Marketing Optimization Example

1. Marketing Optimization Example Maureen McClatchey, Ph.D. mmcclatchey@q.com Denver SAS User's Group presentation

2. Quote from Yogi Berra “Baseball is 90% mental and the other half is physical” Denver SAS User's Group presentation

3. What is marketing optimization? • Optimization enables us to determine • the optimal set of customers to target in a marketing campaign and • the optimal communications (offer type) to use for each customer. Denver SAS User's Group presentation

4. Marketing Optimization enables us to • 1) determine the optimal set of customers to target in a marketing campaign • 2) and the optimal communications to use for each customer. • 3) You can choose the objective to be optimized. For example • Maximize expected revenue or profit • Minimize expected cost of campaign • Maximize total number of expected responses Denver SAS User's Group presentation

5. Marketing Optimization Example: • Business question design tailors predictive models • Models applied to customers calling in to Telecommunications Call Centers • Customers asked for permission to use their proprietary information as part of the call before marketing begins. • Partnership and collaboration among marketers, IT and statisticians Denver SAS User's Group presentation

6. Goal of Marketing Optimization • The goal is to obtain an assignment of each customer to an offer type that optimizes the objective • e.g., maximize expected profit • At the same time satisfy various marketing constraints • e.g., budget constraints, # offers restrictions, channel capacities, contact policy restrictions Denver SAS User's Group presentation

7. Marketing Optimization Input Tables • Input tables to eliminate ineligible assignments of customers to offer types • Customer table • Customer table variables: • Identification number, • Location, • Probability( Attrition), • Revenue, • Expected Value(Attrition) = Probability(Attrition)*NPV, • Automatic payment for services, • Product subscriber, • Credit rating, • Demographic cluster values • (macro or micro) Denver SAS User's Group presentation

8. How Does Lifetime Value Fit In? • Calculate Lifetime Value (LTV) • Create a rule so that customers with the highest expected value of retention also have the highest LTV • Optimize the objective Denver SAS User's Group presentation

9. Campaign Table Denver SAS User's Group presentation

10. Communication Table Denver SAS User's Group presentation

11. Control Table Denver SAS User's Group presentation

12. Additions to MO • Constraints • Minimum Responses • Contact Policies • Attrition probabilities in the customer table need to be calibrated to recent behavior. • Can be handled with a multiplier in a look-up table Denver SAS User's Group presentation

13. Additions to MO (cont’d) • Create a project • Create a scenario • Calculate the objective • Maximize adjusted profit • Expected value = probability(retention)*net present value Denver SAS User's Group presentation

14. Additions to MO (cont’d) • Enter constraints and contact policies • Idea: Use a sequential algorithm at first. Then use the sequential algorithm to create a customer table in SAS. Compare results of sequential algorithm to results using Marketing Optimization. • Optimize a scenario • Results: Optimal offer for each customer Denver SAS User's Group presentation

15. Think about optimization • What are we optimizing? • Please carefully consider. Is there no harm? • What are the benefits of optimization in your biomedical research/pharmaceutical/business setting? • Think about what you are doing! • Slow down a bit and reflect • Ask yourself, “What are the pros?” “What are the cons?” and most importantly, “What are the probable consequences from this work?” • Then do the ethical thing! Denver SAS User's Group presentation

16. Wish list • Build in an ‘after-the-fact’ evaluation component. • What worked? • What did not work? • Quality improve the system • Repeat recursively Denver SAS User's Group presentation