1) The document describes using data mining techniques to build predictive models to improve the marketing efficiency of a home equity product by estimating customer response likelihoods. 2) It analyzes previous marketing campaign response data and customer credit variables to build Logit, Decision Tree, and Neural Network models, with the Logit forward model achieving the highest 67% lift at the 30% budget level. 3) The results show an extreme bimodal distribution separating responsive from non-responsive customers, and adverse selection with less responsive customers having lower FICO scores. Targeting deciles 5-7 balances response and credit risk.

1. Data Mining with Enterprise Miner Improving Marketing Effectiveness for Home Equity ProductUC Berkeley Extension

2. Business DetailMarketing DataBusiness Objective: Improve marketing efficiency by estimating the likelihood of response for a marketing campaign promoting the home equity product Marketing Definition (Policy K.O.): Target potential customers with FICO exceeding 680Marketing Budget: Constrained to cover 30% of overall marketData Source: Response data from previous untargeted trial marketing campaign results

3. Analysis VariablesMarketing DataCredit InquiriesDerogatory AccountsCollection AccountsLiensHome EquityMortgageBank CardsInstallment LoansLeasesRetail TradesRevolvingFICOMDSResponse Flag (response variable)

4. Tech SpecsMarketing DataResponse Variable: binary (1=Y/0=N)The rare (target) event provides the probability that y = 1 given a particular set of values for x1, x2, . . . , xp.E(y)= estimate P(y=1, x1, x2, . . . , xp)Sampling: Target event over-sampled in order to improve statistical learning (28% - YES vs. 72% - NO)Data Partition: Train, Validation, Test (40%, 30%, 30%) respectivelyTransformations: FICO and MDS in order to maximize normality

5. Tech SpecsMarketing DataReplacement (Missing): FICO and MDS data is comprised of 28% missing values; imputation method is to populate with medianVariable Selection: utilized R2 criteria selection as first pass variable selection for Logit and Neural Network modelsModels:Logit: Full, Forward, Backwards, Stepwise, Interaction StepwiseDecision Tree: CHAID, CART, Entropy (C4.5)Neural Network: RBF, MLP2-2, MLP3-3Prior Probability: readjusted oversampling by applying a 1% (response) prior probability to the target event

6. Model ArchitectureMarketing Data

7. LIFTMarketing DataLogit ForwardLogit forward model provides the largest lift percentage (nearly 67%) at 30% market coverage, which equals the budget constraint

8. Targeted Response HistogramMarketing DataThe probability distribution of the target response in very telling. The left-side of the distribution identifies the majority of the market has a low probability of responding. Campaign focus should be geared to the left.

9. Adverse SelectionMarketing DataA target’s willingness to respond is negatively correlated to his/her FICO score.

10. ResultsMarketing DataBest Predictive Model: Logit Forward (67% lift at 30% target market)Extreme bimodal distribution separates responsive targets from non responsiveAs expected adverse selection exist. Target deciles 5,6 and 7 to marry marketing response expectations with credit risk guidance.