Predictive Modeling in Consumer Behavior

To make accurate predictions, you must process hundreds of variables, right? Nope. You just need a handful of the RIGHT ones. Having more can work against you. It’s called the bias-variance tradeoff. For example, in the figure at left, the overly simple, linear model doesn’t fully capture the behavior we see in the data. It’s biased, predicting values too low in the range 0.2 to 0.6 and too high in the other ranges. It’s an underfitting problem. On the other hand, the overly complex polynomial at right shows little bias, almost perfectly bisecting the data. But it’s a poor predictor for any new data: the variance between the test and training data is substantial (0.016) compared to the simple model (0.0008). This equation works well in the lab, but not in the real world. It’s an overfitting problem. The goal is to build parsimonious models – having just enough complexity to describe the behavior while still accommodating randomness. This Goldilocks approach also applies to multiple independent random variables (X, Y, Z, etc.) used for predicting churn, expansions, or any other outcomes. Many tech vendors, however, push sophisticated, reinforcement learning-style AI to “spray and pray.” Their software grinds away at reams of data, hoping for the best. But the models are rarely, if ever, checked for overfitting. Customer health score predictive accuracy is hidden, along with the opaque methods most AI models use for decision-making. What’s more, vendors and analysts alike suffer a cognitive bias called WYSIATI (“What You See Is All There Is”). They think the software usage data they have on hand is all they need. But data on the most telling factors – those describing why some customers leave and why others stay and buy more – goes uncollected. Why care? Bad predictions lead to bad decisions. As a result, Customer Success wastes time on accounts that renew while ignoring accounts that churn. This increases operating costs and reduces NRR. The lesson? Bigger isn’t better. Start with simpler, more effective models using the right inputs before chasing the exotic stuff. You’ll get better results. #customersuccess #customersuccessmanagement #revenueoperations #analytics

One of the biggest challenges in UX research is understanding what users truly value. People often say one thing but behave differently when faced with actual choices. Conjoint analysis helps bridge this gap by analyzing how users make trade-offs between different features, enabling UX teams to prioritize effectively. Unlike direct surveys, conjoint analysis presents users with realistic product combinations, capturing their genuine decision-making patterns. When paired with advanced statistical and machine learning methods, this approach becomes even more powerful and predictive. Choice-based models like Hierarchical Bayes estimation reveal individual-level preferences, allowing tailored UX improvements for diverse user groups. Latent Class Analysis further segments users into distinct preference categories, helping design experiences that resonate with each segment. Advanced regression methods enhance accuracy in predicting user behavior. Mixed Logit Models recognize that different users value features uniquely, while Nested Logit Models address hierarchical decision-making, such as choosing a subscription tier before specific features. Machine learning techniques offer additional insights. Random Forests uncover hidden relationships between features - like those that matter only in combination - while Support Vector Machines classify users precisely, enabling targeted UX personalization. Bayesian approaches manage the inherent uncertainty in user choices. Bayesian Networks visually represent interconnected preferences, and Markov Chain Monte Carlo methods handle complexity, delivering more reliable forecasts. Finally, simulation techniques like Monte Carlo analysis allow UX teams to anticipate user responses to product changes or pricing strategies, reducing risk. Bootstrapping further strengthens findings by testing the stability of insights across multiple simulations. By leveraging these advanced conjoint analysis techniques, UX researchers can deeply understand user preferences and create experiences that align precisely with how users think and behave.

A fascinating new study from PyMC Labs and Colgate-Palmolive reveals a breakthrough in consumer research: AI can now predict purchase behavior with 90% human-level accuracy by explaining its reasoning in natural language. Researchers used a method called Semantic Similarity Rating (SSR). Instead of rating a product on a 1–5 scale, the AI explains its thinking (“I’d probably buy it… the price isn’t too bad.”). Those explanations are then mapped to Likert ratings using semantic similarity. 📊 The results across 57 surveys (9,300 consumers): - Achieved 90% correlation with human test-retest reliability - Reproduced realistic response distributions (85%+ similarity) - Captured demographic nuances (age, income) that shape decisions - Generated rich qualitative feedback automatically It strikes me that this goes beyond marketing. SSR shows how reasoning-based AI can generate insights that blend statistical rigor with human nuance — understanding not just what people say, but how they think. Could be interesting in how this might apply in other sectors: 🏥 In health: SSR might be able model patient decision-making — why someone delays care, trusts one treatment over another, or struggles with adherence. Helpful in addressing treatment hesitancy or developing better explanations that meets people where they are. 🎓 In education: It could model how students respond to feedback or learning challenges, helping AI tutors personalize both content and motivation.  SSR could model how parents reason through trade-offs between tutoring, microschools, and online options, giving leaders a deeper insight into how families actually choose and what information helps them decide. Same for higher education. 🗳️I wonder if this could simulate thousands of citizen perspectives and develop a "first draft" explaining why people support a policy, what values drive their decisions, and how responses shift when given new information. Study: https://lnkd.in/exRNnY_s

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. 

*** Predicting Customer Purchases *** The goal—predicting customer purchases using historical data—for which here’s a statistical model framework tailored for that task: Model Overview: Predicting Customer Purchases Objective Estimate the likelihood or timing of a customer’s next purchase, or forecast future purchase amounts. Data Inputs From your purchase history, you’ll want to extract: • Customer ID • Purchase timestamps • Purchase amounts • Product categories • Channel (online, in-store) • Demographics (if available) You can engineer features like: • Recency: Time since last purchase • Frequency: Number of purchases in a time window • Monetary value: Total spend in a time window • Product affinity: Most purchased categories • Seasonality: Time-of-year effects Model Types for Predicting Customer Purchases 1. Logistic Regression• Use case: Predict whether a customer will purchase within a given time window (yes/no). • Strengths: Simple, interpretable, good baseline model. • Limitations: Assumes linear relationships between features and log-odds. 2. Random Forest / XGBoost (Gradient Boosting)• Use case: Predict purchase likelihood or purchase amount. • Strengths: Handles nonlinearities, interactions, and missing data well. • Limitations: Less interpretable, may require tuning. 3. Time Series Models (ARIMA, Prophet)• Use case: Forecast total purchases over time (e.g., daily/weekly sales). • Strengths: Captures trends and seasonality. • Limitations: Works best for aggregate data, not individual customers. 4. Survival Analysis (e.g., Cox Proportional Hazards Model)• Use case: Predict time until a customer’s next purchase or churn. • Strengths: Models time-to-event data, handles censored data. • Limitations: Requires careful assumptions about hazard rates. 5. RFM Segmentation + Clustering (e.g., K-Means)• Use case: Group customers by behavior (Recency, Frequency, Monetary value). • Strengths: Useful for customer segmentation and targeting. • Limitations: It is not predictive and is used more for profiling. Evaluation Metrics • Classification: Accuracy, Precision, Recall, AUC • Regression: RMSE, MAE, R² • Time-to-event: Concordance index Implementation Tips • Normalize or log-transform skewed features like purchase amount. • Use cross-validation to avoid overfitting. • Consider temporal validation (train on past, test on future). • Use SHAP values or feature importance to interpret results. --- B. Noted

Machine learning models are built to learn from customer behavior and make predictions. But when that behavior shifts rapidly, like during the pandemic, even the most accurate models can fall behind. That’s exactly what the Data Science team at Booking.com experienced while working on cancellation prediction. In a recent blog post, they shared how they evolved their approach to stay aligned with changing user behavior. Originally, the team used traditional classification models to predict whether a booking would be canceled. These models performed well when patterns were stable, but they struggled in fast-changing environments. One key issue: they relied on historical outcomes that often took time to materialize. Plus, they only answered if a cancellation might happen, not when. To address these challenges, the team shifted to survival modeling, which estimates the time until an event occurs. This approach enabled them to generate dynamic, time-sensitive predictions over the course of each booking. With multiple enhancements to their survival modeling pipeline, the team saw improved predictive accuracy, especially in volatile conditions. The shift didn’t just boost performance; it showed how reframing a business problem through a different modeling lens can unlock smarter, more adaptable solutions. #MachineLearning #DataScience #SurvivalModeling #Classification #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gU3TsMQP

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]

Do you know how to pinpoint which customers are most likely to respond to your marketing campaigns? With Cumulative Accuracy Profile (CAP) curves! CAP is powerful tool that helps data scientists enhance the precision of predictive models. Let’s create a scenario: Imagine you're a data scientist at a retail store with 100,000 customers. Typically, when you send out a promotional offer, 10% respond. What if we could improve this response rate through targeted offers? How CAP Works: Random Selection: If you randomly select 20,000 customers, around 2,000 will respond. Model-Based Selection: By using a predictive model, you can identify customers more likely to respond, increasing the response rate significantly. Creating a CAP Curve: Plot the cumulative number of customers on the x-axis and the cumulative number of respondents on the y-axis. Compare the performance of different models by analyzing the area between the CAP curve and the random selection line. Key Insights: The larger the area under the CAP curve, the better the model. An ideal model would predict all responders with minimal outreach, represented by a steep initial curve. Understanding and utilizing the CAP curve enables you to enhance your marketing strategies, ensuring targeted and efficient outreach. Have you used CAP or other advanced model assessment techniques in your job before?  Let me know in the comments! 👇 #DataScience #ModelEvaluation #CAPCurve #MachineLearning #MarketingAnalytics #PredictiveModeling

Hotel Booking Cancelled Last Minute; my friend was applying for a visa and made refundable hotel bookings, later canceling them as the travel dates or visa duration were still uncertain, something many of us have done. I was thinking what kind of prediction modelling for such user's these OTA platforms must be doing, initially thought it would be a classifier to predict cancellation. But they actually use survival modelling, a technique that predicts when a cancellation might occur by estimating a probability distribution over cancellation times rather than a simple yes/no outcome. One key advantage of this approach is that it provides conditional cancellation probabilities; for example, given that a booking hasn’t been canceled by day t, what’s the likelihood it will still be canceled later? This shift from classification to survival modeling helps in handle changing customer behavior, delayed labels, and non-stationarity in data offering deeper insights. Bonus survival modelling can be used on any platform it is more powerful than traditional churn prediction: Survival Modelling can be used to predict likelihood of user visiting back on the platform by 7 days PS. I will write a detailed blog in my Data Science and Problem Solving Newsletter on how survival modelling can be used instead of churn prediction, binary classifier.