Customer question
“Which customers are most likely to buy?”
What this solution does
Instead of treating every customer the same, this ranks them by how likely they are to buy soon, so marketing spend goes toward the people most worth reaching.
Specifically, it answers: Given behavioural signals about a customer, how likely are they to convert in a given window, so marketing can prioritise effort and spend accordingly?
How it works
Behavioural, engagement, and historical purchase variables are fed into a classification model that outputs a conversion probability per customer, which can then be segmented into propensity deciles for targeting.
Data requirements
- Previous purchase history
- Website/app visit and engagement data
- Recency and frequency of interactions
- Product/category view history
- Marketing engagement history
- Category interest signals
Discovery questions
What conversion event is being predicted, and over what time window?
Propensity models must be trained against a clearly defined outcome and horizon, or the score won't mean what stakeholders assume it means.
How much historical conversion data exists to train against?
Classification models need enough labelled positive and negative examples to learn a reliable pattern.
How will the propensity score actually be used in campaigns?
Determines whether the output needs to be a simple decile split, a continuous score, or feed directly into bidding.
Build an experiment
Generate synthetic data
Data readiness
Run model / simulation
Results
Business ⟷ Technical interpretation
The top propensity decile has an average conversion probability of topDecileProbability%, compared to bottomDecileProbability% in the bottom decile — a strong signal for prioritising media spend toward higher-propensity segments.
Shown as a template — the highlighted tokens are filled in once the model has been run.
What would you do next?
- Pilot a campaign targeting only top-decile customers and compare efficiency against a broader send
- Monitor propensity model performance over time and retrain periodically
Solution & vendor landscape
Implementation approaches
Pecan AI
Predictive Analytics / Propensity · Customer Lifetime Value
Klaviyo
Predictive Analytics / Propensity · Customer Lifetime Value
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