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Marketing Optimization Example Maureen McClatchey, Ph.D. 1/23/20131Denver SAS User's Group presentation.

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Presentation on theme: "Marketing Optimization Example Maureen McClatchey, Ph.D. 1/23/20131Denver SAS User's Group presentation."— Presentation transcript:

1 Marketing Optimization Example Maureen McClatchey, Ph.D. 1/23/20131Denver SAS User's Group presentation

2 “BASEBALL IS 90% MENTAL AND THE OTHER HALF IS PHYSICAL” Quote from Yogi Berra 1/23/2013Denver SAS User's Group presentation2

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. 1/23/20133 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 1/23/20134 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 1/23/20135 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 1/23/2013 Denver SAS User's Group presentation 6

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) 1/23/2013 Denver SAS User's Group presentation 7

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 1/23/2013 Denver SAS User's Group presentation 8

9 Campaign Table Campaign_CdCampaign_Desc Camp_1Retention campaign 1 Camp_2Retention campaign 2 1/23/2013 Denver SAS User's Group presentation 9

10 Communication Table Campaign_CdCommunication_CdAvg_Exp_ValAvg_Prob Camp_1Comm_111 Camp_1Comm_211 Camp_2Comm_311 Camp_2Comm_411 1/23/2013 Denver SAS User's Group presentation 10

11 Control Table Campaign_CdCommunication_ Cd Column_NmNumeric_Measure Camp_1Comm_1Prob_Attrition_Reason1Prob Camp_1Comm_2Prob_Attrition_Reason2Prob Camp_2Comm_3Prob_Attrition_Reason3Prob Camp_2Comm_4Prob_Attrition_Reason4Prob Camp_1Comm_1Exp_Val_AttritionExp_Val Camp_1Comm_2Exp_Val_AttritionExp_Val Camp_2Comm_3Exp_Val_AttritionExp_Val Camp_2Comm_4Exp_Val_AttritionExp_Val 1/23/2013 Denver SAS User's Group presentation 11

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 1/23/2013 Denver SAS User's Group presentation 12

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 1/23/2013 Denver SAS User's Group presentation 13

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 1/23/2013 Denver SAS User's Group presentation 14

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! 1/23/2013Denver SAS User's Group presentation15

16 Wish list Build in an ‘after-the-fact’ evaluation component. – What worked? – What did not work? – Quality improve the system – Repeat recursively 1/23/ Denver SAS User's Group presentation


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