Pharmaceutical Sampling Optimization

1. Pharmaceutical Sampling Optimization SDSIC Forum on Analytics November 2007

2. Agenda • Sampling overview • Modeling sampling response • Measuring plan Performance

3. Role of sampling in pharmaceutical sales • Pharmaceutical industry has similar expenditures for marketing and R&D overall • Largest share of marketing $ is for sales force that makes direct calls to health care professionals (HCPs) • Sampling is typically the second largest budget item for providing direct product information to HCPs • Sample budgets for billion dollar products may range from 30-70 million dollars • Often samples are instrumental in gaining access to an HCP to provide product information

4. 120% 100% 80% 60% % Max Margin Rate of marginal return decreases as samples increase 40% 20% 0% 30% 40% 50% 60% 70% 80% 90% 100% 110% 120% 130% 140% Samples - % of Max Margin Diminishing returns inform sampling decision • Example (conceptual) * 90% of max margin samples provides 98% of cumulative margin * 80% of max margin samples provides 94% of cumulative margin

5. Modeling Sampling Response Section 2

6. Rx Samples Details Issues: Correlation • How do you assign a value for the independent influence of two activities, e.g. samples and details?

7. Modeling Technologies

8. Modeling Comparison: Example 1 • Distinguish two spirals: blue versus red • Circles are training data • Plus signs are test data • Kernel method classification accuracy • 100% on training data • 100% on test data • Linear regression accuracy • For y = f(1,x,y,x2,y2,x*y) • 49% on train data • 48% test data • Guessing would give an expected accuracy of 50% Spiral Model Results

9. Response of Rx to Samples Response of Rx to Details New Rx New Rx Details Samples Modeling Comparison: Example 2 • Create a test case promotional response model • Compare performance of KM and Linear Regression • New Rx = Details New Rx + Samples New Rx; no noise • A simple model reflecting important features of the real problem

10. Comparing Rx from Linear Regression with Target Rx Comparing Rx from Linear Regression with Target Rx New Rx New Rx Samples Details Rx = f(S,S2,D,D2,S*D,D0.33) Linear Regression: Model 1 • Linear Regression Model 1 Results • Prediction of sample and detail response has room for improvement • Correlation ( Predicted Rx, Actual Rx ) = 0.994; mean abs error = 0.333 • Graph is prediction for details when samples = 0, and for samples when details = 0

11. Comparing Rx from Linear Regression with Target Rx Comparing Rx from Linear Regression with Target Rx New Rx New Rx Samples Details Rx = f(S,S2,D,D2,D0.33) Linear Regression: Model 2 • Linear Regression Model 2 Results • Correlation ( Predicted Rx, Actual Rx ) = 0.9997; mean abs error = 0.178 • Prediction fit of samples improves, but now the wrong optimal point • Prediction for details has big problem with low details • With linear regression, many models are built with hope of finding a good one

12. Comparing Rx from Linear Regression with Target Rx Comparing Rx from Linear Regression with Target Rx Comparing Rx from Kernel Method with Target Rx New Rx New Rx New Rx New Rx Samples Samples Details Details Rx = f(S,S2,D,D2,S*D,D0.33) Kernel Methods: Model 1 • Kernel Methods model • Correlation ( Predicted Rx, Actual Rx ) = 0.9998; mean abs error = 0.062 • Prediction of response to samples and details close to actual data • This model was done with no tuning and no transformations

13. Comparing Rx from Kernel Method with Target Rx Comparing Rx from Kernel Method with Target Rx New Rx New Rx Samples Details Rx = f(S,S2,D,D2,S*D,D0.33) Kernel Methods: Model 2 • Kernel Methods model 2 – After tuning model parameters • Correlation ( Predicted Rx, Actual Rx ) = 1.0000; mean abs error = 0.003 • Prediction of response to samples and details a bit closer to actual data • Minimal level of tuning

14. Measuring Plan Performance Section 3

15. Q4-2007 Sampling Sensitivity Analysis $2,500,000 $2,000,000 $1,500,000 $1,000,000 $500,000 Likely Adherence $0 Incremental Prescription Sales Optimal Adherence -30% -20% -10% 0% 10% 20% 30% $500,000 $1,000,000 $1,500,000 $2,000,000 $2,500,000 % Change in Sampling Rate Sampling level analysis example • With continuing adherence patterns, new samples will decrease sales • With optimal adherence, substantially more samples could be appropriately added • With modest increases in adherence, increasing number of samples is likely to increase sales

16. Adherence of Sampled HCPs 100% 90% 80% 1%-40% 70% 41%-80% 60% 50% 81%-120% 40% 121%-200% 30% >200% 20% 10% 0% Q1 Q2 Q3 Q4 Adherence by Individual HCP (Under/over-sampling) • In this example, a large but decreasing number of HCPs have > 200% over-sampling

17. Example: Validation model of over-sampling • Loss of revenue related to level of over-sampling • Revenue loss increased faster than over-sampling rates

18. Forgone Revenue in Million $ from Lack of Sampling Adherence $7.0 $6.0 $5.0 $4.0 $ in Millions Over-Sampled Under-Sampled $3.0 $2.0 $1.0 $0.0 Q1 Q2 Q3 Q4 Example: Forecasting model results Zero-sampled HCPs must be detailed to be considered; gains are discounted for group practice and other exclusion factors.

19. Pharmaceutical Sampling Optimization SDSIC Forum on Analytics November 2007