Predictive Customer Insights

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

Last yr, I went to Joshua Tree and saw a 70-year-old grandma driving a Harley-Davidson. Why does this matter to DTC?   Most DTC brands blindly focus on the demographics and lifestyle profiles of their customers.   (Grandmas, young, male, household income.)   . . . When what is more predictive is their behavior.   "Who are our customers?" Think actions: ➝ Acquired through Google. ➝ Visited our site 3 times before purchasing. ➝ Haven’t been back in 4 days. The more you focus on behavioral segments first, the easier it will be to grow your business. Three reasons why behavioral profiling gives you an edge:   1️⃣ More predictive. Who is more likely to buy from you in the future: The person who last visited your website yesterday or the person who last visited two years ago?   Recency matters.   Who is more likely to buy from you in the future, the customer who bought from you once before or the customer who bought from you ten times before?   Frequency matters. This is why at PostPilot, we build most retention campaigns on a Recency Frequency (RF) basis.   2️⃣ More helpful in selling to your existing customers. Two guys: Steve (household income of 20K) and Joe (household income of 200K).    Poor Steve’s bought from you before. Rich Joe hasn’t.   In Steve’s case, he bought a jump rope from you before. You want to sell more stuff to your customers. Based on what you’ve seen from your customer base, people who buy jump ropes ultimately buy kettlebells.   So your next offer to Steve is a kettlebell. And maybe a warm-up band.    Like many of your customers before, Steve buys the kettlebell as the natural second purchase.   And Joe still hasn’t made a purchase yet.   The behavioral record will help us increase our CLV from Steve, where demographic information won’t do that. 3️⃣ Behavioral segmentation is WAY more actionable. It doesn’t help me to know that the typical customers on my website might read Time magazine or live in New Jersey or are an average age of 51.   But if I know... ➝ Products they’ve purchased before ➝ Last time they opened an email ➝ How they were acquired . . . And all kinds of behavioral factors, I can act.   I can set up rules in tools like Klaviyo and PostPilot, and I can market to them differently and sell to them differently. It’s much more actionable. And automate-able.   BTW. . .    I’m not arguing that demographic segmentation is useless.    Certainly, it’s helpful.    (Really, the Holy Grail is when you can combine behavioral with demographic segmentation.)   But RF(M) behavior should be your first and consistent focus.    And direct mail can help there. We build all the following campaign types around RF: ➝ Winbacks/VIP winbacks ➝ Second-purchase campaigns ➝ Cross-sells & upsells ➝ Subscriber reactivation ➝ Replenishment reminders   Set yourself up and drive repurchases from your own Harley Grannies. 

🚨 I've been teaching personalization wrong. After analyzing 1,000+ campaigns, I discovered what the 89% who see ROI actually do differently. It's not what you think. While most brands are personalizing EMAILS... The smart ones are personalizing PREDICTIONS. Here's what I found: The $82 Billion Secret: • Predictive analytics market exploding from $18.89B to $82.35B by 2030 • But 73% of companies still react to customer behavior instead of predicting it • The winners? They know what you want before YOU do 3 Things the 89% Do That You Probably Don't: 1️⃣ Entity Optimization (Not Just Keywords) → They use schema markup to make AI understand their content → Result: 2x more discoverable in AI search results → While you optimize for Google, they're optimizing for ChatGPT 2️⃣ Predictive Personalization (Not Reactive) → They analyze intent data to identify prospects before they're ready to buy → Result: 5x faster lead identification and 300% better accuracy → While you send "personalized" emails, they predict customer lifetime value 3️⃣ Behavioral Forecasting (Not Demographics) → They track micro-behaviors across 12+ touchpoints → Result: 122% higher email ROI and 202% better conversion rates → While you segment by age/location, they predict next purchase timing The brutal truth? 76% of consumers get frustrated when brands fail to deliver true personalization. Your customers can smell "Dear [First Name]" from a mile away. But here's what terrifies me: 71% of B2B buyers now EXPECT personalized digital interactions. If you're not using predictive analytics, your competitors who are will capture your market share while you're still guessing what customers want. The question that keeps me up at night: Are you predicting customer behavior or just reacting to it? What's the biggest challenge you face with implementing predictive analytics?

Survey data often ends up as static reports, but it doesn’t have to stop there. With the right tools, those responses can help us predict what users will do next and what changes will matter most. In recent years, predictive modeling has become one of the most exciting ways to extend the value of UX surveys. Whether you’re forecasting churn, identifying what actually drives your NPS score, or segmenting users into meaningful groups, these methods offer new levels of clarity. One technique I keep coming back to is key driver analysis using machine learning. Traditional regression models often struggle when survey variables are correlated. But newer approaches like Shapley value analysis are much better at estimating how each factor contributes to an outcome. It works by simulating all possible combinations of inputs, helping surface drivers that might be masked in a linear model. For example, instead of wondering whether UI clarity or response time matters more, you can get a clear ranked breakdown - and that turns into a sharper product roadmap. Another area that’s taken off is modeling behavior from survey feedback. You might train a model to predict churn based on dissatisfaction scores, or forecast which feature requests are likely to lead to higher engagement. Even a simple decision tree or logistic regression can identify risk signals early. This kind of modeling lets us treat feedback as a live input to product strategy rather than just a postmortem. Segmentation is another win. Using clustering algorithms like k-means or hierarchical clustering, we can go beyond generic personas and find real behavioral patterns - like users who rate the product moderately but are deeply engaged, or those who are new and struggling. These insights help teams build more tailored experiences. And the most exciting part for me is combining surveys with product analytics. When you pair someone’s satisfaction score with their actual usage behavior, the insights become much more powerful. It tells us when a complaint is just noise and when it’s a warning sign. And it can guide which users to reach out to before they walk away.

Some moments in life remind you that the journey is just as important as the destination. Grateful for the people who make learning, growth, and hard work enjoyable. Behind every project is the support, laughter, and encouragement that keep us going. Telecom companies spend millions acquiring new customers, yet many leave within their first six months. What if businesses could predict who is likely to leave and take action before they do? That’s exactly what I set out to solve using Machine Learning and #datascience. I built a customer retention prediction model using real-world telecom data to uncover patterns behind service cancellations and help businesses retain more customers. I started with exploratory data analysis to identify key trends influencing customer drop-off. Feature engineering played a huge role in transforming tenure, contract types, payment methods, and internet usage into meaningful insights. To improve prediction accuracy, I balanced the dataset using #SMOTE and tested multiple machine learning models, including Logistic Regression, KNN, Decision Trees, and Random Forest. After rigorous testing, Random Forest with fully engineered features delivered the best performance, achieving a ROC-AUC score of 0.845. It effectively identified at-risk customers with a recall of 79.2 percent while maintaining a precision of 51.8 percent to reduce false alarms. This project is not just about building models but about making Machine Learning work for real business problems. Turning raw data into actionable insights is where the real impact happens. GitHub Repository: [Customer Retention Prediction](https://lnkd.in/d4e6p_M4) #MachineLearning #DataScience #CustomerRetention #PredictiveAnalytics #Python #AI #FeatureEngineering #RandomForest #BusinessStrategy

This is the way we've always done it. Those words will cost dealerships millions in 2025. Here's why based on how I see it. While you're running your store the same way you did in 2023, your customers have evolved. They're interacting with AI daily - from Netflix recommendations to Amazon shopping to their iPhone's predictive text. They expect the same intelligence from their car buying experience. Here are 3 simple, high-impact AI implementations any dealership can deploy in 2025: Intelligent Service Follow-Up Stop sending generic "14-day service follow-up" emails. Use AI to analyze repair orders, vehicle history, and customer behavior to send personalized follow-ups that actually drive value: - Predictive maintenance recommendations based on driving patterns - Custom offers based on repair history - Targeted trade-in opportunities based on service costs → Impact: 40%+ increase in service retention Data-Driven Customer Intelligence Stop treating every lead the same. Use AI to understand your customer before the first interaction: - Calculate true purchase propensity using behavioral patterns - Analyze website engagement depth and frequency - Assess affordability based on customer cohort data - Understand similar customer purchase patterns - Track digital body language across all touchpoints This intelligence helps you instantly distinguish between ready-to-buy customers, early-stage shoppers, and tire kickers - allowing your team to customize their approach and maximize every interaction. → Impact: 2-3x improvement in lead conversion rates Unified Customer Insight for Sales Transform how your sales team understands customers. Create a single, AI-powered view that brings together: - Complete vehicle ownership history - Service interaction patterns - Communication preferences - Family vehicle needs - Recent life events - Website browsing patterns - Current vehicle equity position This enables your team to have meaningful, personalized conversations from the first interaction - no more generic "what brings you in today?" → Impact: 30%+ reduction in sales cycle time, 25% improvement in customer satisfaction The beauty? These aren't massive technology overhauls. They're practical implementations that work alongside your existing systems. The cost of maintaining "the way we've always done it" isn't just measured in missed opportunities - it's measured in customers who choose to shop elsewhere. What "always done it" processes are you ready to evolve? #QoreAI #Automotive #AI #Innovation #DealershipOperations #DigitalTransformation

Smart CRM Basics Predictive Customer Behavior Modeling The Advantages of Predictive Behavior Modeling When Marketers can target specific customers with a specific marketing action – you are likely to have the most desirable campaign impact. Every marketing campaign and retention tactic will be more successful. The ROI of upsell, cross-sell, and retention campaigns will be more significant. For example, imagine being able to predict which customers will churn and the particular marketing actions that will cause them to remain long-term customers. Customers will feel the greater relevance of the company’s communications with them – resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals. Enhancing Customer Segmentation for Personalization Predictive analytics refines customer segmentation by identifying patterns within data. By understanding customer segments on a deeper level, businesses can personalize their interactions, marketing messages, and product recommendations. This tailored approach fosters a stronger connection with customers, leading to increased loyalty. Anticipating Customer Needs Through Lead Scoring Lead scoring becomes more accurate with the integration of predictive analytics. By evaluating customer data, such as interactions with emails, website visits, and social media engagement, businesses can prioritize leads based on their likelihood to convert. This ensures that sales teams focus their efforts on leads with the highest potential. Optimizing Sales Forecasting Accurate sales forecasting is crucial for effective resource allocation and business planning. Predictive analytics in CRM analyzes past sales data, market trends, and customer behaviors to generate more accurate sales forecasts. This empowers businesses to make informed decisions, allocate resources efficiently, and capitalize on emerging opportunities. Transforming CRM with Predictive Analytics Predictive analytics is revolutionizing CRM by providing invaluable insights into customer behaviors. From personalized marketing campaigns to proactive churn prevention, businesses can leverage these predictions to enhance customer relationships and drive growth. As technology continues to advance, integrating predictive analytics into CRM systems is not just a strategy for staying competitive; it's a key component in building lasting customer-centric businesses in the digital age. #PredictiveAnalytics #CRMInsights #CustomerBehavior #DataDrivenDecisions #BusinessIntelligence #CustomerRetention #SalesForecasting #MarketingStrategy #EthicalCRM #DynamicPricing

“She blinded me with science!” 🎤 “She Blinded Me With Science” -Thomas Dolby If a #CustomerSuccess leader found a genie that gave them 3 wishes, after rightly asking for “more wishes,” I’m guessing they would ask for a way to predict churn. [OK maybe they’d ask for world peace, a raise, … but go with me!] Our brilliant data science, Pau Ortí Codina, helped us understand which product features at Gainsight were the most predictive of retention or churn. For context, for years, we had done analysis to look at how usage of specific Gainsight Customer Success features correlate with retention. The challenge is that this approach can end up outputting many features that align to retention. But some of these features may themselves be correlated to each other. So the question is which few features we should focus on? Luckily, we had Pau. I asked him about his methodology and here’s how he approached it: 1: We started with hypotheses - which features could be indicators of retention. 2: We pulled renewal data from a year ago. 3: We made sure to avoid “survivorship bias” by looking at data 9-12 months before the renewal. The logic is that if you look at usage data near the renewal, it could be misleading. A customer’s usage could have dropped BECAUSE they are leaving. 4: We used several statistical methods to see how each feature correlated to renewal outcomes; we removed those without strong correlations. 5: We employed a decision tree classifier (see below) to understand how the variables relate to each other. 6: Pau then evaluated the model. If the model predicted a renewal, it was correct 96% of the time. By contrast, if the model predicted a churn, 50% of the time the client renewed. This isn’t great (ideally, churn predictions would have no false positives), but it’s better in CS to be more cautious rather than less. At the end of the day, we determined that clients with usage of our Journey Orchestrator digital automation feature were much more likely to renew. Have you run any data science-based model to predict renewal and churn for your business? If so, what did you learn? [The red boxes are confidential data that I blanked out]

Sometimes customers walk away from banking and financial services like they suddenly remember they left the stove on. It's frustrating!  You're offering great products, stellar service (or so you think), and suddenly – poof – they're gone. What gives?  Understanding and predicting churn is essential for the BFSI sector, and that's where the power of data comes in. Key Churn Indicators: What to Watch For? 🏃♂️ Inactivity: If a customer hasn't logged into their account, used their card, or interacted with you in ages, it's a red flag. 🏦 Reduced Transaction Volume: A sudden decline in transactions or spending patterns could signal dissatisfaction or a shift to a competitor. 👺 Complaints and Negative Feedback: Don't ignore those grumbles! Complaints are often the first step towards churn. Analyze them closely. 🌎 Demographic Shifts: Changes in a customer's life stage, income, or location can all lead to changing financial needs and potentially churn. Google BigQuery and Looker to the Rescue. This is where the dynamic duo of Google BigQuery and Looker enter the picture. Imagine having a crystal ball that can show you which customers are most likely to churn, and why. 👉 BigQuery: This powerhouse data warehouse lets you store and analyze massive amounts of customer data from numerous sources. It's like having a giant filing cabinet overflowing with customer insights. 👉 Looker: This data visualization platform transforms the raw data in BigQuery into stunning visualizations. Consider it the tool that translates complex data into clear, actionable patterns. BFSI + BigQuery + Looker = A Winning Combo. So, what kind of magic happens when you bring these tools together for BFSI? 📰 Customer Segmentation: Slice and dice your customer base to identify high-risk groups. Are young professionals more likely to churn? What about customers with high balances but low engagement? 🔮 Predictive Modeling: Develop models to predict which customers are most likely to leave, giving you a chance to intervene before bidding them farewell. 🎯 Targeted Retention Campaigns: No more generic "we miss you" emails! Use your insights to personalize retention offers and messaging that truly resonate with at-risk customers. Churn hurts, but it doesn't have to be a mystery. By harnessing the power of data analysis, BFSI organizations can get ahead of the churn curve.  You'll improve customer retention, boost revenue, and quite possibly stop yourself from obsessively checking if the stove is still on. If you're a BFSI organization struggling with data overload and want to turn those insights into action, we at Google are here to help. Let's talk about how I can help transform your data problems into profitable solutions. Reach out to us! Follow Omkar Sawant and (VJ) Vijaykumar Jangamashetti ☁️ for more information! #Churn #GoogleCloud #Fintech #DataAnalytics #ml #DataDrivenDecisions