Predicting Mail-Order Repeat Buying: Which Variables Matter?

1. Predicting Mail-Order Repeat Buying: Which Variables Matter? Group2 王祥義 謝宜君

2. Agenda • Abstract • Introduction • Research Questions • RFM Variables • Non-RFM Variables • Methodology • Data • Empirical Findings • Conclusion

3. Abstract • Customer-oriented conceptual model of segmentation variables for mail-order repeat buying behavior. • 1) from a theoretical perspective what customer-related variables should be included in response models . 2) empirically validate how these variables perform for predictive purpose. • Traditionally- Three variables Which variables can additional?

4. Introduction • The success of a database-driven (mail-order) marketing campaign mainly depends on the customer list to which it is targeted. • Response modeling for database marketing is concerned with the task of modeling the customers’ purchasing behavior.

5. 1.Direct-Mail Patronage Behavior • A Conceptual Model of Segmentation Variables Independent variable Dependent variable Within a fixed time interval

6. Overview of variables Behavioral variables usually correlate more strongly with future purchase behavior . Non-company specific variables generally have to be purchased form external vendor.

7. 2. Research Questions • This study focuses on the issue of what variables to include in predicting repeat purchase behavior by mail-order. • RQ1a & RQ1b focus in the traditionally RFM variables. • RQ2 address the issue of including other predictors into response model.

8. RQ1a • Address the issue of “how good a model performance can be achieved by RFM variables.” RQ1a What is the total performance of the combined use of the three RFM variables in predicting repurchase behavior?

9. RQ1b • The relative importance of three components has never been thoroughly investigated. • “Frequency” is the most important. RQ1b What is the relative importance of recency, frequency and monetary value predicting repurchase behavior ?

10. RQ2 • Several variables have been added to RFM variables in specific implementations, but have never been systematically investigated. RQ2 How much predictive power do additional, i.e non-RFM, Variables offer in modeling mail-order repeat purchasing?

11. RFM variables • Recency Recencyhas been found to be inversely related to the probability of the next purchase • Frequency Frequency is that heavier buyers show greater loyalty as measured by their repurchase probabilities • Monetary The volume of purchases a consumer makes with a particular mail-order company is a measure of usage which has been an important behavioral segmentation variable in several studies

12. Company & Behavioral Length of the relationship Type/category of product Source of the customer Customer/company interaction Non-RFM variables 1) company specific or not 2) behavioral or non-behavioral

13. Company & Behavioral • Length of relationship • Social psychology/Economics/OB • The duration of a relationship may have predictive power with regard to the continuation of the relationship. 2) Type/Category of Product  Kestnbaum suggests to replace RFM by the new acronym FRAC ( amount, category of product)

14. Company & Behavioral 3) Source of the Customer - Member introduces member - Child from a member parent - Internal mailing lists - Rented mailing lists - Spontaneous requests • Customer/Company Interaction Contact-information includes several different types: (1) Information inquiries (2) Orders (purchasing) (3) Complaints (post-purchase). Higher probability of repurchase Complaint management is a key element.

15. Company & Non-behavioral Customer Satisfaction • When applied to direct marketing, we can state that the probability of repeat behavior will increase if the total buying experience meets or exceeds the expectations of the consumer with respect to the performance. • Purchasing behavior was positively reinforced by tracking customer satisfaction.

16. Non-company & Behavioral General Mail-Order buying behavioral • when the person only recently became a customer at a particular mail-order company, knowledge about the customer’s general mail-order buying behavior may be valuable in predicting future purchasing behavior.

17. Non-company & Non-behavioral • Benefit segmentation - The benefit people seek in products are the basic reasons for heterogeneity in their choice behavior. Therefore, benefit are relevant bases for segmentation. - Other studies have shown that benefit segments are identifiable and substantial, and differ in brand purchase behavior. - Convenience, Credit line • Socio-Demographic -Background ex. age education occupation salary

18. Methodology * In order to address RQ1a, RQ1b, RQ2 • Specific modeling technique for purchase incidence modeling • Model structure & the level of parameterization • Evaluation Criteria → to assess “improvement” in predictive accuracy • Procedure for variable introduction

19. Methodology * Purchasing or not is a binary decision problem (two class classification) The Binary Logit Model is used to approximate a probability Whereby: Pi represents the a posteriori probability of a repeat purchase for customer i Xij represents independent variable j for customer i bj represent the parameters (to be estimated) n represents the number of independent variables (二類評定模型) 0 ~ 1

20. Methodology Evaluation Criteria • Percentage correctly classified (accuracy) at the ‘economically optimal’ cutoff purchase probability (PCC) • Area under the receiver operating characteristic curve (AUC) RankingLikelihood buyer A most likely . . . . . . buyer N latest likely Buyer ←cutoff value Non-buyer * Classification :

21. Methodology 分類正確率 預測Buyer正確率 正確 錯誤 預測 Non-buyer正確率 (錯差矩陣) 正確率 靈敏度 明確性

22. Methodology Cutoff value = Minimal probability of purchase (門檻值、臨界值) ie. 郵寄成本、目錄製作成本 $ 5 $10 • the objective is to maximize total profits, the optimal decision rule is to mail up until the point where the incremental revenue derived from the mailing equals the incremental cost incurred by sending this additional mailing. • Disadvantage : Estimated Value for cost & revenue • Heterogeneity (異質性) in average

23. Methodology ROC (Receiver Operating Characteristic) Curve (收受者操作特性曲線) 1.0 True positive Rate (Sensitivity) = Accuracy 越大表示越佳 AUC (hit percentage) 0.0 0.0 1.0 False positive Rate (1-Specificity) (false-alarm probability)

24. Data Internal data from mail-order company Questionnaire data from households • Past purchase behavior • when purchase • what quantity • which product • what price • Benefit segmentation • variable • Customer satisfaction • General mail-order • purchasing Database marketing data warehouse for response modeling Figure 2: Summary of data sources

25. Empirical Findings RQ1a: performance of RFM in predicting 1.0 AUC True positive Rate (Sensitivity) null model AUC performance AUC = 0.5 null model 0.0 0.0 1.0 False positive Rate (1-Specificity) room for improvement perfect model 1.0 perfect model AUC = 1.0 PCC 0.0 0.0 PCC performance 1.0

26. Empirical Findings RQ1b: relative importance of RFM in predicting (相對的重要性) <Single Predictor> (most important) <Multiple Predictors> (accuracy) Not sensitive as F is include → F (1st) ; M (2nd) ; R (3rd)

27. Empirical Findings • RQ2: How much predictive power do additional from non-RFM • Financial Convenience : credit usage • Length of relationship : log (number of days) • General mail-order buying behavior : frequency

28. Empirical Findings Cumulative AUC performance of predictor models Gen. Credit AUC on Test Sample 0.769 (+53.8%) Length. Monetary value all variables differ < 0.10 0.754 (+50.8%) Recency Frequency Number of Variables in Response Model

29. Conclusion • The importance of RFM • More variables = efficiency • Cutoff value is important • Different industry may choose different variables \

30. THANK YOU !