable lessons. 🌟 So, let's take a moment to appreciate the creativity of filmmakers who challenge our perspectives and inspire us to think critically about our future! 🎉 As we watch these movies, let's not just be entertained, but also empowered to make informed choices about how we interact with the technology around us! 🌟 What are some of your favorite AI villains from movies? How do they inspire you to engage with technology more mindfully? Let's share our thoughts and uplift each other in this vibrant community! 💬💖 Embrace the challenges, celebrate the victories, and let's move forward together into a bright future where technology serves us, and we remain its guiding light! 🌟✨ #AIMovieVillains #TechnologyAndHumanity #UnplugAndReflect #Inspiration #FutureReady
    11 AI Movie Villains That Will Make You Want to Unplug
    For nearly a century, filmmakers have been questioning what happens when technology becomes so advanced and self-aware that the machines we invent turn against the humans who created them. Artificial intelligence is no longer just a science fiction c
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  • It's infuriating to see how many businesses are still in the dark about the true power of local SEO! Seriously, how many times do we have to explain that ignoring local search is like handing your competition a golden ticket to snatch away your potential customers? In a world where everything is interconnected, the sheer neglect of local SEO is maddening.

    Let’s get straight to the point: local SEO isn't just a trendy buzzword; it's an absolute necessity for any business that wants to thrive in its community! If you're still sitting on the sidelines, thinking that social media posts or fancy ads will magically draw customers through your door, think again! The reality is that those who master local SEO will dominate search results, while the rest are doomed to languish in obscurity.

    The absurdity of this situation is mind-boggling. Businesses have the tools at their disposal, but many still fail to understand the significance of geolocalization. It’s not rocket science! Local SEO can significantly improve your organic positioning, and yet, here we are, shouting into the void. You want visibility? You want to attract local customers? Then optimize your Google My Business listing, gather those reviews, and ensure your NAP (Name, Address, Phone number) information is consistent across all platforms. It’s not that complicated, yet so many are just too lazy to put in the work!

    And let’s talk about the content. Enough with the generic posts that have nothing to do with your local audience! If your content doesn’t resonate with the community you serve, it’s as good as throwing money out the window. Local SEO thrives on relevance and authenticity, so start creating content that speaks directly to your audience. Be the business that knows its customers, not just another faceless entity in the digital ether.

    It’s time to wake up, people! Local SEO is the lifeblood of businesses that want to thrive in today’s competitive landscape. Stop making excuses for why you can’t implement these strategies. It’s not about being tech-savvy; it’s about being smart, strategic, and willing to adapt. The longer you wait, the more customers you lose to those who understand the importance of local SEO.

    If you’re still clueless, it’s time to educate yourself because ignoring local SEO is a direct ticket to failure. Don’t let your competitors leave you in their dust. Step up, get informed, and start making the changes that will propel your business forward. Your community is waiting for you—don’t keep them waiting any longer!

    #LocalSEO #DigitalMarketing #SmallBusiness #OrganicPositioning #SEO
    It's infuriating to see how many businesses are still in the dark about the true power of local SEO! Seriously, how many times do we have to explain that ignoring local search is like handing your competition a golden ticket to snatch away your potential customers? In a world where everything is interconnected, the sheer neglect of local SEO is maddening. Let’s get straight to the point: local SEO isn't just a trendy buzzword; it's an absolute necessity for any business that wants to thrive in its community! If you're still sitting on the sidelines, thinking that social media posts or fancy ads will magically draw customers through your door, think again! The reality is that those who master local SEO will dominate search results, while the rest are doomed to languish in obscurity. The absurdity of this situation is mind-boggling. Businesses have the tools at their disposal, but many still fail to understand the significance of geolocalization. It’s not rocket science! Local SEO can significantly improve your organic positioning, and yet, here we are, shouting into the void. You want visibility? You want to attract local customers? Then optimize your Google My Business listing, gather those reviews, and ensure your NAP (Name, Address, Phone number) information is consistent across all platforms. It’s not that complicated, yet so many are just too lazy to put in the work! And let’s talk about the content. Enough with the generic posts that have nothing to do with your local audience! If your content doesn’t resonate with the community you serve, it’s as good as throwing money out the window. Local SEO thrives on relevance and authenticity, so start creating content that speaks directly to your audience. Be the business that knows its customers, not just another faceless entity in the digital ether. It’s time to wake up, people! Local SEO is the lifeblood of businesses that want to thrive in today’s competitive landscape. Stop making excuses for why you can’t implement these strategies. It’s not about being tech-savvy; it’s about being smart, strategic, and willing to adapt. The longer you wait, the more customers you lose to those who understand the importance of local SEO. If you’re still clueless, it’s time to educate yourself because ignoring local SEO is a direct ticket to failure. Don’t let your competitors leave you in their dust. Step up, get informed, and start making the changes that will propel your business forward. Your community is waiting for you—don’t keep them waiting any longer! #LocalSEO #DigitalMarketing #SmallBusiness #OrganicPositioning #SEO
    SEO local, ¿qué es y cómo ayuda a mejorar el posicionamiento orgánico?
    SEO local, ¿qué es y cómo ayuda a mejorar el posicionamiento orgánico? En un mundo cada vez más conectado, el SEO local se ha consolidado como una de las estrategias más efectivas para mejorar la visibilidad de los negocios locales que dependen de la
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  • Ankur Kothari Q&A: Customer Engagement Book Interview

    Reading Time: 9 minutes
    In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns.
    But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic.
    This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results.
    Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.

     
    Ankur Kothari Q&A Interview
    1. What types of customer engagement data are most valuable for making strategic business decisions?
    Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns.
    Second would be demographic information: age, location, income, and other relevant personal characteristics.
    Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews.
    Fourth would be the customer journey data.

    We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data.

    2. How do you distinguish between data that is actionable versus data that is just noise?
    First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance.
    Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in.

    You also want to make sure that there is consistency across sources.
    Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory.
    Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy.

    By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions.

    3. How can customer engagement data be used to identify and prioritize new business opportunities?
    First, it helps us to uncover unmet needs.

    By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points.

    Second would be identifying emerging needs.
    Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly.
    Third would be segmentation analysis.
    Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies.
    Last is to build competitive differentiation.

    Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions.

    4. Can you share an example of where data insights directly influenced a critical decision?
    I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings.
    We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms.
    That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs.

    That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial.

    5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time?
    When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences.
    We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments.
    Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content.

    With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns.

    6. How are you doing the 1:1 personalization?
    We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer.
    So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer.
    That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience.

    We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers.

    7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service?
    Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved.
    The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments.

    Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention.

    So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization.

    8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights?
    I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights.

    Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement.

    Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant.
    As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively.
    So there’s a lack of understanding of marketing and sales as domains.
    It’s a huge effort and can take a lot of investment.

    Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing.

    9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data?
    If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge.
    Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side.

    Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important.

    10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before?
    First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do.
    And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations.
    The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it.

    Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one.

    11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations?
    We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI.
    We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals.

    We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization.

    12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data?
    I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points.
    Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us.
    We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels.
    Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms.

    Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps.

    13. How do you ensure data quality and consistency across multiple channels to make these informed decisions?
    We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies.
    While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing.
    We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats.

    On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically.

    14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years?
    The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices.
    Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities.
    We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases.
    As the world is collecting more data, privacy concerns and regulations come into play.
    I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies.
    And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture.

    So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.

     
    This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die.
    Download the PDF or request a physical copy of the book here.
    The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.
    #ankur #kothari #qampampa #customer #engagement
    Ankur Kothari Q&A: Customer Engagement Book Interview
    Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question, we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage. #ankur #kothari #qampampa #customer #engagement
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    Ankur Kothari Q&A: Customer Engagement Book Interview
    Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.
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  • How to Create a Successful Leadership Development Program

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    How to Create a Successful Leadership Development Program

    At Harvard Business Impact, we partner with organizations to craft tailored learning experiences for leaders across all levels. Though each collaboration is unique, there is a proven process for designing and developing impactful learning initiatives.

    Leverage our checklist to help your organization develop a leadership development program that delivers results.

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    Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units

    Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for…

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    Succeeding in the Digital Age: Why AI-First Leadership Is Essential

    While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and…

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    4 Keys to AI-First Leadership: The New Imperative for Digital Transformation

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    The post How to Create a Successful Leadership Development Program appeared first on Harvard Business Impact.
    #how #create #successful #leadership #development
    How to Create a Successful Leadership Development Program
    Insights How to Create a Successful Leadership Development Program At Harvard Business Impact, we partner with organizations to craft tailored learning experiences for leaders across all levels. Though each collaboration is unique, there is a proven process for designing and developing impactful learning initiatives. Leverage our checklist to help your organization develop a leadership development program that delivers results. View the infographic Leadership DevelopmentStrategic Alignment Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… : Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… : Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… : 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… : Leadership Fitness Behavioral Assessment Job Aid The post How to Create a Successful Leadership Development Program appeared first on Harvard Business Impact. #how #create #successful #leadership #development
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    How to Create a Successful Leadership Development Program
    Insights How to Create a Successful Leadership Development Program At Harvard Business Impact, we partner with organizations to craft tailored learning experiences for leaders across all levels. Though each collaboration is unique, there is a proven process for designing and developing impactful learning initiatives. Leverage our checklist to help your organization develop a leadership development program that delivers results. View the infographic Leadership DevelopmentStrategic Alignment Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post How to Create a Successful Leadership Development Program appeared first on Harvard Business Impact.
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  • NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs

    Generative AI has reshaped how people create, imagine and interact with digital content.
    As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well.
    By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4.
    NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance.
    In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers.
    RTX-Accelerated AI
    NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs.
    Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution.
    To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one.
    SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs.
    FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup.
    Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch.
    The optimized models are now available on Stability AI’s Hugging Face page.
    NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July.
    TensorRT for RTX SDK Released
    Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers.
    Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time.
    With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature.
    The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview.
    For more details, read this NVIDIA technical blog and this Microsoft Build recap.
    Join NVIDIA at GTC Paris
    At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay.
    GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event.
    Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. 
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    #nvidia #tensorrt #boosts #stable #diffusion
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information. #nvidia #tensorrt #boosts #stable #diffusion
    BLOGS.NVIDIA.COM
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion (SD) 3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kit (SDK) double performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time (JIT), on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8 (right) generates images in half the time with similar quality as FP16 (left). Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.
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  • Mirela Cialai Q&A: Customer Engagement Book Interview

    Reading Time: 9 minutes
    In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential.
    That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success.
    In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers.
    You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI.
    Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.

     
    Mirela Cialai Q&A Interview
    1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience?

    Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives.

    This could be revenue growth, customer retention, market expansion, or operational efficiency.
    We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition.
    We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals.
    In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance.
    This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth.
    Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings.
    Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences.
    To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale.

    By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals.

    2. What steps did you take to ensure data accuracy?
    The data team was very diligent in ensuring that our data warehouse had accurate data.
    So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc.

    That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data.

    3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy?
    Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability.
    I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%.
    This data helps make a compelling case to stakeholders about the importance of prioritizing retention.
    Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth.
    This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives.

    By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy.

    4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement?
    Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach.
    The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives.
    I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse.
    Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows.
    Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities.

    Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape.

    5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for?
    I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels.
    Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns.
    Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns.
    Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability.

    If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs.

    6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap?
    Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes.
    Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact.
    Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert.

    By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success.

    7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives?
    To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success.
    Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value.
    Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities.
    Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth.
    By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs.

    In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability.

    In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first.
    8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you?
    Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability.
    We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success.
    To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams.

    To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together.

    9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like?
    A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine.
    In one word: PAPER. Here’s how it breaks down.

    Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals.
    Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps.
    Prioritize: initiatives based on impact, feasibility, and ROI potential.
    Execute: by implementing the roadmap in manageable phases.
    Refine: by continuously improving CRM performance and refining the roadmap.

    So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy.

    10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively?
    The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences.

    The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth.

    Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies.
    The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes.
    Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution.
    A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions.
    Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others.
    While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends.

    By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success.

    11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind?
    I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives.
    Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives.

    Another important lesson: The roadmap is only as effective as the data and systems it’s built upon.

    I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on.
    A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers.

    So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.

     

     
    This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die.
    Download the PDF or request a physical copy of the book here.
    The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage.
    #mirela #cialai #qampampa #customer #engagement
    Mirela Cialai Q&A: Customer Engagement Book Interview
    Reading Time: 9 minutes In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential. That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success. In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers. You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI. Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.   Mirela Cialai Q&A Interview 1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience? Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives. This could be revenue growth, customer retention, market expansion, or operational efficiency. We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition. We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals. In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance. This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth. Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings. Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences. To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale. By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals. 2. What steps did you take to ensure data accuracy? The data team was very diligent in ensuring that our data warehouse had accurate data. So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc. That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data. 3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy? Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability. I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%. This data helps make a compelling case to stakeholders about the importance of prioritizing retention. Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth. This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives. By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy. 4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement? Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach. The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives. I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse. Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows. Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities. Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape. 5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for? I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels. Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns. Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns. Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability. If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs. 6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap? Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes. Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact. Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert. By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success. 7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives? To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success. Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value. Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities. Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth. By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs. In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability. In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first. 8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you? Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability. We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success. To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams. To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together. 9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like? A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine. In one word: PAPER. Here’s how it breaks down. Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals. Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps. Prioritize: initiatives based on impact, feasibility, and ROI potential. Execute: by implementing the roadmap in manageable phases. Refine: by continuously improving CRM performance and refining the roadmap. So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy. 10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively? The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences. The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth. Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies. The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes. Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution. A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions. Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others. While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends. By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success. 11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind? I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives. Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives. Another important lesson: The roadmap is only as effective as the data and systems it’s built upon. I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on. A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers. So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.     This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage. #mirela #cialai #qampampa #customer #engagement
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    Mirela Cialai Q&A: Customer Engagement Book Interview
    Reading Time: 9 minutes In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential. That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success. In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers. You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI. Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.   Mirela Cialai Q&A Interview 1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience? Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives. This could be revenue growth, customer retention, market expansion, or operational efficiency. We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition. We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals. In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance. This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth. Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings. Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences. To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale. By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals. 2. What steps did you take to ensure data accuracy? The data team was very diligent in ensuring that our data warehouse had accurate data. So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc. That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data. 3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy? Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability. I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%. This data helps make a compelling case to stakeholders about the importance of prioritizing retention. Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth. This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives. By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy. 4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement? Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach. The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives. I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse. Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows. Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities. Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape. 5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for? I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels. Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns. Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns. Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability. If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs. 6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap? Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes. Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact. Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert. By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success. 7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives? To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success. Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value. Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities. Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth. By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs. In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability. In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first. 8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you? Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability. We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success. To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams. To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together. 9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like? A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine. In one word: PAPER. Here’s how it breaks down. Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals. Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps. Prioritize: initiatives based on impact, feasibility, and ROI potential. Execute: by implementing the roadmap in manageable phases. Refine: by continuously improving CRM performance and refining the roadmap. So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy. 10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively? The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences. The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth. Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies. The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes. Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution. A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions. Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others. While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends. By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success. 11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind? I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives. Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives. Another important lesson: The roadmap is only as effective as the data and systems it’s built upon. I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on. A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers. So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.     This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage.
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  • Reclaiming Control: Digital Sovereignty in 2025

    Sovereignty has mattered since the invention of the nation state—defined by borders, laws, and taxes that apply within and without. While many have tried to define it, the core idea remains: nations or jurisdictions seek to stay in control, usually to the benefit of those within their borders.
    Digital sovereignty is a relatively new concept, also difficult to define but straightforward to understand. Data and applications don’t understand borders unless they are specified in policy terms, as coded into the infrastructure.
    The World Wide Web had no such restrictions at its inception. Communitarian groups such as the Electronic Frontier Foundation, service providers and hyperscalers, non-profits and businesses all embraced a model that suggested data would look after itself.
    But data won’t look after itself, for several reasons. First, data is massively out of control. We generate more of it all the time, and for at least two or three decades, most organizations haven’t fully understood their data assets. This creates inefficiency and risk—not least, widespread vulnerability to cyberattack.
    Risk is probability times impact—and right now, the probabilities have shot up. Invasions, tariffs, political tensions, and more have brought new urgency. This time last year, the idea of switching off another country’s IT systems was not on the radar. Now we’re seeing it happen—including the U.S. government blocking access to services overseas.
    Digital sovereignty isn’t just a European concern, though it is often framed as such. In South America for example, I am told that sovereignty is leading conversations with hyperscalers; in African countries, it is being stipulated in supplier agreements. Many jurisdictions are watching, assessing, and reviewing their stance on digital sovereignty.
    As the adage goes: a crisis is a problem with no time left to solve it. Digital sovereignty was a problem in waiting—but now it’s urgent. It’s gone from being an abstract ‘right to sovereignty’ to becoming a clear and present issue, in government thinking, corporate risk and how we architect and operate our computer systems.
    What does the digital sovereignty landscape look like today?
    Much has changed since this time last year. Unknowns remain, but much of what was unclear this time last year is now starting to solidify. Terminology is clearer – for example talking about classification and localisation rather than generic concepts.
    We’re seeing a shift from theory to practice. Governments and organizations are putting policies in place that simply didn’t exist before. For example, some countries are seeing “in-country” as a primary goal, whereas othersare adopting a risk-based approach based on trusted locales.
    We’re also seeing a shift in risk priorities. From a risk standpoint, the classic triad of confidentiality, integrity, and availability are at the heart of the digital sovereignty conversation. Historically, the focus has been much more on confidentiality, driven by concerns about the US Cloud Act: essentially, can foreign governments see my data?
    This year however, availability is rising in prominence, due to geopolitics and very real concerns about data accessibility in third countries. Integrity is being talked about less from a sovereignty perspective, but is no less important as a cybercrime target—ransomware and fraud being two clear and present risks.
    Thinking more broadly, digital sovereignty is not just about data, or even intellectual property, but also the brain drain. Countries don’t want all their brightest young technologists leaving university only to end up in California or some other, more attractive country. They want to keep talent at home and innovate locally, to the benefit of their own GDP.
    How Are Cloud Providers Responding?
    Hyperscalers are playing catch-up, still looking for ways to satisfy the letter of the law whilst ignoringits spirit. It’s not enough for Microsoft or AWS to say they will do everything they can to protect a jurisdiction’s data, if they are already legally obliged to do the opposite. Legislation, in this case US legislation, calls the shots—and we all know just how fragile this is right now.
    We see hyperscaler progress where they offer technology to be locally managed by a third party, rather than themselves. For example, Google’s partnership with Thales, or Microsoft with Orange, both in France. However, these are point solutions, not part of a general standard. Meanwhile, AWS’ recent announcement about creating a local entity doesn’t solve for the problem of US over-reach, which remains a core issue.
    Non-hyperscaler providers and software vendors have an increasingly significant play: Oracle and HPE offer solutions that can be deployed and managed locally for example; Broadcom/VMware and Red Hat provide technologies that locally situated, private cloud providers can host. Digital sovereignty is thus a catalyst for a redistribution of “cloud spend” across a broader pool of players.
    What Can Enterprise Organizations Do About It?
    First, see digital sovereignty as a core element of data and application strategy. For a nation, sovereignty means having solid borders, control over IP, GDP, and so on. That’s the goal for corporations as well—control, self-determination, and resilience.
    If sovereignty isn’t seen as an element of strategy, it gets pushed down into the implementation layer, leading to inefficient architectures and duplicated effort. Far better to decide up front what data, applications and processes need to be treated as sovereign, and defining an architecture to support that.
    This sets the scene for making informed provisioning decisions. Your organization may have made some big bets on key vendors or hyperscalers, but multi-platform thinking increasingly dominates: multiple public and private cloud providers, with integrated operations and management. Sovereign cloud becomes one element of a well-structured multi-platform architecture.
    It is not cost-neutral to deliver on sovereignty, but the overall business value should be tangible. A sovereignty initiative should bring clear advantages, not just for itself, but through the benefits that come with better control, visibility, and efficiency.
    Knowing where your data is, understanding which data matters, managing it efficiently so you’re not duplicating or fragmenting it across systems—these are valuable outcomes. In addition, ignoring these questions can lead to non-compliance or be outright illegal. Even if we don’t use terms like ‘sovereignty’, organizations need a handle on their information estate.
    Organizations shouldn’t be thinking everything cloud-based needs to be sovereign, but should be building strategies and policies based on data classification, prioritization and risk. Build that picture and you can solve for the highest-priority items first—the data with the strongest classification and greatest risk. That process alone takes care of 80–90% of the problem space, avoiding making sovereignty another problem whilst solving nothing.
    Where to start? Look after your own organization first
    Sovereignty and systems thinking go hand in hand: it’s all about scope. In enterprise architecture or business design, the biggest mistake is boiling the ocean—trying to solve everything at once.
    Instead, focus on your own sovereignty. Worry about your own organization, your own jurisdiction. Know where your own borders are. Understand who your customers are, and what their requirements are. For example, if you’re a manufacturer selling into specific countries—what do those countries require? Solve for that, not for everything else. Don’t try to plan for every possible future scenario.
    Focus on what you have, what you’re responsible for, and what you need to address right now. Classify and prioritise your data assets based on real-world risk. Do that, and you’re already more than halfway toward solving digital sovereignty—with all the efficiency, control, and compliance benefits that come with it.
    Digital sovereignty isn’t just regulatory, but strategic. Organizations that act now can reduce risk, improve operational clarity, and prepare for a future based on trust, compliance, and resilience.
    The post Reclaiming Control: Digital Sovereignty in 2025 appeared first on Gigaom.
    #reclaiming #control #digital #sovereignty
    Reclaiming Control: Digital Sovereignty in 2025
    Sovereignty has mattered since the invention of the nation state—defined by borders, laws, and taxes that apply within and without. While many have tried to define it, the core idea remains: nations or jurisdictions seek to stay in control, usually to the benefit of those within their borders. Digital sovereignty is a relatively new concept, also difficult to define but straightforward to understand. Data and applications don’t understand borders unless they are specified in policy terms, as coded into the infrastructure. The World Wide Web had no such restrictions at its inception. Communitarian groups such as the Electronic Frontier Foundation, service providers and hyperscalers, non-profits and businesses all embraced a model that suggested data would look after itself. But data won’t look after itself, for several reasons. First, data is massively out of control. We generate more of it all the time, and for at least two or three decades, most organizations haven’t fully understood their data assets. This creates inefficiency and risk—not least, widespread vulnerability to cyberattack. Risk is probability times impact—and right now, the probabilities have shot up. Invasions, tariffs, political tensions, and more have brought new urgency. This time last year, the idea of switching off another country’s IT systems was not on the radar. Now we’re seeing it happen—including the U.S. government blocking access to services overseas. Digital sovereignty isn’t just a European concern, though it is often framed as such. In South America for example, I am told that sovereignty is leading conversations with hyperscalers; in African countries, it is being stipulated in supplier agreements. Many jurisdictions are watching, assessing, and reviewing their stance on digital sovereignty. As the adage goes: a crisis is a problem with no time left to solve it. Digital sovereignty was a problem in waiting—but now it’s urgent. It’s gone from being an abstract ‘right to sovereignty’ to becoming a clear and present issue, in government thinking, corporate risk and how we architect and operate our computer systems. What does the digital sovereignty landscape look like today? Much has changed since this time last year. Unknowns remain, but much of what was unclear this time last year is now starting to solidify. Terminology is clearer – for example talking about classification and localisation rather than generic concepts. We’re seeing a shift from theory to practice. Governments and organizations are putting policies in place that simply didn’t exist before. For example, some countries are seeing “in-country” as a primary goal, whereas othersare adopting a risk-based approach based on trusted locales. We’re also seeing a shift in risk priorities. From a risk standpoint, the classic triad of confidentiality, integrity, and availability are at the heart of the digital sovereignty conversation. Historically, the focus has been much more on confidentiality, driven by concerns about the US Cloud Act: essentially, can foreign governments see my data? This year however, availability is rising in prominence, due to geopolitics and very real concerns about data accessibility in third countries. Integrity is being talked about less from a sovereignty perspective, but is no less important as a cybercrime target—ransomware and fraud being two clear and present risks. Thinking more broadly, digital sovereignty is not just about data, or even intellectual property, but also the brain drain. Countries don’t want all their brightest young technologists leaving university only to end up in California or some other, more attractive country. They want to keep talent at home and innovate locally, to the benefit of their own GDP. How Are Cloud Providers Responding? Hyperscalers are playing catch-up, still looking for ways to satisfy the letter of the law whilst ignoringits spirit. It’s not enough for Microsoft or AWS to say they will do everything they can to protect a jurisdiction’s data, if they are already legally obliged to do the opposite. Legislation, in this case US legislation, calls the shots—and we all know just how fragile this is right now. We see hyperscaler progress where they offer technology to be locally managed by a third party, rather than themselves. For example, Google’s partnership with Thales, or Microsoft with Orange, both in France. However, these are point solutions, not part of a general standard. Meanwhile, AWS’ recent announcement about creating a local entity doesn’t solve for the problem of US over-reach, which remains a core issue. Non-hyperscaler providers and software vendors have an increasingly significant play: Oracle and HPE offer solutions that can be deployed and managed locally for example; Broadcom/VMware and Red Hat provide technologies that locally situated, private cloud providers can host. Digital sovereignty is thus a catalyst for a redistribution of “cloud spend” across a broader pool of players. What Can Enterprise Organizations Do About It? First, see digital sovereignty as a core element of data and application strategy. For a nation, sovereignty means having solid borders, control over IP, GDP, and so on. That’s the goal for corporations as well—control, self-determination, and resilience. If sovereignty isn’t seen as an element of strategy, it gets pushed down into the implementation layer, leading to inefficient architectures and duplicated effort. Far better to decide up front what data, applications and processes need to be treated as sovereign, and defining an architecture to support that. This sets the scene for making informed provisioning decisions. Your organization may have made some big bets on key vendors or hyperscalers, but multi-platform thinking increasingly dominates: multiple public and private cloud providers, with integrated operations and management. Sovereign cloud becomes one element of a well-structured multi-platform architecture. It is not cost-neutral to deliver on sovereignty, but the overall business value should be tangible. A sovereignty initiative should bring clear advantages, not just for itself, but through the benefits that come with better control, visibility, and efficiency. Knowing where your data is, understanding which data matters, managing it efficiently so you’re not duplicating or fragmenting it across systems—these are valuable outcomes. In addition, ignoring these questions can lead to non-compliance or be outright illegal. Even if we don’t use terms like ‘sovereignty’, organizations need a handle on their information estate. Organizations shouldn’t be thinking everything cloud-based needs to be sovereign, but should be building strategies and policies based on data classification, prioritization and risk. Build that picture and you can solve for the highest-priority items first—the data with the strongest classification and greatest risk. That process alone takes care of 80–90% of the problem space, avoiding making sovereignty another problem whilst solving nothing. Where to start? Look after your own organization first Sovereignty and systems thinking go hand in hand: it’s all about scope. In enterprise architecture or business design, the biggest mistake is boiling the ocean—trying to solve everything at once. Instead, focus on your own sovereignty. Worry about your own organization, your own jurisdiction. Know where your own borders are. Understand who your customers are, and what their requirements are. For example, if you’re a manufacturer selling into specific countries—what do those countries require? Solve for that, not for everything else. Don’t try to plan for every possible future scenario. Focus on what you have, what you’re responsible for, and what you need to address right now. Classify and prioritise your data assets based on real-world risk. Do that, and you’re already more than halfway toward solving digital sovereignty—with all the efficiency, control, and compliance benefits that come with it. Digital sovereignty isn’t just regulatory, but strategic. Organizations that act now can reduce risk, improve operational clarity, and prepare for a future based on trust, compliance, and resilience. The post Reclaiming Control: Digital Sovereignty in 2025 appeared first on Gigaom. #reclaiming #control #digital #sovereignty
    GIGAOM.COM
    Reclaiming Control: Digital Sovereignty in 2025
    Sovereignty has mattered since the invention of the nation state—defined by borders, laws, and taxes that apply within and without. While many have tried to define it, the core idea remains: nations or jurisdictions seek to stay in control, usually to the benefit of those within their borders. Digital sovereignty is a relatively new concept, also difficult to define but straightforward to understand. Data and applications don’t understand borders unless they are specified in policy terms, as coded into the infrastructure. The World Wide Web had no such restrictions at its inception. Communitarian groups such as the Electronic Frontier Foundation, service providers and hyperscalers, non-profits and businesses all embraced a model that suggested data would look after itself. But data won’t look after itself, for several reasons. First, data is massively out of control. We generate more of it all the time, and for at least two or three decades (according to historical surveys I’ve run), most organizations haven’t fully understood their data assets. This creates inefficiency and risk—not least, widespread vulnerability to cyberattack. Risk is probability times impact—and right now, the probabilities have shot up. Invasions, tariffs, political tensions, and more have brought new urgency. This time last year, the idea of switching off another country’s IT systems was not on the radar. Now we’re seeing it happen—including the U.S. government blocking access to services overseas. Digital sovereignty isn’t just a European concern, though it is often framed as such. In South America for example, I am told that sovereignty is leading conversations with hyperscalers; in African countries, it is being stipulated in supplier agreements. Many jurisdictions are watching, assessing, and reviewing their stance on digital sovereignty. As the adage goes: a crisis is a problem with no time left to solve it. Digital sovereignty was a problem in waiting—but now it’s urgent. It’s gone from being an abstract ‘right to sovereignty’ to becoming a clear and present issue, in government thinking, corporate risk and how we architect and operate our computer systems. What does the digital sovereignty landscape look like today? Much has changed since this time last year. Unknowns remain, but much of what was unclear this time last year is now starting to solidify. Terminology is clearer – for example talking about classification and localisation rather than generic concepts. We’re seeing a shift from theory to practice. Governments and organizations are putting policies in place that simply didn’t exist before. For example, some countries are seeing “in-country” as a primary goal, whereas others (the UK included) are adopting a risk-based approach based on trusted locales. We’re also seeing a shift in risk priorities. From a risk standpoint, the classic triad of confidentiality, integrity, and availability are at the heart of the digital sovereignty conversation. Historically, the focus has been much more on confidentiality, driven by concerns about the US Cloud Act: essentially, can foreign governments see my data? This year however, availability is rising in prominence, due to geopolitics and very real concerns about data accessibility in third countries. Integrity is being talked about less from a sovereignty perspective, but is no less important as a cybercrime target—ransomware and fraud being two clear and present risks. Thinking more broadly, digital sovereignty is not just about data, or even intellectual property, but also the brain drain. Countries don’t want all their brightest young technologists leaving university only to end up in California or some other, more attractive country. They want to keep talent at home and innovate locally, to the benefit of their own GDP. How Are Cloud Providers Responding? Hyperscalers are playing catch-up, still looking for ways to satisfy the letter of the law whilst ignoring (in the French sense) its spirit. It’s not enough for Microsoft or AWS to say they will do everything they can to protect a jurisdiction’s data, if they are already legally obliged to do the opposite. Legislation, in this case US legislation, calls the shots—and we all know just how fragile this is right now. We see hyperscaler progress where they offer technology to be locally managed by a third party, rather than themselves. For example, Google’s partnership with Thales, or Microsoft with Orange, both in France (Microsoft has similar in Germany). However, these are point solutions, not part of a general standard. Meanwhile, AWS’ recent announcement about creating a local entity doesn’t solve for the problem of US over-reach, which remains a core issue. Non-hyperscaler providers and software vendors have an increasingly significant play: Oracle and HPE offer solutions that can be deployed and managed locally for example; Broadcom/VMware and Red Hat provide technologies that locally situated, private cloud providers can host. Digital sovereignty is thus a catalyst for a redistribution of “cloud spend” across a broader pool of players. What Can Enterprise Organizations Do About It? First, see digital sovereignty as a core element of data and application strategy. For a nation, sovereignty means having solid borders, control over IP, GDP, and so on. That’s the goal for corporations as well—control, self-determination, and resilience. If sovereignty isn’t seen as an element of strategy, it gets pushed down into the implementation layer, leading to inefficient architectures and duplicated effort. Far better to decide up front what data, applications and processes need to be treated as sovereign, and defining an architecture to support that. This sets the scene for making informed provisioning decisions. Your organization may have made some big bets on key vendors or hyperscalers, but multi-platform thinking increasingly dominates: multiple public and private cloud providers, with integrated operations and management. Sovereign cloud becomes one element of a well-structured multi-platform architecture. It is not cost-neutral to deliver on sovereignty, but the overall business value should be tangible. A sovereignty initiative should bring clear advantages, not just for itself, but through the benefits that come with better control, visibility, and efficiency. Knowing where your data is, understanding which data matters, managing it efficiently so you’re not duplicating or fragmenting it across systems—these are valuable outcomes. In addition, ignoring these questions can lead to non-compliance or be outright illegal. Even if we don’t use terms like ‘sovereignty’, organizations need a handle on their information estate. Organizations shouldn’t be thinking everything cloud-based needs to be sovereign, but should be building strategies and policies based on data classification, prioritization and risk. Build that picture and you can solve for the highest-priority items first—the data with the strongest classification and greatest risk. That process alone takes care of 80–90% of the problem space, avoiding making sovereignty another problem whilst solving nothing. Where to start? Look after your own organization first Sovereignty and systems thinking go hand in hand: it’s all about scope. In enterprise architecture or business design, the biggest mistake is boiling the ocean—trying to solve everything at once. Instead, focus on your own sovereignty. Worry about your own organization, your own jurisdiction. Know where your own borders are. Understand who your customers are, and what their requirements are. For example, if you’re a manufacturer selling into specific countries—what do those countries require? Solve for that, not for everything else. Don’t try to plan for every possible future scenario. Focus on what you have, what you’re responsible for, and what you need to address right now. Classify and prioritise your data assets based on real-world risk. Do that, and you’re already more than halfway toward solving digital sovereignty—with all the efficiency, control, and compliance benefits that come with it. Digital sovereignty isn’t just regulatory, but strategic. Organizations that act now can reduce risk, improve operational clarity, and prepare for a future based on trust, compliance, and resilience. The post Reclaiming Control: Digital Sovereignty in 2025 appeared first on Gigaom.
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