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

A coiled giant anaconda. They are the largest snake species in Brazil and play a major role in legends including the ‘Boiuna’ and the ‘Cobra Grande.’ CREDIT: Beatriz Cosendey. Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. South America’s lush Amazon region is a biodiversity hotspot, which means that every living thing must find a way to co-exist. Even some of the most feared snakes on the planet–anacondas. In a paper published June 16 in the journal Frontiers in Amphibian and Reptile Science, conservation biologists Beatriz Cosendey and Juarez Carlos Brito Pezzuti from the Federal University of Pará’s Center for Amazonian Studies in Brazil, analyze the key points behind the interactions between humans and the local anaconda populations. Ahead of the paper’s publication, the team at Frontiers conducted this wide-ranging Q&A with Conesday. It has not been altered. Frontiers: What inspired you to become a researcher? Beatriz Cosendey: As a child, I was fascinated by reports and documentaries about field research and often wondered what it took to be there and what kind of knowledge was being produced. Later, as an ecologist, I felt the need for approaches that better connected scientific research with real-world contexts. I became especially interested in perspectives that viewed humans not as separate from nature, but as part of ecological systems. This led me to explore integrative methods that incorporate local and traditional knowledge, aiming to make research more relevant and accessible to the communities involved. F: Can you tell us about the research you’re currently working on? BC: My research focuses on ethnobiology, an interdisciplinary field intersecting ecology, conservation, and traditional knowledge. We investigate not only the biodiversity of an area but also the relationship local communities have with surrounding species, providing a better understanding of local dynamics and areas needing special attention for conservation. After all, no one knows a place better than those who have lived there for generations. This deep familiarity allows for early detection of changes or environmental shifts. Additionally, developing a collaborative project with residents generates greater engagement, as they recognize themselves as active contributors; and collective participation is essential for effective conservation. Local boating the Amazon River. CREDIT: Beatriz Cosendey. F: Could you tell us about one of the legends surrounding anacondas? BC: One of the greatest myths is about the Great Snake—a huge snake that is said to inhabit the Amazon River and sleep beneath the town. According to the dwellers, the Great Snake is an anaconda that has grown too large; its movements can shake the river’s waters, and its eyes look like fire in the darkness of night. People say anacondas can grow so big that they can swallow large animals—including humans or cattle—without difficulty. F: What could be the reasons why the traditional role of anacondas as a spiritual and mythological entity has changed? Do you think the fact that fewer anacondas have been seen in recent years contributes to their diminished importance as an mythological entity? BC: Not exactly. I believe the two are related, but not in a direct way. The mythology still exists, but among Aritapera dwellers, there’s a more practical, everyday concern—mainly the fear of losing their chickens. As a result, anacondas have come to be seen as stealthy thieves. These traits are mostly associated with smaller individuals, while the larger ones—which may still carry the symbolic weight of the ‘Great Snake’—tend to retreat to more sheltered areas; because of the presence of houses, motorized boats, and general noise, they are now seen much less frequently. A giant anaconda is being measured. Credit: Pedro Calazans. F: Can you share some of the quotes you’ve collected in interviews that show the attitude of community members towards anacondas? How do chickens come into play? BC: When talking about anacondas, one thing always comes up: chickens. “Chicken is herfavorite dish. If one clucks, she comes,” said one dweller. This kind of remark helps explain why the conflict is often framed in economic terms. During the interviews and conversations with local dwellers, many emphasized the financial impact of losing their animals: “The biggest loss is that they keep taking chicks and chickens…” or “You raise the chicken—you can’t just let it be eaten for free, right?” For them, it’s a loss of investment, especially since corn, which is used as chicken feed, is expensive. As one person put it: “We spend time feeding and raising the birds, and then the snake comes and takes them.” One dweller shared that, in an attempt to prevent another loss, he killed the anaconda and removed the last chicken it had swallowed from its belly—”it was still fresh,” he said—and used it for his meal, cooking the chicken for lunch so it wouldn’t go to waste. One of the Amazonas communities where the researchers conducted their research. CREDIT: Beatriz Cosendey. Some interviewees reported that they had to rebuild their chicken coops and pigsties because too many anacondas were getting in. Participants would point out where the anaconda had entered and explained that they came in through gaps or cracks but couldn’t get out afterwards because they ‘tufavam’ — a local term referring to the snake’s body swelling after ingesting prey. We saw chicken coops made with mesh, with nylon, some that worked and some that didn’t. Guided by the locals’ insights, we concluded that the best solution to compensate for the gaps between the wooden slats is to line the coop with a fine nylon mesh, and on the outside, a layer of wire mesh, which protects the inner mesh and prevents the entry of larger animals. F: Are there any common misconceptions about this area of research? How would you address them? BC: Yes, very much. Although ethnobiology is an old science, it’s still underexplored and often misunderstood. In some fields, there are ongoing debates about the robustness and scientific validity of the field and related areas. This is largely because the findings don’t always rely only on hard statistical data. However, like any other scientific field, it follows standardized methodologies, and no result is accepted without proper grounding. What happens is that ethnobiology leans more toward the human sciences, placing human beings and traditional knowledge as key variables within its framework. To address these misconceptions, I believe it’s important to emphasize that ethnobiology produces solid and relevant knowledge—especially in the context of conservation and sustainable development. It offers insights that purely biological approaches might overlook and helps build bridges between science and society. The study focused on the várzea regions of the Lower Amazon River. CREDIT: Beatriz Cosendey. F: What are some of the areas of research you’d like to see tackled in the years ahead? BC: I’d like to see more conservation projects that include local communities as active participants rather than as passive observers. Incorporating their voices, perspectives, and needs not only makes initiatives more effective, but also more just. There is also great potential in recognizing and valuing traditional knowledge. Beyond its cultural significance, certain practices—such as the use of natural compounds—could become practical assets for other vulnerable regions. Once properly documented and understood, many of these approaches offer adaptable forms of environmental management and could help inform broader conservation strategies elsewhere. F: How has open science benefited the reach and impact of your research? BC: Open science is crucial for making research more accessible. By eliminating access barriers, it facilitates a broader exchange of knowledge—important especially for interdisciplinary research like mine which draws on multiple knowledge systems and gains value when shared widely. For scientific work, it ensures that knowledge reaches a wider audience, including practitioners and policymakers. This openness fosters dialogue across different sectors, making research more inclusive and encouraging greater collaboration among diverse groups. The Q&A can also be read here. #qampampa #how #anacondas #chickens #locals

Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, 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 Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage. #alec #haase #qampampa #customer #engagement

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

🎬 It's Monday! Welcome back to another episode of my weekly CGHOW community reply series, where I go through your comments from Discord and YouTube and answer your questions about Unreal Engine, Niagara, and real-time VFX! Whether you're a beginner or an advanced VFX artist, I'm here to help you level up your skills, fix common issues, and give you tips based on your feedback and questions. 👇 Got a question for next week? Drop it in the comments or join the CGHOW Discord! 🔗 Join the CGHOW Community: 👉 Discord: 📌 Don't forget to like, comment, and subscribe to support the channel! 📅 New VFX videos uploaded every week! #UnrealEngine #RealtimeVFX #cghow #qampampa #your #questions #insights

In February, as part of my role as a senior software development consultant for Unity Accelerate Solutions, I led a technical webinar about the Addressables Asset System. During the live session, I demonstrated various profiling tools that you can use to optimize a project’s runtime memory and build size. The webinar ended with a Q&A, and our team received more questions than we had time to answer.The following is an extension of that closing Q&A, so we can answer more of your questions.Q: Is the Addressables system needed for light games – like casual, arcade, or puzzle games – if I don’t have memory issues? A: Maybe not, but it’s good to keep in mind that the Addressables system doesn’t only improve memory performance. Having the ability to choose when you load content can improve loading times. Building content in Addressables enables you to have iterative builds that don’t take as long. For example, if you make a small script change, you may not have to rebuild all of your bundles.Q: Are loaded assets released when the scene switches? A: Potentially. Loaded assets from Addressables that are ready to be released because they have a ref count of zero might be unloaded from memory during a scene transition. When transitioning from scenes non-additively, call Resources.UnloadUnusedAssets. This is expensive on the CPU, but allows you to partially unload AssetBundles.Q: Do object pooling and Addressables work well together? A: Yes. You can load your object once from Addressables and then instantiate multiple copies of it to create your pool. When you are done with the pool, destroy all the objects and release the AsyncOperationHandle that was used to load the asset.Q: Are groups and bundles loaded into memory all at once? A: Addressables groups are an Editor-only concept. At runtime, you only deal with bundles. Bundles are loaded into memory only when they are needed and only the desired content is loaded.Example: You have one bundle with 10 characters in it. You ask Addressables to load three characters. The bundle’s metadata and the three characters will be loaded.Q: If I want to release an asset, do I need to keep the AsyncOperationHandle or the AssetReference? A: We recommend keeping the handle and using it, since you’re responsible for releasing content when you are done using it.As an example, members of our team will often go the handle route in order to avoid calling Instantiate/Release directly on the AssetReference.Q: What are the disadvantages of many small bundles? A: This documentation lists several disadvantages of too many bundles.Q: When an asset in a bundle is needed, what overhead do the other assets in the same bundle have? If it’s a remote bundle it must be downloaded, but is there really no memory overhead from unused assets in the bundle? A: Correct, a remote bundle will be fully downloaded before you can use it.Unloaded assets in a loaded asset bundle have minimal overhead at runtime. Whenever you load assets from a bundle, you need to load the bundle’s metadata. Part of this metadata includes a table of contents that lists all the assets in the bundle. More assets in a bundle equates to larger metadata.You can view this memory overhead by taking a capture with the Unity Memory Profiler. In the “All Of Memory” tab, there’s a list of all the “SerializedFile” objects in memory, one for each bundle. These objects are your bundles’ metadata.Learn more about this metadata in our documentation.Q: When working in an open-world setting, what bundling strategies can I use to unload individual assets without half unloading a bundle and relying on Resources.UnloadUnusedAssetsto clean it up, without the overhead of having every asset in its own bundle? A: The key thing to remember is that content should be bundled together if you expect to unload it at the same time. If your game world has “static” content, like trees and rocks for a certain biome that will not be moved by the player, that content should be bundled together. Any “dynamic” content, like items the player can pick up, should be bundled separately.This blog post and linked GitHub repo covers splitting bundles for an open-world game. It also features a way to deduplicate bundles to reduce the memory overhead of each bundle. Stages 4 and 5 are particularly relevant to open worlds.Q: When should I leave “AssetBundle CRC” enabled? A: The recommended practice is to have this enabled, excluding cached AssetBundles for Remote groups, and disabled for Local groups. The check is only meant to make sure the data wasn’t corrupted on download. There’s almost no reason to do the check for local AssetBundles.Q: When is it not worth it to use Addressables due to CPU performance concerns when loading and unloading assets? A: The Addressables system has a positive impact on CPU loading performance due to not needing to load all content up front.If you don’t use Addressables when loading a scene, you’d have to load all content and references. If you move the content to Addressables, you can choose when to load which content.For example, say you have an Inventory Manager in a scene that has a reference to 1,000 inventory items. If you don’t use Addressables, you’ll have to load every mesh, texture, audio clip, etc., for all these inventory items. If you wait to load this content, loading the scene will be faster.Q: Do all dependencies of an addressable asset also need to be Addressables, or is that only necessary if they are shared? A: Dependencies do not need to be marked addressable. Dependencies will be pulled into Addressables during the build process if necessary.As an example, if you make a player prefab an addressable, you don’t have to manually mark the player’s mesh, textures, or audio as addressable, too. When the bundle is built, all the dependencies that don’t yet exist in Addressables will be automatically included in the player prefab bundle.Q: If I forgot to release an asset and change scenes, what happens to this asset? A: Changing scenes does not inherently interact poorly with handles. But if you load an asset and forget to release its handle, the asset will persist in memory.Addressables has an internal reference-counting system. Handles are how we interact with this system. Loading an asset increments the reference count, and releasing decrements the reference count.Creators are responsible for keeping this reference count up to date. The asset will be in memory as long as the reference count is greater than one.Q: Related to the webinar example, suppose I’m making an open-world game. The boss is present somewhere in the open world. When the player heads to the boss, how do I use Addressables here? Do I send the command to load the sword async, via a trigger, at a certain distance from the enemy, or something else? A: It can be a fine line to choose when to load and unload content. You want to be sure the boss is ready when the player needs to see it, but might not want to load it too early when the player is still able to turn around and avoid the boss.The good thing is that you can iterate on when to load and unload content – you don’t have to get it perfectly optimized on the first try.To get started, we suggest loading all content for a particular “zone” when the player gets near. If this causes unnecessary memory pressure, you can add more fine-grained loading and unloading.If the sword is not loading soon enough, consider moving the loading trigger to start earlier, improving the load time of the sword assets by using Unity Profiler’s CPU module to see what is being loaded, or using Addressables synchronously to ensure the load is finished.This documentation includes more details and a code snippet for synchronous Addressables.Q: If I load an addressable when a scene starts, do I need to have a loading screen for it? A: Loading from Addressables is typically done in an asynchronous way, like with Addressables.LoadAssetAsync.There may be some content you don’t want to load before leaving a loading screen. You can collect these AsyncOperationHandles and wait for the necessary ones to complete before leaving.Q: What is the memory footprint of the addressables metadata at runtime? A: During Addressables initialization, the catalog file is loaded so that Addressables knows how to map labels and addresses to assets on disk or in remote locations. A larger catalog equates to a larger memory overhead at runtime.Catalog size can be reduced by stripping unnecessary data, like not including labels or GUIDs in groups that don’t need them, or by reducing the size of existing data. For example, by setting a group’s Internal Asset Naming Mode to GUID instead of filename or full path. You can view the runtime memory size of the catalog in Unity Memory Profiler.Q: What is the Unity Editor doing in the time it spends building Addressables? A: A build report log is output in the /Library folder. This log shows each step of the build process. To add additional details to the log, follow this path to select “Use Detailed Build Log”: Enabling Edit > Preferences > Scriptable Build Pipeline > Use Detailed Build Log.Check out visuals and documentation on how to view the log.Q: Does Resources.Loadalso have a duplication problem? A: Yes. It can be useful to think of Addressables content and Resources content as different “worlds.” If you have a texture in /Resources, one copy of that texture is included in the Resources file. If bundles in Addressables depend on that texture, each bundle includes an implicit copy of it. You end up with multiple copies of the texture on disk and potentially multiple copies in memory.To avoid this duplication, move the texture out of /Resources and add it to an Addressables group.Q: Do you get similar size on disk issues that are resolved by removing duplicate bundles when you don’t use Addressables? A: Yes. In the webinar and slides we show how deduplicating the two water racing scenes significantly reduced build size.Q: How can I prevent shader variant duplicates? A: Shaders can be deduplicated in the same process as any other asset – explicitly declare them in a group.If an asset is explicitly declared in an Addressables group that is going into your build, that asset will not be duplicated across multiple bundles.For shaders specifically, it is common practice for projects to use a “Shared shaders” group to contain shaders that you expect to need in memory for the lifespan of your app, and that are shared across many assets.Q: Do two Unity scenes sharing the same prefab duplicate build size? A: This depends on if the prefab the scenes depend on has been explicitly included in Addressables, and if the scenes are in the same or different bundles.See the visual explanation of how duplication occurs in the webinar slides and in this blog post under Stage 4.The key to remember is that all content going into a bundle needs to be able to access all of its dependencies. If you put a scene into a bundle, all of its dependencies need to either be:Explicitly included somewhere in AddressablesImplicitly included in the same bundleQ: Is it possible to compare duplicates in given groups to prevent having all the game assets packed together into an isolated group? A: Yes. You can run the built-in deduplication rule and then sort the assets in the Addressables Groups window into better groupings.Or, a more scalable approach is to write your own Addressables AnalyzeRules, which will appear in the Analyze window. The built-in rules are delivered as C# in the Addressables package and can serve as a baseline.For example, you may want to find every duplicate across all of your groups that start with “Character-”. Any implicit duplicates can be placed in a “Shared-Character” group.Q: Are you going to cover remote builds and local paths? A: We did not cover remote and local paths, which are called “Addressables Profiles” in the webinar. However, we do describe what Addressables Profiles are and how to use them in this documentation.Q: How does Addressables work with Cloud Content Delivery? A: CCD integration is discussed in this documentation.Q: Can you please give pointers on best practices to implement low- and high-resolution Addressables variations? A: You can find an example in the Addressables Sample on GitHub.Q: What if bundle content is encrypted? Does the UnityDataTool also decrypt the content? A: No. The data will need to be decrypted before UnityDataTool can analyze the content.Q: Is it a supported use case to build bundles from one Unity project and load the bundles at runtime from an app built from a different project? A: Yes. This is covered by using multiple catalogs at the same time.Q: Are there drawbacks to using InstantiateAsync, or situations where it is better to use LoadAsync + manual Instantiate? A: It is recommended to use Addressables.LoadAssetAsyncand call Object.Instantiate. Addressables.InstantiateAsynchas a larger performance cost.Q: I have a lot of ScriptableObjects with at least 1–2 sprites referenced as variables. If I want to change the sprites to Addressables, do I have to change the references to Addressables one by one, or is there any trick to do this? A: An Editor script is probably the way to go to convert these references.You can add the AssetReference fields to your ScriptableObject. Then, you can write an Editor script that iterates through your ScriptableObjects, looks up the Sprite asset in Addressables to find the associated AddressableAssetEntry, and stores the address or creates an AssetReference to be stored on the ScriptableObject.Lastly, you can remove the direct Sprite references and swap any related code to use the AssetReference.Q: Can I use addressables for WebGL games? If yes, are there any specific things to look for? A: Yes, and yes. Two things to note: First, WebGL does not support threading, so don’t use Tasks. Second, caching works differently on WebGL – we’ve seen issues with caching remote AssetBundles before.Q: If I use Shader.Find, is this coming from the build or Addressables? A: These are coming from the build of the Unity Player, not Addressables. Shader.Finddoes not return results from AssetBundles.Q: How can I organize the Addressables Groups window when I have many similarly-named groups? A: For organizing the Addressables Groups UI, you can enable Group Hierarchy with Dashes. This will group similarly-named groups together. For example, “Character-person” and “Character-person2” will appear in the UI under the “Character” grouping.This does not affect how bundles are created. This is only a UI organizational change.Share your feedback with us in the Addressables forum. Be sure to watch for new technical blogs from other Unity developers as part of the ongoing Tech from the Trenches series. #extended #qampampa #optimizing #memory #build

Disney announced on Monday evening that it will be holding a livestream Q&A for season 2 of its Star Wars drama Andor on Tuesday, May 14, at 12 p.m. ET. The stream will be hosted on YouTube, and will center on series creator, writer, and showrunner Tony Gilroy and star Diego Luna, though other guests from the show will also be present. Here’s the embedded livestream and the description of the event: In anticipation of the three-episode series finale of Andor, join Diego Luna and Creator & Executive Producer Tony Gilroy as they take us behind-the-scenes of the show’s final season. Diego and Tony will be joined by special guests Adria Arjona, Denise Gough, Elizabeth Dulau, Genevieve O’Reilly and Kyle Soller for live questions and reflections across their Star Wars journeys. With special appearances by some of your favorite creators including @HeroesReforged, @CatherineLaSalle, and @MaceAhWindu. Two things about this livestream feel unusual: First, it’s dropping right before the season and series finale of Andor, which goes live on Disney Plus at 9 p.m. ET on May 14, so fans won’t be able to ask questions that take the end of the season into account. (Likely a measure to avoid spoilers for people who haven’t watched yet.) And second, Disney says the participants will be taking live questions from viewers. As anyone who’s ever attended a live Q&A with significantly famous folk probably already knows, these kinds of forums rarely produce really great questions. That’s more often true in a live setting, where questioners often just want to hold the attention of those famous people, and tend to ramble or not ask questions at all. “This is more a comment than a question…” and “As a content creator myself, here are my thoughts on your work…” are clichés that still crop up at nearly every audience Q&A I attend at film festivals. And I will never forget attending a live-on-stage George Lucas interview that made time for questions at the end. A young man with a Chewbacca-bandolier messenger bag and no compunctions about wasting everyone else’s time got up to brag to Lucas about how many Moleskine notebooks full of story ideas he had back home, and ended with, “So my question is, Mr. Lucas, what can I do for you?” Lucas was… not gentle in his response. Collectively, we can do better. The key to a good Q&A is preparation — thinking in advance about questions that matter to you, then checking to see if maybe the participants have already answered that exact question elsewhere. (E.g. “Hey, Tony Gilroy, were you thinking of Nazi Germany when you wrote this show, or something more recent?”) And it’s important to be as specific as possible with questions — “What was the hardest part of the show to do?” isn’t a bad one, but it’s broad enough that it could be applied to any aspect of the writing, casting, shooting, editing, or post-production work, and might not get a particularly specific answer. So let’s pregame this interview. What, at this point, do you want to know from Tony Gilroy and Diego Luna (or any other promised participant in this project) that you think they haven’t already answered? What would you most like to hear them talk about? Personally, I’d like to know whether Gilroy ever considered any other end to Syril Karn’s arc. Not saying there’s anything wrong with what we saw on screen — Gilroy has called it a “Greek and dramatic” ending — but I really thought he was being set up for something else specific. What do you most want to ask the Andor creator and cast? (If nothing else, maybe the rest of us can help find a place where your question has already been answered, since there’s going to be a lot of competition to get questions through during the livestream.) Source: https://www.polygon.com/star-wars/598764/andor-tony-gilroy-diego-luna-season-2-livestream-where-to-watch #lets #pregame #tony #gilroy #diego #lunas #andor #livestream #qampampa