Propensity & Capacity Modeling

Propensity models predict the likelihood of a customer taking a specific action. Capacity models estimate the potential value of that action. Together, they turn raw customer data into actionable intelligence — telling you not just who to target, but how much to invest in reaching them.

How it works

Propensity modeling operates at the individual customer level, predicting the probability of specific actions like making a purchase. It optimizes marketing spend by zeroing in on customers most likely to convert. The model generates propensity scores — probabilities reflecting future actions based on customer attributes like demographics, behavior, and history.

Capacity modeling predicts how much a customer is likely to spend. You can frame it as classification (grouping customers into spending tiers) or regression (predicting exact dollar amounts). By targeting customers with both high propensity and high capacity, businesses maximize ROI.

Sports marketing case study

I applied this to season ticket sales for professional sports teams. The challenge: thousands of fans in each team’s database and limited sales resources. We needed to predict not only who would buy, but their spending potential.

The process involved connecting to team databases, handling data imbalance between buyers and non-buyers, engineering features from historical spending trends and team-level metrics, and building unique models tailored to each team’s customer base.

One key decision: rather than using the same model for all teams, we experimented to find the best architecture for each. Automated feature selection kept models focused on the most predictive variables. Logging features and performance metrics provided transparency for continuous improvement.

Operationalization

Building models is half the battle. We implemented automated pipelines that process new data through the same training pipeline, apply the right models, and append results to the database. A feedback loop compared predictions against actual outcomes, keeping models relevant over time.

Key takeaway

Combining customer data with external factors — team performance, pricing, schedules — produced significantly deeper insights. The models didn’t just optimize sales outreach; they fundamentally changed how teams thought about customer relationships and revenue strategy.