Your data already contains the early signs of what customers will do: the pattern of visits before a purchase, the fall in engagement before a cancellation, the category browsed before a trade-up. Predictive Consumer Behavior Analytics trains models on that history and on live signals, so your team knows what a customer is likely to do while there is still time to influence it.
Where it helps
- Churn or lapse discovered only after the customer has gone quiet
- Cross-sell and upgrade offers sent to everyone because nobody knows who is ready
- Demand for products or content that is forecast by category, not by customer
- Campaign timing based on the calendar rather than on when customers are receptive
What we deliver
- Propensity models. The likelihood of purchase, repeat purchase, upgrade or cancellation for each customer, with the factors that drive it.
- Preference prediction. Which categories, products, content and offers each customer is most likely to respond to.
- Engagement forecasting. Expected activity by customer and segment, so campaigns and stock can be planned ahead of demand.
- Live scoring. Scores refreshed on a schedule or in real time, written back to your CRM, messaging and ad platforms.
How an engagement runs
- Assess. We review the behaviours you want to predict, the history available and how the predictions will be used.
- Build. We train the first models, test them against past periods and check that they perform across segments, not only on average.
- Integrate. Scores reach the tools that act on them, from campaign audiences to the personalization engine.
- Run. We monitor accuracy and drift, retrain as behaviour changes and report on the outcomes the models influenced.
What changes for your team
- At-risk customers are flagged while retention is still possible
- Offers go to customers likely to want them, which keeps contact volume down
- Planning starts from a forecast of customer behaviour rather than last year’s pattern
