Propensity Modeling
A statistical technique that scores individual users on their likelihood to take a specific action, such as purchasing, churning, or upgrading.
Also known as: propensity scoring, likelihood modeling
Why propensity modeling matters
Propensity models turn your user base from a homogeneous group into a ranked list of opportunities. Instead of sending the same email to everyone, you can target the top 10% most likely to convert, personalize messaging for the middle, and save resources on the bottom.
The most common applications are propensity to buy, propensity to churn, and propensity to upgrade. Each model takes behavioral signals and outputs a score that your marketing and sales teams can act on.
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Propensity Modeling examples
A B2B SaaS tool scores trial users on their propensity to convert. Sales prioritizes the top 20% for personal outreach, increasing trial conversion by 35% while reducing sales effort.
Benchmark: Top-decile propensity scores should convert 3-5x above average
An online retailer scores visitors on purchase propensity based on browse depth, cart additions, and session duration. High-propensity visitors see real-time incentives.
Benchmark: Propensity-targeted campaigns typically see 2-4x lift over untargeted
How to Track in KISSmetrics
KISSmetrics person-level data is ideal for propensity modeling. Export user behavior data (events, properties, timing) to build models. Use the resulting scores as KISSmetrics user properties to create targeted segments and campaigns.
Common Mistakes
- -Using the model output as a binary (will/will not) instead of a probability range
- -Not retraining the model as your product and user base evolve
- -Building separate models when a single multi-class model would be more efficient
Pro Tips
- +Feed propensity scores back into KISSmetrics as user properties for targeted populations and campaigns
- +Calibrate your model so that a 70% score actually means 70% probability - this enables better decision-making
- +Test your model's business impact with a holdout group before full deployment
Related Terms
Predictive Analytics
The use of statistical models, machine learning, and historical data to forecast future outcomes like customer behavior, churn probability, or revenue trends.
Lead Scoring
Lead scoring is a methodology that assigns numerical values to leads based on their demographic attributes and behavioral engagement, ranking them by their likelihood to convert into paying customers and enabling sales teams to prioritize outreach.
Churn Prediction
A predictive model that identifies customers at risk of cancelling their subscription based on behavioral signals, usage patterns, and historical churn data.
AI Segmentation
The use of machine learning algorithms to automatically discover meaningful user groups based on behavioral patterns, without requiring manual segment definition.
Further Reading
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