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 proposeBy 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 variableIndependent variable

Dependent variableDependent variable

Introduction(3/3)

Behavioral Non-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 questions3 questions

What is the total performance of the combined use of the three RFM variables in predicting repurchase behavior?

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 ?

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?

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 member2. Child from a member parent3. Spontaneous requests4. Rented mailing lists5. Internal mailing lists

Kestnbaum (1992) suggests to replace RFM bythe new acronym FRAC (category of product)

Non-RFM Variables(4/7)

Company & Behavioral

Customer/company interaction

Contact-information consists of several differenttypes: (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

Internal data from mail-order company

Questionnaire datafrom households

Questionnaire datafrom households

Database marketing data warehouse

for response modeling

Database marketing data warehouse

for response modeling

Figure 2: Summary of data sources

1.Benefit segment variable

2.Customer satisfaction3.General mail-order

purchasing

1. Past purchase 2. behavior

%5858.0)5.01(

)5.0754.0(

%5.25255.0)529.01(

)529.0649.0(

AUC

PCC

AUC performance

PCC performance

Empirical Findings(1/4)

Empirical Findings(1/4)

AUC PCC

Recency 0.625 0.417

Frequency 0.743 0.678

Monetary value 0.708 0.592

Num. of var. R, F, or M AUC

1 F 0.743

2 F & M 0.753

3 R, F ,& M 0.754

Relative important of RFM value in predicting

Num. of var.

List of var. AUC

4 Best RFM & Credit 0.743

5Best RFM, Credit, &

Length.0.753

6Best RFM, Credit,

Length. & Gen.0.754

Multiple predictors

Empirical Findings(2/4)

0.73

0.735

0.74

0.745

0.75

0.755

0.76

0.765

0.77

0.775

1 2 3 4 5 6

Number of Variables in Response Model

AU

C o

n T

est

Sam

ple

Frequency

Monetary value

Recency

CreditLength.

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