Customer Profitability Customer Lifetime Value (CLV), Customer Equity (CE), and Shareholder Value

Transcription

1 BA 597: Customer Profitability Customer Lifetime Value (CLV), Customer Equity (CE), and Shareholder Value 1

2 Today s Agenda To discuss how to calculate customer lifetime value (CLV) To show how it is calculated using actual data To discuss the Tuscan Lifestyles case results 2

3 Selection Metrics Is CLV better? Using 54 months of data to predict the next 18 months (for the top 15% of customers) CLV RFM PCV Avg Revenue 30,427 21,201 21,929 Gross Value 9,184 6,360 6,579 Variable Costs Net Value 9,077 6,260 6,484 3

4 Customer Lifetime Value Definition: The sum of calculated cash flows discounted using the weighted average cost of capital (WACC) of a customer over his or her entire lifetime with the company. 4

5 Customer Lifetime Value 5

6 Customer Lifetime Value Computational and Substantive Issues: How many periods to compute? (t n) How to predict gross contribution margin in the future? (GC it ) How to determine future marketing spending? (Mktg it ) Is there anything missing from the CLV model? 6

7 Customer Lifetime Value 7

8 Customer Lifetime Value Aggregate Approach: Suppose we have a cohort of 100 customers for a bank. We observe 3 months of purchases starting in January We have the following data: Projected average gross contribution = $500 per year Average marketing cost = $45/year Average acquisition cost = $60 Discount rate = 15% per year Retention rate = 75% per year 8

9 Customer Lifetime Value 9

10 Customer Lifetime Value 10

11 Customer Lifetime Value Individual-Level Approach: What s different here than in the aggregate approach? Dealing with retention of an individual Predictions for individuals rather than averages 11

12 Customer Lifetime Value Dependent Variables of Interest: GC it [usually P(Buy it )*E(GC it Buy it =1)] Mktg it [Should we predict it?] T i or P(Alive it )[Depends on contractual nature] What are the drivers of each of the three models? 12

13 Customer Lifetime Value P(Alive it ) using Excel (BG/NBD) Required Variables x t x T The number of transactions by a given customer over all time periods. Here we assume that it is the sum of the variable Purchase where customers at most made 1 purchase per quarter. This is the time of the last transaction, i.e. the last quarter where Purchase = 1. The total time between the first purchase and the end of the observation window, i.e. 12 quarters for all customers. 13

14 Customer Lifetime Value Typical CLV Drivers: Exchange Characteristics Customer Characteristics Product Characteristics Firm s Marketing Actions 14

15 Customer Lifetime Value Model Formulation/Data Requirements: For prediction you need time dynamics (i.e. DV is in time = t and IVs are in time = t x) For example: GC it = f (GC i,t-1, ) You need sufficient history to make better future predictions You need to select the right model for the DV type (e.g. is the variable continuous, discrete, etc.) 15

16 Customer Equity Conceptually, what is it? All the discounted profit from current customers All the discounted profit from customers acquired in the future 16

17 Customer Equity 17

18 Customer Equity We know some different techniques for computing the CLV of current customers. What about future customers? Computational Issues: How many new customers should we expect? Does risk matter? 18

19 Computing CLV What is the average value of a retained customer? CLV retained $ M * r T t t t 1 (1 d) M = (GC - Mktg) d = (discount rate) r = (retention rate) Turns out we can convert the infinite sum into: CLV retained $ M * (1 d) (1 d r) 19

20 Computing New CLV What is the average value of a new customer? CLV new T $ M * GR t t 1 (1 d) t AcqCost M = (GC - Mktg) d = (discount rate) GR = (growth rate) What about all new customers? T new t CLVnew (1 d) CLV * t new 1 (1 d) (1 d GrowthRate) 20

21 Is CE related to MC? What can we do? Run a regression! MC CE t 0 1 t t What do you expect (hope) to find? 21

22 CE to Shareholder Value Is there a link between customer equity and shareholder value? ** From Kumar and Shah (2009) ** 22

23 CE to Shareholder Value How can we leverage CRM strategies to increase stock price? ** From Kumar and Shah (2009) ** 23

24 CE to Shareholder Value What is the timeline for the analysis? ** From Kumar and Shah (2009) ** 24

25 CE to Shareholder Value What happened pre- and poststrategy implementation? ** From Kumar and Shah (2009) ** 25

26 CE to Shareholder Value How did this firm compare to the general market performance during the same time period? ** From Kumar and Shah (2009) ** 26

27 Innovations in Maximizing CE Customer Equity refers to the entire base of current and future customers from a firm Does this sound like a problem from finance? We have different cash flows from different customers over time We have to choose which customers to invest in at each time period 27

28 Portfolio Theory (Finance) What we know from Finance: Markowitz (1952): Investors should not try to maximize expected returns. Instead, investors should consider maximizing expected return (desirable) and minimizing variance of return (undesirable) And, we cannot assume that the law of large numbers will diversify away our risk completely 28

29 Portfolio Theory (Finance) 29

30 Finance to Marketing We can treat customers as risky assets to the firm. But, we cannot directly transfer portfolio theory from finance to marketing for the following reasons: ROI vs. ROMI Customer Value and Security Value Role of Unexpected Information Risk 30

31 Risk (Volatility of CLV) 31

32 Risk (Vulnerability of CLV) 32

33 Tuscan Lifestyles Questions: What is the average CLV (after 5 years) of a customer whose initial purchase is less than $50? What is the average CLV (after 5 years) of a customer whose initial purchase is $50 or greater? 33

34 Tuscan Lifestyles Other Questions: Based on these CLVs, what marketing plans might be advisable? Do you agree with Joan s assumption that 5 years is a reasonable time horizon? Do you have suggestions for other ways to group customers for determining lifetime values? What additional information might be helpful for Joan? 34

35 For Next Class Topic: Data Mining, Decision Trees, and Data Analysis Designing a Loyalty Program Reading: Applied Logistic Regression Assessing a Model s Performance: Lifts, Gains, and Profits The Mismanagement of Customer Loyalty 35