Customer Analytics Part II

Abstract

Customer value management rests on the idea of allocating resources differently according to the characteristics of different customers. The basis of this differential resource allocation is the economic value of the respective customer to the firm. Thus, before one can start to manage customers, one must have developed a thorough understanding of how to compute the value contribution each customer makes to a firm. Various economic concepts and procedures have been developed that help us to achieve this. Chapter 5 proceeds to conceptualize strategic metrics of customer value and introduces popular customer selection strategies and techniques to evaluate these strategies.

Appendices

Appendix I Notation Key

Notation	Explanation
a	Coefficient of acquisition
AC	Acquisition costs
ACS	Acquisition costs savings
Ar	Acquisition rate
c	Category
CE	Customer equity
Dr	Defection rate
GC	Gross contribution
i	Individual customer
I	Total number of buyers with a focal firm
j	Firm
J	Total number of firms in a market
LTV	Lifetime value
MC	Marketing costs
n	Customer in cohort
N	Cohort size
r	Coefficient of retention
Rr	Retention rate
Rrc	Retention rate ceiling
S	Sales (value)
Sr	Survival rate
t	Time period
T	Length of time horizon
V	Sales (volume)
δ	Applicable discount rate

Appendix II Regression Scoring Models

Scoring models is the process of evaluating potential customer behavior on the basis of test results. Typically, a test is conducted in a limited market or in an experimental set up on a small subset of customers. This subset of customers is exposed to a marketing campaign and a product offering. The purpose of this test is to assign to each of the remaining customers a value which is extrapolated from the results of this test. These values typically reflect the prospective customer’s likelihood of purchasing the test marketed product. The process of regression scoring can be represented in the following steps:

• 1. Draw a random sample from the overall population of prospective customers.

• 2. Obtain data from the sample that profiles individual consumer characteristics. The R, F, and M scores are variables which profile behavioral characteristics of a customer and are typically used in this procedure, along with other relevant variables.

• 3. Initiate a marketing campaign directed at the random sample, and record the individuals who become customers.

• 4. With that information, develop a regression scoring model to obtain a series of weighted variables that either predict which prospects are more likely to become customers or the value of profits that each customer is likely to provide, based on their individual characteristics.

• 5. By applying these weights to individual characteristics of prospective customers, we can arrive at a value for each customer which indicates how likely it is that the customer will purchase a product, or how much profit the customer will generate, if exposed to the tested marketing campaign.

Appendix III BEI Computations for RFM Cells

Cell #	RFM codes	Cost per mail ($)	Net profit per sale ($)	Breakeven (%)	Actual response (%)	Breakeven index
1	111	1	45	2.22	17.55	690
2	112	1	45	2.22	17.45	685
3	113	1	45	2.22	17.35	681
4	114	1	45	2.22	17.25	676
5	115	1	45	2.22	17.15	572
6	121	1	45	2.22	17.05	667
…
52	312	1	45	2.22	12.91	481
53	313	1	45	2.22	0.98	−56
54	314	1	45	2.22	0.94	−58
55	375	1	45	2.22	0.90	−60
56	321	1	45	2.22	0.136	−61
57	322	1	45	2.22	0.82	−63
58	323	1	45	2.22	0.78	−65
…
75	355	1	45	2.22	−0.15	−107
76	411	1	45	2.22	11.25	−107
77	412	1	45	2.22	11.22	406
78	413	1	45	2.22	0.55	405
79	414	1	45	2.22	0.52	−75
80	415	1	45	2.22	0.49	−77
…
100	455	1	45	2.22	−0.11	−105
101	511	1	45	2.22	10.88	390
102	512	1	45	2.22	10.85	388
103	513	1	45	2.22	0.78	−65
104	514	1	45	2.22	0.73	−67
105	515	1	45	2.22	0.70	−69
106	521	1	45	2.22	0.67	−70
…
120	545	1	45	2.22	0.25	−89
121	551	1	45	2.22	0.22	−90
122	552	1	45	2.22	0.19	−91
123	553	1	45	2.22	0.10	−96
124	554	1	45	2.22	0.01	−100
125	555	1	45	2.22	−0.08	−104