Group 3: Simon Tier Jack Cindy Lily Hector Predicting Mail-Order Repeat Buying: Which Variables Matter? Predicting Mail-Order Repeat Buying: Which Variables Matter?

 Abstract  Introduction  Research Questions  RFM Variables  Non-RFM Variables  Methodology  Data  Empirical Findings  Conclusion Outline

Abstract  Major propose  By customer-oriented conceptual model of segmentation variables for mail-order repeat buying behavior.  Traditionally- Three variables Which variables can additional?

Introduction(1/3)  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.

Introduction(2/3) A Conceptual Model of Segmentation Variables Independent variable Dependent variable

Introduction(3/3) BehavioralNon-Behavioral Company specific Recency Frequency Monetary value Length of relationship Type/category of product Source of customer Customer/company interaction Customer satisfaction Non-company specific General mail-order buying behavior Benefit segmentation Socio-demographics

Research Questions 3 questions What is the total performance of the combined use of the three RFM variables in predicting repurchase behavior? RQ1a What is the relative importance of recency, frequency and monetary value predicting repurchase behavior ? RQ1b RQ2 How much predictive power do additional, i.e non-RFM, Variables offer in modeling mail-order repeat purchasing?

RFM Variables  Recency Recency has been found to be inversely related to the probability of the next purchase (Cullinan, 1977;Shepard, 1995)  Frequency Frequency is that heavier buyers show greater loyalty as measured by their repurchase probabilities (Morrison, 1966; Lawrence, 1980)  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 (Kotler, 1994)

Non-RFM Variables(1/7) Company & Behavioral Length of the relationship Type/category of product Source of the customer Customer/company interaction

Non-RFM Variables(2/7) 1. Social psychology 2. Economics investigate 3. Organizational behavioral Company & Behavioral Length of the relationship Simpson (1987) states that ‘Relationship duration also ought to prognosticate relationship stability

Non-RFM Variables(3/7) Company & Behavioral Type/category of product Source of the customer 1.Member introduces member 2.Child from a member parent 3.Spontaneous requests 4.Rented mailing lists 5.Internal mailing lists Kestnbaum (1992) suggests to replace RFM by the new acronym FRAC (category of product)

Non-RFM Variables(4/7) Company & Behavioral Customer/company interaction Contact-information consists of several different types: (1) Information inquiries (2) Orders (purchasing) (3) Complaints (post-purchase).

Non-RFM Variables(5/7) 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.

Non-RFM Variables(6/7) 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.

Non-RFM Variables(7/7) Non-Company & Non-Behavioral Benefit segmentation Socio-Demographic Background ex. age education occupation salary Convenience is often cited as one of the major driving forces for direct marketing patronage behavior (Gehrt et al., 1996). Credit line (provided by the company or by credit cards) does facilitate spending and also increases the amounts being spent. (Feinberg 1986)

Methodology(1/4) 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.

This section introduces and justifies the choice of two performance criteria:  Percentage correctly classified (accuracy) at the ‘economically optimal’ cutoff purchase probability (PCC)  Area under the receiver operating characteristic curve (AUC). Evaluation Criteria Methodology(2/4)

Methodology(3/4)

When the objective is to maximize total profits, we know from microeconomics that 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 Methodology(4/4) Cutoff value = Minimal probability of purchase

Data Internal data from mail-order company Questionnaire data from households Database marketing data warehouse for response modeling Figure 2: Summary of data sources 1.Benefit segment variable 2.Customer satisfaction 3.General mail-order purchasing 1. Past purchase 2. behavior

AUC PCC AUC performance PCC performance Empirical Findings(1/4)

AUCPCC Recency Frequency Monetary value Num. of var.R, F, or MAUC 1F F & M R, F,& M0.754 Relative important of RFM value in predicting

Num. of var. List of var.AUC 4Best RFM & Credit Best RFM, Credit, & Length Best RFM, Credit, Length. & Gen Multiple predictors Empirical Findings(2/4)

Number of Variables in Response Model AUC on Test Sample Frequency Monetary value Recency Credit Length. Gen. Cumulative AUC performance of predictor models Empirical Findings(3/4)

 The importance of Frequency  More variables = efficiency  Cutoff value is important  Different industry may choose different variables Conclusion