Predictive Modeling in Consumer Behavior

Most teams think of predictive models as answer machines. You ask a question, you get a score. Will this customer churn? How likely is this lead to convert? That's valuable. But it's only half the story. The real gold sits in the SHAP values behind each prediction. SHAP (SHapley Additive exPlanations) breaks down exactly which variables pushed a prediction in each direction, for every single customer or record. Not just "this customer is likely to churn," but why. Was it their purchase frequency? The channel they came from? How much revenue they've generated? Think of it as doing BI on AI. When you analyze SHAP values with a business lens, you stop looking at individual predictions and start seeing patterns. You can identify entire segments that share the same risk drivers. Maybe your high-revenue customers from one acquisition channel are three times more likely to leave than those from another. That's not just a prediction. That's a strategy. This is one of the most overlooked benefits of having a strong predictive model in place. The predictions tell you what's coming. The SHAP values tell you what to do about it. Want to go deeper? Here's a solid breakdown of how SHAP values work: https://lnkd.in/dxMMyhFH

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

*** 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

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.

👉 This will replace early-stage consumer surveys in #CPG 👈 This new paper is too important to keep inside my latest newsletter. It shows how #AI can now predict consumer purchase intent with over 90% correlation to real survey data — at a fraction of the time and cost. If you work in marketing, this changes your process. In simple: 👉 The problem before: Traditional consumer surveys are expensive and slow. People use panels to ask, “On a scale from 1–5, how likely are you to buy this product?” But when AI models are given that same task, they give boring answers: almost everything lands in the middle, with very little variation and no real differentiation. 👉 The innovation: The authors propose Semantic Similarity Rating (#SSR): 1. First, ask the AI model for a text explanation (e.g. “I might buy this if it helps my dry skin and isn’t too expensive”). 2. Then use semantic embeddings (a way of converting text meaning into numerical form) to match the explanation to predefined “anchor statements” that represent the 5 levels (1–5). This gives a probability distribution over 1–5 for each response, instead of forcing a single guess. 👉 It can now predict purchase intent with over 90% agreement to human surveys, simply by asking why, not just what. **** Some research details: - They tested SSR using: 57 real consumer product surveys, 9,300 responses from real humans (used as a benchmark) on personal care products.  - They used GPT-4o and Gemini 2.0 Flash.  - They tried different temperatures (a setting that controls randomness in AI): 0.5 and 1.5. -> They found little difference, so they only show results for temperature 0.5.  Things to watch out for: - AI is strong in domains it's seen a lot (e.g. personal care, oral care). In niche categories with little AI training data, results may degrade. - Persona conditioning is essential for realistic data. See link for paper in comment. Probably interesting for you too: Alexandra Ender Anne-Sophie Francois Iryna Zhuravel #marketresearch #innovation #insight #consumerproduct #LLM #AI #KI

What if AI could predict what you’d buy and why? That’s exactly what a new study from PyMC Labs just demonstrated. Researchers found that large language models (LLMs) can replicate 90% of human reliability in consumer purchase intent surveys — using a technique called semantic similarity rating (SSR). Instead of asking the model to pick a number on a 1–5 scale (“Would you buy this product?”), they asked it to explain its reasoning in words. Then they measured how close those words were, semantically, to real human responses. For the AI inclined: the team compared the embedded AI-generated text to reference statements like “very likely to purchase” or “not interested at all” using cosine similarity. To make the simulations realistic, researchers even told the LLM what kind of person to act as: age, gender, and financial status — before it gave its answer. The result: realistic, human-like survey data without the massive cost of traditional panels. https://lnkd.in/gpb7dEcP