Kuntal Malia

What about #Predictive #Segmentation? In my last post, I discussed the power of historical data in segmenting customers using methods like RFM. But what if we could look ahead, not just at what customers have done, but what they’re likely to do next? That’s where Predictive Segmentation steps in. Let’s clear up a common misconception first: predictions are often labeled as the latest “AI” breakthrough, but in reality, predictive modeling is rooted in Machine Learning (ML) techniques that have been around long before the AI boom. While predictive segmentation does leverage AI, it’s not a new technology in our domain. Here’s how predictive segmentation can transform your CRM strategy: 1. Predicting New User Behavior Predictive models can analyze user actions — even those who haven’t made a purchase yet — to forecast future behavior. By examining non-purchase actions like browsing patterns or content engagement, businesses can segment users who are likely to convert into buyers, long before they do. This insight allows us to create proactive engagement strategies that are personalized and timely. 2. Predicting Existing Customer Behavior For your existing customers, predictions can help identify those who are at risk of churn. For example, a Champion customer today could be on a path to becoming a lapsed customer based on recent actions like lower interaction frequency or reduced spend. Without predictive models, detecting these shifts would be much harder, if not impossible. By forecasting future behaviors, businesses can intervene with retention strategies before it’s too late. 3. Predictive Customer Profiling: Finding Your Ideal Customers Beyond segmenting based on behavior, predictive models can help businesses define their ideal customer profiles (ICPs) by analyzing demographic data. These models use data such as age, location, income, and other characteristics to find patterns that reveal your best customers — those most likely to engage, convert, or become repeat buyers. This helps businesses tailor their marketing strategies toward the right audience, ensuring efforts are targeted at customers who fit their ideal profile. Predictive profiling also helps identify new customer types, expanding your reach to similar profiles you might not have initially considered. In essence, predictive segmentation isn’t just about understanding past behavior but anticipating the future, allowing businesses to stay ahead of their customers’ needs. Stay tuned for my next post, where I’ll dive into how to implement predictive models into your everyday customer-centric strategies. #PredictiveSegmentation #CRM #MachineLearning #CustomerInsights #AI #SupervisedLearning #UnsupervisedLearning #CustomerProfiling #CustomerExperience

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If you missed my ESOMAR talk on AI and its implications for the Market Research industry, you can view it here: https://bit.ly/3NhReLT (registration required but it's free).

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A culture of AI experimentation is not simply born. It is cultivated through fostering curiosity and encouraging creative risk-taking. Leaders are often expected to have all the answers, particularly when it comes to the rapidly evolving field of AI. However, the reality is that we might not have all the solutions just yet. Instead of focusing solely on finding definitive answers, we should prioritise creating a culture that embraces AI experimentation. This is especially necessary as there is a significant disconnect between the growing interest in AI and its actual implementation in the workplace. With only 15% of workers feeling fully prepared to use AI effectively, it’s clear that there’s a need for a shift in approach. #AI Culture #Innovation #Leadership

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The digital age brings with it a range of complexities, and AI-generated misinformation is one of them. As AI capabilities continue to evolve, so too do the challenges associated with its potential misuse. However, by fostering a culture of critical thinking and equipping people with the skills to discern fact from fiction, we can get ahead of and mitigate the spread of misinformation. #AI

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🧩 Reading [Google Research's latest article](https://lnkd.in/ejk3_wMH) on understanding relationships between datasets got me thinking about the bigger picture in data management. This research brings out the importance of uncovering and automating complex relationships within raw data—a task that’s essential but often challenging. ✨ Palantir’s Ontology approach came to mind here as a powerful example of putting these ideas into action. By creating a digital twin of business entities and their relationships, Palantir enables companies to interact with data at a higher, business-relevant level. This abstraction layer transforms raw data into meaningful insights. ⚙️ To bring further structure, tools like Featuretools, Woodwork, Compose, and EvalML offer an open-source framework set that standardizes features and prepares data for machine learning. With **Featuretools** for automated feature engineering and **Woodwork** for consistent data typing, this toolkit enables the creation of reusable data products right from raw schemas and tables. 🌐 Imagine combining Google’s research-driven insights, Palantir’s digital twin framework, and the Featuretools ecosystem. Together, they could enable a truly dynamic data ecosystem—one where relationships are not just mapped but also standardized, enriched, and primed for decision-making. #DataManagement #Ontology #FeatureEngineering #GoogleResearch #Palantir #MachineLearning #OpenSource

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In the rush to adopt artificial intelligence, many organizations are getting caught up in the hype, chasing after the latest AI trends and technologies. However, this excitement often overshadows a crucial truth: AI is only as good as the data it's built upon. A strong data foundation is the bedrock of successful AI implementation. This means having clean, well-organized, and accessible data across your organization. It involves proper data governance, robust data infrastructure, and effective data management practices. While your competitors may be scrambling to implement flashy AI solutions, those who invest in their data foundations will have a significant advantage. With high-quality, well-structured data, you'll be able to: - Train AI models more effectively - Generate more accurate insights - Make better-informed decisions - Adapt more quickly to changing market conditions In the long run, this focus on fundamentals will give you a competitive edge that flashy but poorly implemented AI solutions can't match. Remember, in the AI race, the tortoise with solid data practices often outpaces the hare chasing after the latest trends. By prioritizing your data foundation, you're not just preparing for current AI applications, but positioning your organization for future innovations as well. In the ever-evolving landscape of AI, a strong data foundation is your most valuable asset.

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A single ChatGPT query can generate up to 100 times more carbon emissions than a standard Google search. Now that the initial generative AI hype has passed its peak, it’s becoming more clear that we need a nuanced approach to tech adoption. As we push the boundaries of what’s possible with AI, we must also remain vigilant stewards of our planet. Balancing technological advancement with sustainability is not just a challenge but an opportunity to lead in both innovation and environmental responsibility. #AI #ChatGPT

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Calling all retailers, marketers, and industry experts! Don’t miss out on the latest retail and shopping trends shaping consumer mindsets in the Middle East. From sustainable consumerism to AI-powered hyper-personalization and the rise of creator brands, these insights are set to transform the industry. Get ready to dive in and be inspired! #retail #MENA #Shopping #KSA #consumertrends #consumerinsights #Marketresearch #4sight #AI #sustainable #shoppingtrends #personalization #industryreport

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🔍 Just read a fascinating paper on the future of data ecosystem business models in the emergent "Artificial Intelligence of Thinks" (AIoT) space! Very useful systematic research that yields some key insights on how companies are transforming data into value in this developing ecosystem. https://lnkd.in/ert4nU7X 🔄 FOUR DISTINCT BUSINESS MODEL ARCHETYPES IN THE AGE OF AI ARISING 🤝 Adaptive Partnership - High control + High customization - Focuses on developing close, collaborative relationships with customers - Offers deep insights and personalized solutions - Best suited for strategic, long-term business relationships 👥 User-Centric - Low control + High customization - Prioritizes customer experience and flexibility - Enables broad market reach and scalability - Emphasizes user autonomy in configuring solutions ⚡ Standardized Efficiency - High control + Low customization - Focus on operational excellence and cost reduction - Streamlined processes and standardized offerings - Ideal for high-volume, routine operations [3] 🔄 Flexible Transaction - Low control + Low customization - Transaction-focused with minimal ongoing engagement - Emphasizes automation and self-service - Suitable for commoditized offerings 💎 AIOT BUSINESS MODEL VALUE CAPTURE STRATEGIES: 📊 Responsive Monitoring: Goes beyond traditional monitoring by enabling instant actions based on real-time data, including automatic corrective functions and predictive maintenance to prevent failures 🤖 Object Self-Service: Drives objects toward independent functionality with less human intervention, incorporating self-adjustment, replenishment, and self-operation capabilities 💡 Insights as a Service: A data-driven value proposition that leverages AIoT to collect, analyze and deliver real-time and collective insights that give meaning to massive amounts of data, enabling customers to make informed decisions   ✨ Enhanced User Experience: Focuses on improving customer interaction through digital add-ons and customizable interfaces and content that adapt to individual users' preferences and behaviors over time 💡💡 The research shows successful AIoT implementations balance control and customization to create sustainable competitive advantages. 💡💡 🤔 Where do you see your organization fitting in this framework? #AIoT #DigitalTransformation #Innovation #BusinessStrategy

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If you use the right predictive model, you can have an outsized influence on the "movable middle." Here's how our approach is moving the needle for our clients: 1. Model Selection: We're flexible. Whether it's a regression model or a sophisticated machine learning algorithm, we choose what works best for the specific challenge at hand. 2.Customer Ranking: Our models rank customers from most to least likely to exhibit a desired behavior. 3. Decile Division: We split these rankings into 10 equal groups, or deciles. From there, we can lump customers into one of three categories: -Top Deciles: They'll likely engage without extra incentives. -The Middle: This is often where the greatest opportunity lies. -Bottom Deciles: Unlikely to engage regardless of efforts. We focus on that potentially movable middle group. By testing different offers, messages, and channel mixes, we can influence behavior where it matters most. By understanding where customers fall in these deciles, we can optimize marketing efforts and allocate resources for maximum impact. #DataDrivenMarketing #CustomerInsights

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Want to know how I turned a data team into a well-oiled machine? (Hint: No actual oil required) Just dropped my latest "Data Science for Decision Makers" column on All Things Innovation. "Building the Right Data & Insights Team" - sounds thrilling, right? Like watching paint dry, but with more Python. Trust me: It's a rollercoaster ride through the wild world of data team structures. Centralized, decentralized, hub-and-spoke - it's like a corporate version of rock-paper-scissors, except everyone loses if you pick wrong. Want to know which structure will make your data scientists actually talk to other humans? Or how to avoid turning your analytics department into a glorified Excel club? Head over to All Things Innovation and prepare to have your mind blown. Or at least mildly expanded. No refunds if you fall asleep halfway through. P.S. If you read it and still can't figure out how to structure your team, just blame it on "market volatility" like everyone else. #DataStrategy #TeamBuilding #CorporateComedy https://lnkd.in/e3QBSF9D

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Greetings and welcome to the latest edition of Ipsos Update for April 2024. In this issue, we delve into how organisations can harness the potential of individuals outside the mainstream, how manufacturers and retailers can promote sustainable behavior among shoppers, and strategies for engaging with consumers during Ramadan. Our global surveys cover a range of topics, including changes in happiness levels worldwide over the past decade, attitudes towards gender equality, and the evolving concerns around issues like inflation. Highlights from this edition include insights on tapping into the creativity and innovation of those on the fringes, the latest findings from the Global Happiness Survey, and reflections on International Women's Day 2024. Additionally, we explore ways to shift shoppers' attitudes towards ESG behaviors and examine the opinions of Internally Displaced Persons and Ukrainian refugees. In addtition to mark International Day for the Elimination of Racial Discrimination we are publishing some of our findings in advance of our upcoming #Equalities Index. And four years on from #COVID19, We look at how views on everything from inflation to mental health have changed since 2020. Join us as we discuss what worries the world today, the significance of Ramadan in the MENA region, and the future implications of Italy's declining population on various aspects of society. #MarketResearch #IpsosUpdate #IWD2024 #Happiness #Ramadan #Italy #IDERD

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For those who want a 5 min crash course on MMM and potential biases that can impact modeling, this is an incredibly useful read.

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Who knows? I think looking 10 years into the future is a fools errand with AI, but that doesn’t change the things we already know. 1. Current GenAI is very good at a limited number of tasks and fails miserably at other tasks. Use it where it’s good. 2. AI will be better, cheaper, and faster than it is right now. Some things that fail today will work in the future. Plan for better (but not perfect and omniscient) AI. 3. It’s about tasks not jobs. Understand the tasks people do and which can be improved by AI. It’s not replacing whole jobs today. 4. Coding is a huge unlock. Having someone in the team, department, or org who can code can 10x the use cases where AI can be brought to bear and is critical for automation. You probably already have this person / people on staff. Empower them. Look for the people writing excel macros or doing data analysis with R/python. 5. There’s no going back. AI is a D mark in humanity the same way personal computers, the internet, and mobile were. Eschewing AI is robbing yourself or your org of the skills it needs even if AI is “kindof dumb” right now.

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Making Sense of Data Context: A Simple Guide We often see LinkedIn posts and read about "understanding data context" but what does this really mean in practice? For me, it boils down to asking these three questions in a respectful, safe environment where no one feels offended: • Inquire about data sources: • Would you mind sharing the sources of this data? • Once you know the sources, ask: • Who can tell me which systems touched this data along along the way? • Before delivering the dashboards or reports ask: • Why does it matter to our users? Pro tip: Ask these questions early in your data projects. They'll save you time, build trust with stakeholders, and ensure your analysis actually drives value. Remember: Better context leads to better decisions but make sure the environment is safe before you shoot your questions. What other things you do to learn the data context better? Please share. #businessIntelligence #dataContext #databestpractices #dataleadership #bi #dataengineering

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Good Read for best Prompt techniques

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The definitions of qual and quant are getting blurred with new AI capabilities as is clear with our new AI moderated video feature in Upsiide, by Dig Insights. But this confusion over definitions of quant and qual isn’t a new phenomenon. Social scientists and statisticians (my undergrad) use the terms quantitative vs. qualitative data to refer to continuous vs. discrete data in a model. For example, if you have a “qualitative” dependent variable, then you’d run a logistic vs. linear regression. In market research (my industry), qual typically means unstructured data (e.g., interviews/text) and quant means structured data, usually survey data. But other data heavy industries sometimes refer to surveys as qual because they're stated opinions and behaviors, not data on what actually happened. To some people, quant means behavioral data (e.g., purchases at store). And in the future, our data capabilities may increase by another step change. We could have real-time data ingestion using computer vision techniques to quantify almost anything, such as how many people are wearing a specific brand of shoes in a specific city. The AI is already capable, but it’s a complex issue in terms of data privacy and security. If that does come to fruition, then maybe the goal posts shift again and current behavioral quant data becomes “qual” and real-time data is the new gold standard “quant”?

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