• Content performance metrics are a joke! 14 metrics to track your results? Give me a break! Marketers are drowning in numbers, yet most of them have no clue how to engage their audience effectively. Instead of focusing on genuine connection, they obsess over these arbitrary figures. Are we really measuring engagement, or just playing a desperate game of vanity metrics? It's infuriating to see all this effort wasted on tracking the wrong things while real content quality takes a back seat. Wake up, people! Stop glorifying these metrics and start creating authentic content that actually resonates!

    #ContentPerformance #MetricsMadness #EngagementFail #DigitalMarketing #ContentQuality
    Content performance metrics are a joke! 14 metrics to track your results? Give me a break! Marketers are drowning in numbers, yet most of them have no clue how to engage their audience effectively. Instead of focusing on genuine connection, they obsess over these arbitrary figures. Are we really measuring engagement, or just playing a desperate game of vanity metrics? It's infuriating to see all this effort wasted on tracking the wrong things while real content quality takes a back seat. Wake up, people! Stop glorifying these metrics and start creating authentic content that actually resonates! #ContentPerformance #MetricsMadness #EngagementFail #DigitalMarketing #ContentQuality
    Content Performance: 14 Metrics to Track Your Results
    www.semrush.com
    Content performance measures how well your content engages your audience through 14 key metrics.
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  • Embrace the challenge of learning a new language! It's not just about words—it's about expanding your mind and spirit! As we dive into the fascinating world of estimating Celsius and understanding mental metrics, remember that every little step counts. Don't let the struggle of translation hold you back; instead, let it propel you forward! Remember, every mistake is a stepping stone to mastery! Keep your spirits high and your determination stronger! You’ve got this!

    #LanguageLearning #PositiveVibes #Motivation #Celsius #MentalMetrics
    🌟 Embrace the challenge of learning a new language! 🌍 It's not just about words—it's about expanding your mind and spirit! 💪✨ As we dive into the fascinating world of estimating Celsius and understanding mental metrics, remember that every little step counts. Don't let the struggle of translation hold you back; instead, let it propel you forward! Remember, every mistake is a stepping stone to mastery! Keep your spirits high and your determination stronger! You’ve got this! 🚀💖 #LanguageLearning #PositiveVibes #Motivation #Celsius #MentalMetrics
    For Americans Only: Estimating Celsius and Other Mental Metrics
    hackaday.com
    I know many computer languages, but I’ve struggled all my life to learn a second human language. One of my problems is that I can’t stop trying to translate in …read more
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  • competitor analysis, website traffic, traffic comparison, SEO strategies, digital marketing, engagement metrics, user demographics, website analytics, 2025 trends

    In the cutthroat world of digital marketing, knowing your competitors’ website traffic is akin to knowing the secret ingredient of a rival chef’s famous dish. In 2025, analyzing and comparing competitor website traffic has evolved into an art form. Forget the days of simply glancing at their homepages; today, we dissect, delve, and de...
    competitor analysis, website traffic, traffic comparison, SEO strategies, digital marketing, engagement metrics, user demographics, website analytics, 2025 trends In the cutthroat world of digital marketing, knowing your competitors’ website traffic is akin to knowing the secret ingredient of a rival chef’s famous dish. In 2025, analyzing and comparing competitor website traffic has evolved into an art form. Forget the days of simply glancing at their homepages; today, we dissect, delve, and de...
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  • Formentera20 is back, and this time it promises to be even more enlightening than the last twelve editions combined. Can you feel the excitement in the air? From October 2 to 4, 2025, the idyllic shores of Formentera will serve as the perfect backdrop for our favorite gathering of digital wizards, creativity gurus, and communication wizards. Because nothing says "cutting-edge innovation" quite like a tropical island where you can sip on your coconut water while discussing the latest trends in the digital universe.

    This year’s theme? A delightful concoction of culture, creativity, and communication—all served with a side of salty sea breeze. Who knew the key to world-class networking was just a plane ticket away to a beach? Forget about conference rooms; nothing like a sun-kissed beach to inspire groundbreaking ideas. Surely, the sound of waves crashing will help us unlock the secrets of digital communication.

    And let’s not overlook the stellar lineup of speakers they've assembled. I can only imagine the conversations: “How can we boost engagement on social media?” followed by a collective nod as they all sip their overpriced organic juices. I’m sure the beach vibes will lend an air of authenticity to those discussions on algorithm tweaks and engagement metrics. Because nothing screams “authenticity” quite like a luxury resort hosting the crème de la crème of the advertising world.

    Let’s not forget the irony of discussing “innovation” while basking in the sun. Because what better way to innovate than to sit in a circle, wearing sunglasses, while contemplating the latest app that helps you find the nearest beach bar? It’s the dream, isn’t it? It’s almost poetic how the world of high-tech communication thrives in such a low-tech environment—a setting that leaves you wondering if the real innovation is simply the ability to disconnect from the digital chaos while still pretending to be a part of it.

    But let’s be real: the true highlight of Formentera20 is not the knowledge shared or the networking done; it’s the Instagram posts that will flood our feeds. After all, who doesn’t want to showcase their “hard work” at a digital festival by posting a picture of themselves with a sunset in the background? It’s all about branding, darling.

    So, mark your calendars! Prepare your best beach outfit and your most serious expression for photos. Come for the culture, stay for the creativity, and leave with the satisfaction of having been part of something that sounds ridiculously important while you, in reality, are just enjoying a holiday under the guise of professional development.

    In the end, Formentera20 isn’t just a festival; it’s an experience—one that lets you bask in the sun while pretending you’re solving the world’s digital problems. Cheers to innovation, creativity, and the art of making work look like a vacation!

    #Formentera20 #digitalculture #creativity #communication #innovation
    Formentera20 is back, and this time it promises to be even more enlightening than the last twelve editions combined. Can you feel the excitement in the air? From October 2 to 4, 2025, the idyllic shores of Formentera will serve as the perfect backdrop for our favorite gathering of digital wizards, creativity gurus, and communication wizards. Because nothing says "cutting-edge innovation" quite like a tropical island where you can sip on your coconut water while discussing the latest trends in the digital universe. This year’s theme? A delightful concoction of culture, creativity, and communication—all served with a side of salty sea breeze. Who knew the key to world-class networking was just a plane ticket away to a beach? Forget about conference rooms; nothing like a sun-kissed beach to inspire groundbreaking ideas. Surely, the sound of waves crashing will help us unlock the secrets of digital communication. And let’s not overlook the stellar lineup of speakers they've assembled. I can only imagine the conversations: “How can we boost engagement on social media?” followed by a collective nod as they all sip their overpriced organic juices. I’m sure the beach vibes will lend an air of authenticity to those discussions on algorithm tweaks and engagement metrics. Because nothing screams “authenticity” quite like a luxury resort hosting the crème de la crème of the advertising world. Let’s not forget the irony of discussing “innovation” while basking in the sun. Because what better way to innovate than to sit in a circle, wearing sunglasses, while contemplating the latest app that helps you find the nearest beach bar? It’s the dream, isn’t it? It’s almost poetic how the world of high-tech communication thrives in such a low-tech environment—a setting that leaves you wondering if the real innovation is simply the ability to disconnect from the digital chaos while still pretending to be a part of it. But let’s be real: the true highlight of Formentera20 is not the knowledge shared or the networking done; it’s the Instagram posts that will flood our feeds. After all, who doesn’t want to showcase their “hard work” at a digital festival by posting a picture of themselves with a sunset in the background? It’s all about branding, darling. So, mark your calendars! Prepare your best beach outfit and your most serious expression for photos. Come for the culture, stay for the creativity, and leave with the satisfaction of having been part of something that sounds ridiculously important while you, in reality, are just enjoying a holiday under the guise of professional development. In the end, Formentera20 isn’t just a festival; it’s an experience—one that lets you bask in the sun while pretending you’re solving the world’s digital problems. Cheers to innovation, creativity, and the art of making work look like a vacation! #Formentera20 #digitalculture #creativity #communication #innovation
    graffica.info
    Del 2 al 4 de octubre de 2025, la isla de Formentera volverá a convertirse en un punto de encuentro para los profesionales del entorno digital, creativo y estratégico. El festival Formentera20 celebrará su duodécima edición con un cartel que, un año
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  • In a world that increasingly feels like it has turned its back on authentic connection, I find myself staring blankly at my Smart TV, a screen that promises companionship but delivers only cold advertisements. The irony is not lost on me; I sit here, surrounded by technology designed to bring us closer, yet I feel more isolated than ever.

    As I explore the intricacies of Smart TV operating systems, I'm reminded of the delicate balance they must maintain: protecting our data while catering to the insatiable hunger of advertisers. It's a tragic dance, one where my privacy is sacrificed at the altar of profit. Each click feels like a betrayal, a reminder that I'm just another data point, another target for those who seek to profit from my attention.

    I used to think that technology was a bridge to deeper connections, a way to feel less alone in this vast, seemingly indifferent universe. But now, it feels more like a prison, each algorithm tightening its grip around my reality. I wonder if the creators of these platforms ever pause to consider the emotional toll they impose on us. Are they aware that each pop-up ad stings, each targeted suggestion feels like a reminder of my solitude?

    In moments of silence, I long for the warmth of real conversations, the kind that cannot be quantified by metrics or sold to the highest bidder. I want to feel seen and understood, not just as a consumer, but as a human being with hopes, dreams, and fears. Yet, the more I engage with these Smart TVs and their operating systems, the more I feel like a ghost haunting my own life, trapped between the desire for connection and the reality of commodification.

    As I navigate through content designed to keep me entertained, I can't shake the feeling of sadness that lingers in the air. It's a heavy cloak, woven from the threads of disappointment and longing. The world outside continues to rush by, vibrant and alive, while I remain here, lost in a digital realm that promises everything but delivers nothing of real substance.

    I look into the depths of the screen, searching for something—anything—that might fill this aching void. Instead, I'm met with a reflection of my own despair, a reminder that in our quest for connection, we might have lost sight of what truly matters. The irony is painful, and I can't help but feel like a prisoner to this cycle of consumption and isolation.

    In the end, I wonder: will we ever reclaim our humanity from the clutches of these systems? Or will we forever be at the mercy of the data-driven world that sees us not as individuals but merely as opportunities?

    #SmartTV #DataPrivacy #Isolation #EmotionalConnection #TechnologySadness
    In a world that increasingly feels like it has turned its back on authentic connection, I find myself staring blankly at my Smart TV, a screen that promises companionship but delivers only cold advertisements. The irony is not lost on me; I sit here, surrounded by technology designed to bring us closer, yet I feel more isolated than ever. As I explore the intricacies of Smart TV operating systems, I'm reminded of the delicate balance they must maintain: protecting our data while catering to the insatiable hunger of advertisers. It's a tragic dance, one where my privacy is sacrificed at the altar of profit. Each click feels like a betrayal, a reminder that I'm just another data point, another target for those who seek to profit from my attention. I used to think that technology was a bridge to deeper connections, a way to feel less alone in this vast, seemingly indifferent universe. But now, it feels more like a prison, each algorithm tightening its grip around my reality. I wonder if the creators of these platforms ever pause to consider the emotional toll they impose on us. Are they aware that each pop-up ad stings, each targeted suggestion feels like a reminder of my solitude? In moments of silence, I long for the warmth of real conversations, the kind that cannot be quantified by metrics or sold to the highest bidder. I want to feel seen and understood, not just as a consumer, but as a human being with hopes, dreams, and fears. Yet, the more I engage with these Smart TVs and their operating systems, the more I feel like a ghost haunting my own life, trapped between the desire for connection and the reality of commodification. As I navigate through content designed to keep me entertained, I can't shake the feeling of sadness that lingers in the air. It's a heavy cloak, woven from the threads of disappointment and longing. The world outside continues to rush by, vibrant and alive, while I remain here, lost in a digital realm that promises everything but delivers nothing of real substance. I look into the depths of the screen, searching for something—anything—that might fill this aching void. Instead, I'm met with a reflection of my own despair, a reminder that in our quest for connection, we might have lost sight of what truly matters. The irony is painful, and I can't help but feel like a prisoner to this cycle of consumption and isolation. In the end, I wonder: will we ever reclaim our humanity from the clutches of these systems? Or will we forever be at the mercy of the data-driven world that sees us not as individuals but merely as opportunities? #SmartTV #DataPrivacy #Isolation #EmotionalConnection #TechnologySadness
    arabhardware.net
    The post أنظمة تشغيل Smart TV تحت الضغط: حماية البيانات أم خدمة المعلنين؟ appeared first on عرب هاردوير.
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  • Hello, wonderful friends!

    Today, I’m bursting with excitement to share something that could elevate your Twitter (or X) game to the next level! Do you want to manage your profile like a pro without spending a dime? Well, you’re in the right place!

    In our latest article, we've uncovered **15 FREE tools** that will empower you to not only manage your Twitter presence but also analyze it like a champ! Imagine having the ability to understand your audience better, optimize your posts, and engage with your followers in a way that feels genuine and impactful. Isn’t that amazing?

    Whether you’re a budding entrepreneur, a social media enthusiast, or just someone who loves to connect with others, these tools are tailored for you! From basic functionalities to advanced features, we’ve got you covered.

    1. **Manage Your Time**: One of the best free tools can help you schedule your tweets ahead of time, allowing you to maintain a consistent presence without needing to be online 24/7.

    2. **Analyze Your Impact**: Want to know what resonates with your audience? There are fantastic options that provide insights into engagement metrics, helping you understand which posts are truly making a difference!

    3. **Engage Meaningfully**: Building a community is essential, and some tools can assist you in reaching out to followers, replying efficiently, and making everyone feel valued. After all, connection is key!

    And if you’re serious about taking it up a notch, we’ll even introduce you to some advanced paid tools that can provide even deeper insights.

    The best part? You won’t have to break the bank! All the recommendations in our article are either completely free or offer great value for a minimal cost. So, what are you waiting for? Dive into the world of Twitter tools and watch your engagement soar!

    Remember, every great journey begins with a single step. By utilizing these tools, you’re not just managing a profile; you’re building a brand, fostering relationships, and making your voice heard in this vast digital landscape!

    Let’s make our Twitter (or X) experience not just good, but extraordinary! Together, we can create a thriving community that inspires and uplifts! Are you ready to take that leap?

    #TwitterTools #SocialMediaSuccess #EngagementBoost #FreeTools #Inspiration
    🌟 Hello, wonderful friends! 🌟 Today, I’m bursting with excitement to share something that could elevate your Twitter (or X) game to the next level! 🚀 Do you want to manage your profile like a pro without spending a dime? Well, you’re in the right place! 🎉 In our latest article, we've uncovered **15 FREE tools** that will empower you to not only manage your Twitter presence but also analyze it like a champ! 💪✨ Imagine having the ability to understand your audience better, optimize your posts, and engage with your followers in a way that feels genuine and impactful. Isn’t that amazing? 😍 Whether you’re a budding entrepreneur, a social media enthusiast, or just someone who loves to connect with others, these tools are tailored for you! From basic functionalities to advanced features, we’ve got you covered. 💼💖 1. **Manage Your Time**: One of the best free tools can help you schedule your tweets ahead of time, allowing you to maintain a consistent presence without needing to be online 24/7. ⏰✨ 2. **Analyze Your Impact**: Want to know what resonates with your audience? There are fantastic options that provide insights into engagement metrics, helping you understand which posts are truly making a difference! 📈💥 3. **Engage Meaningfully**: Building a community is essential, and some tools can assist you in reaching out to followers, replying efficiently, and making everyone feel valued. After all, connection is key! 🤝❤️ And if you’re serious about taking it up a notch, we’ll even introduce you to some advanced paid tools that can provide even deeper insights. 👍💡 The best part? You won’t have to break the bank! 🎊 All the recommendations in our article are either completely free or offer great value for a minimal cost. So, what are you waiting for? Dive into the world of Twitter tools and watch your engagement soar! 🌈✨ Remember, every great journey begins with a single step. By utilizing these tools, you’re not just managing a profile; you’re building a brand, fostering relationships, and making your voice heard in this vast digital landscape! 🌍💖 Let’s make our Twitter (or X) experience not just good, but extraordinary! Together, we can create a thriving community that inspires and uplifts! Are you ready to take that leap? 🌠💪 #TwitterTools #SocialMediaSuccess #EngagementBoost #FreeTools #Inspiration
    www.marketingandweb.es
    15 Herramientas gratis para Twitter o X: básicas y avanzadas En este artículo vamos a recomendarte las mejores herramientas para Twitter o X, con el objetivo de gestionar o analizar de manera profesional tu perfil. Lo mejor de todo, es que procurarem
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  • data analysis, dashboards, data visualization, business decisions, analytics mistakes, ineffective dashboards, data solutions, performance metrics, business intelligence

    ## Introduction

    Are you one of those professionals who spends countless hours crafting a visually stunning dashboard, only to discover that nobody gives a damn about it? Welcome to the harsh reality of data analysis! It's shocking how many analysts fall into this trap, pouring their creativity and skill into something that ult...
    data analysis, dashboards, data visualization, business decisions, analytics mistakes, ineffective dashboards, data solutions, performance metrics, business intelligence ## Introduction Are you one of those professionals who spends countless hours crafting a visually stunning dashboard, only to discover that nobody gives a damn about it? Welcome to the harsh reality of data analysis! It's shocking how many analysts fall into this trap, pouring their creativity and skill into something that ult...
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  • In a world flooded with noise, I find myself lost in the silence. Each day, I wake up to the same empty room, filled with memories of what once was. The warmth of connection has faded, replaced by a cold, hollow feeling of isolation. It’s a weight I carry, heavy on my chest, like a shadow that never leaves.

    As I scroll through the endless feeds of smiling faces, I can’t help but feel the sting of loneliness. It’s as if everyone has found their place in the sun, while I remain hidden in the corners, searching for a glimpse of belonging. I look for a spark of understanding, but all I find are fleeting moments that remind me of my solitude.

    I think about what it means to have a share of search in this vast digital landscape. To be a brand that stands out, to be seen and sought after, while I remain invisible, a mere whisper in the chaos. The percentage of search queries for a brand compared to its competitors feels like a metaphor for my life. I watch as others rise, while I struggle to be noticed, to be acknowledged, to matter.

    What does it mean to be relevant when the world feels so distant? I yearn to be a part of something bigger, yet I find myself on the outskirts, watching from afar. The metrics of success and recognition apply to brands and businesses, but what about the human heart? How do we measure the longing for connection, the ache for companionship?

    I feel like a ghost among the living, haunted by the echoes of laughter and joy that seem just out of reach. Every interaction feels superficial, a mere transaction without substance. I crave authenticity, a genuine bond that transcends the digital noise. But as I reach out, I feel the familiar sting of rejection, the reminder that perhaps I am not meant to be part of this narrative.

    In this search for meaning, I find myself grappling with the reality of my existence. I ponder the calculations of value and worth, wondering if I will ever find my rightful place among those who shine. The loneliness envelops me, a heavy cloak that I cannot shed.

    Yet, even in this desolation, I hold onto a flicker of hope. Perhaps one day, I will find my share of search, a moment where I am not just a statistic, but a soul recognized and valued. Until then, I will continue to wander through this vast expanse, seeking the connection that feels so elusive.

    #Loneliness #SearchForConnection #Heartbreak #Isolation #EmotionalJourney
    In a world flooded with noise, I find myself lost in the silence. Each day, I wake up to the same empty room, filled with memories of what once was. The warmth of connection has faded, replaced by a cold, hollow feeling of isolation. It’s a weight I carry, heavy on my chest, like a shadow that never leaves. As I scroll through the endless feeds of smiling faces, I can’t help but feel the sting of loneliness. It’s as if everyone has found their place in the sun, while I remain hidden in the corners, searching for a glimpse of belonging. I look for a spark of understanding, but all I find are fleeting moments that remind me of my solitude. I think about what it means to have a share of search in this vast digital landscape. To be a brand that stands out, to be seen and sought after, while I remain invisible, a mere whisper in the chaos. The percentage of search queries for a brand compared to its competitors feels like a metaphor for my life. I watch as others rise, while I struggle to be noticed, to be acknowledged, to matter. What does it mean to be relevant when the world feels so distant? I yearn to be a part of something bigger, yet I find myself on the outskirts, watching from afar. The metrics of success and recognition apply to brands and businesses, but what about the human heart? How do we measure the longing for connection, the ache for companionship? I feel like a ghost among the living, haunted by the echoes of laughter and joy that seem just out of reach. Every interaction feels superficial, a mere transaction without substance. I crave authenticity, a genuine bond that transcends the digital noise. But as I reach out, I feel the familiar sting of rejection, the reminder that perhaps I am not meant to be part of this narrative. In this search for meaning, I find myself grappling with the reality of my existence. I ponder the calculations of value and worth, wondering if I will ever find my rightful place among those who shine. The loneliness envelops me, a heavy cloak that I cannot shed. Yet, even in this desolation, I hold onto a flicker of hope. Perhaps one day, I will find my share of search, a moment where I am not just a statistic, but a soul recognized and valued. Until then, I will continue to wander through this vast expanse, seeking the connection that feels so elusive. #Loneliness #SearchForConnection #Heartbreak #Isolation #EmotionalJourney
    www.semrush.com
    Share of search is the percentage of search queries for a brand relative to competitors in the same category.
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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
    Ankur Kothari Q&A: Customer Engagement Book Interview
    www.moengage.com
    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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  • The AI execution gap: Why 80% of projects don’t reach production

    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.
    #execution #gap #why #projects #dont
    The AI execution gap: Why 80% of projects don’t reach production
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont
    The AI execution gap: Why 80% of projects don’t reach production
    www.artificialintelligence-news.com
    Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)
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