Optimizing Co-Pay Card Strategies with Prescriptive Analytics

SweetSpotML provides brands the opportunity to potentially identify significant co-pay card related cost efficiencies quickly, and accurately via the application of machine learning technology and simulation.

We leverage potentially millions of rows of historical real-world claims data to develop highly accurate predictions of patient abandonment & persistency based on variation in out of pocket costs.

We have developed a proprietary simulation technique to leverage ML-based predictions to simulate outcomes of hundreds (often thousands) of distinct co-pay offer strategies, based on varying business rules (such as front/back end cap amounts, bifurcated offers, annual vs. monthly maximums, etc).

For each simulation, we are able to predict shifts in abandonment, persistency, redemption costs, and program transition rates in order to pinpoint optimal strategies based on demand maximization, cost minimization, profitability maximization, or a strategically-weighted combination of all three.