1. Value for Customers Valuing Customers
Measuring value for customers
Measuring value of customers (CLV)
Targeting based on customer value
This lecture should form the foundation of any marketing course—it concerns how to understand (and model) what customers value and also to determine how valuable customers are to the firm.

2. Quotes for the Day
Anyone can measure the number of seeds in an apple. Who can measure the number of apples in a seed?
--Anon
Price is an observable description of a state of the market. Customer value is the hidden source of ideas about what to do to make a market more profitable for the business.

3. Customer Needs and Customer Value Measurement
Present
State
Behaviors
Ignore
Postpone
Engage in
Purchase Process
Desired
Functional
and
Economic
Needs
Perceived
Psychological
Search for options
Evaluate options
Choose option
Purchase Option
Use Option
Customer
Value
Measurement
Approaches
Objective
Measures
of Value
Perceptual
Behavioral
Customer Needs and Buying Process
Motivation
There are three sets of ways to infer customer value….no one is the “best” and they can and should complement each other in use

4. Definition of Objective Customer Value
The hypothetical price for a supplier’s offering at which a particular customer would be at overall economic break-even relative to the best alternative available to that customer for performing a set of functions.
This approach to measuring customer value is typically useful in B2B situations.
Offering: the totality of the offering
Economic break-even: The price at which the customer is indifferent between buying our product and not buying it. Realistically, we cannot charge this price because there is no benefit to the customer, or to the intermediaries.
Best alternative: Need to know what best alternative to our offering is for the customer. We need to understand customer how a customer will use a product/service to know what the best alternative is.
In other words, customer value is the hypothetical price that eradicates all the benefits relative to the best alternative, at the price it is available.

5. Cost, Price, and Customer Value
Perceived Value
Price
Total Cost
Cost of Goods and Services
Value Created
Value Distributed or Economic Driving Force
Margin
Potential Value Lost
Value Added
We can now express Customer Value in dollars, and we have an idea of the space between value and cost, which is the value we create.
Customer Value: Can be very high if there is a unique product with unique characteristics or attributes.
Value Distributed -- each transaction creates a little piece of wealth. The customer doesn’t always get to keep that wealth. Sometimes our customer passes it along to his or her customer(s).
Potential Value Lost: If the perceived value deviates widely from the real value, there is potential value lost in the system.
If perceived value is higher than real value, then our advertising and word-of-mouth have created high expectations for the product, and our product needs to deliver on that promise (like iPod when it first came out). In most situations, customer value is higher than the perceived value, and the firm is doing a poor job of determining and communicating the actual value of its products to customers, based on the best alternative the customer has if he/she does not buy our product.
The question becomes: How much of the value created do we keep?

6. An Example…
Steel Manufacturer develops new “RapidForm” steel for a muffler application:
Reduces Scrap
Runs Faster
…Than the “Incumbent” – High Carbon Steel.
What is the value-in-use of RapidForm in this application for this customer?

7. VIU Example: “RapidForm” Steel vs
VIU Example: “RapidForm” Steel vs. High Carbon (HC) Steel in stamped automotive part…
 Long Coil  “Riverside” plant  “Partsco” Application  Etc...
HC Steel ¢ lb.
- 25% scrap rate
- 80¢ Machine time
Rapid Form - ??¢ lb
- 5% scrap rate
- 70¢ Machine time

8. Original RapidForm Pricing
It cost very little more to make RapidForm
And H-C sells for $0.60/lb
Let’s try $0.70/lb
They’ll buy at $0.68/lb
We’ll make a bundle…
This is the most common (unscientific) view of pricing—it ignores real customer value and, as we will see, leaves money on the table.

9. Example Calculations =
“The hypothetical price of an offering for a particular customer in a particular application, that leaves the customer at overall economic breakeven with respect to the next best alternative…”
RF:
(2 x VRF) + .70
( ) =
HC:
(2 x .60) + .80
( )
Note how the VIU calculation removes the production cost from the equation and focuses on value to the customer
.92¢/lb = VRF

10. Costs, Prices and Values
Customer Value
Perceived Value
Total Cost
External Purchases
$0.92
Probably could have priced higher
$0.68
Several implications: (1) Money left on the table; (2) the real role of marketing communications is to align perceived value with actual customer value, permitting a supplier to charge a higher price in the market, but to have that price perceived as fair.

11. Cost, Price, and Customer Value
Potential Value Lost
Margin
Value Created
Value Distributed or Economic Driving Force
Perceived Value
Price
Value Added
Total Cost
At this point the instructor might use the Abcor2000.xls exercise. It illustrates how a VIU calculator works and will force students to realize that many different offers can be equally attractive to a customer—although some are better for the selling firm than others.
Cost of Goods and Services

12. Perceived Value Choosing a Value Assessment Method
Criterion
Objective
Value Based
Behavior Based
Conjoint/ Tradeoff?
Unconstrained
Amount of Customer Info Needed
High
Low
Medium
No. of Customers
Any
Good in Dynamic Markets?
Yes No
Partly*
Past Purchase Data Available?
Not Necessary
Needed
Analysis Time Frame?
Long
Long/Medium
Short
Cost
Very High/Respondent
Insight
Very High
Appropriate for Lead Users?
Predictive of Behavior?
Moderate
There is no best value assessment method—this chart helps show the strengths and weaknesses of each.
* If customers can reliably report how they will behave after change

13. Choice Models
We focus on Behavior-Based Value Assessment (choice models) using the Multinomial Logit Model here next; we cover Conjoint Analysis in chapter 6.
Unconstrained methods are based on customer surveys or secondary data, and are covered in basic marketing research courses.

14. Behavior-Based Customer Value Determined From “Choice Models”
1. Observe choice:
Buy/not buy -- direct marketers Brand bought -- packaged goods
Share of requirements – B2B
2. Capture related characteristics data:
demographics
attitudes/perceptions
market conditions (price, promotion, etc.)
3. Link
1 to 2 via “choice model” – the model predicts customers’ probabilities of purchase and also reveals importance weights of characteristics. 49 49

15. Contexts in Which Choice Models are Appropriate
Binary Choice
Buy or Not Buy
Yes or No
Own or Don’t own
Clinton or Obama
Multinomial Choice
ABB, GE, McGraw-Edison or Westinghouse
Bus, Train, or Plane
Yes, No, Don’t Know
Choices are mutually exclusive. The customer chooses only one of the options at a given choice occasion.

16. Choice Models Versus Needs Surveys
With standard survey methods . . .
preference/ importance choice ï weights ´ perceptions ñ ñ ñ predict observe/ask observe/ask
But with choice models . . .
importance choice ï weights ´ perceptions ñ ñ ñ observe infer observe/ask
Note the conceptual difference between choice models and standard survey methods—importance weights are revealed by customer actions. 50 50

17. Using Choice Models for Customer Targeting
Step 1
Create database of customer responses (choices) based either on
test mailing to a sample of prospects/customers, or historical data
of past customer purchases.
Step 2
Use models such as regression, RFM, and Logit to assess the
impact of independent variables (drivers) of customer response.
Step 3
Score each customer/prospect based on the drivers identified in
Step 2 - the higher the score, the more likely is the predicted
response.
Step 4
Classify customers into deciles (or smaller groupings) based
on their scores.
This process, in one form or another, is used in every customer-targeting procedure.
Step 5
Based on profitability analyses, determine the top deciles to which
a marketing action (e.g., mailing of brochure) will be targeted.

18. Database for BookBinders Book Club Case
Step 1
Database for BookBinders Book Club Case
Predict response to a mailing for the book, Art History of Florence, based on the following variables accumulated in the database and the responses to a test mailing:
Gender
Amount purchased
Months since first purchase
Months since last purchase
Frequency of purchase
Past purchases of art books
Past purchases of children’s books
Past purchases of cook books
Past purchases of DIY books
Past purchases of youth books

19. Drivers of the RFM Model
Step 2
Drivers of the RFM Model
Recency R
Time/purchase occasions since the last purchase
Frequency F
Number of purchase occasions since first purchase
Monetary
Value M
Amount spent since the first purchase
Total RFM Score: R Score + F score + M Score

20. Example RFM Model Scoring Criteria
Step 2
Example RFM Model Scoring Criteria R
Months from last purchase
13-max
10-12
7-9
3-6
0-2
Score
5pts 10 15 20 25 F
Frequency
> 30
21-30
16-20
11-15
0-10 M
Amount purchased
> 400
 100 50 45 30
Variables that represent recency, frequency, and monetary value are the following:
Recency: Months since last purchase (Last_purchase)
Frequency: Total number of purchase (Frequency)
Monetary value: Total money spent on BBBC (Amoun_purchased)
Implement using Nested If statements in Excel

21. Computing Scores Based on Regression
Step 2
Computing Scores Based on Regression
Run regression model to predict probability of purchase:
Probability of Choice (0 or 1) = a0 +a1 x Gender+a2 x Income +…
Note that predicted choice probabilities from the regression model need not necessarily lie between 0 and 1, although most of the probabilities will fall in that range.

22. The Customer Choice (Logit) Model in MEXL
Step 2
The Customer Choice (Logit) Model in MEXL
The primary objective of the model is to predict the probabilities that the individual will choose each of several choice alternatives. The model has the following properties:
The probabilities lie between 0 and 1, and sum to 1.
The model is consistent with the proposition that customers pick the choice alternative that offers them the highest utility on a purchase occasion, but the utility has a random component that varies from one purchase occasion to the next.
The model has the proportional draw property -- each choice alternative draws from other choice alternatives in proportion to their utility.

23. Logit Model of Response to Direct Mail
Step 2
Logit Model of Response to Direct Mail
Probability of function of (past response behavior,
responding to = marketing effort,
direct mail characteristics of
solicitation customers)

24. The Multinomial Logit Model
Step 2
The Multinomial Logit Model
Purchase probability (Product A) =
Utility of A
Sum of Utilities of other alternatives
Where…
Utility(Product A)= (a function of)
a0 +
a1 x Rating of A on attribute 1+
a2 x Rating of A on attribute 2+
+ + + etc.

25. Example: Choosing Among Three Brands
Step 2
Example: Choosing Among Three Brands
Brand Performance Quality Variety Value A B C
D (new)
Estimated
Importance Weight

26. (c) computed, for example, as
Step 2
Example Computations
(a) (b) (c) (d) (e)
Sum of Share Share Brand weight  estimate Draw value (Aij) without with (c)–(d) new brand new brand A B C D
(c) computed, for example, as

27. An Important Implication of the Logit Model
Step 2
An Important Implication of the Logit Model
Marginal Impact of a Marketing
Action
Probability of Choosing an Alternative
0.0
0.5
1.0
Low
High

28. Compute Choice Scores (Probability of Purchase)
Step 3
Compute Choice Scores (Probability of Purchase)
RFM Model: Use computed score as an index of the probability of purchase (i.e., higher the RFM score, the greater the probability of purchase).
Regression:
Logit:
's are weights estimated by the Regression or Logit models.
RFM and Regression models can be implemented in Excel.
Also, all three scoring procedures for “probability of purchase” can
be implemented in Excel.
You may want to skip this slide or do it in Excel, showing the students how to mutliply regression weights times related variable levels and how the “exp( ) function works in Excel.

29. Score Customers for their Potential Profitability (Example)
Step 3
Score Customers for their Potential Profitability (Example)
A B C D Score Average Customer (Purchase Purchase Expected $ Customer Probability) Volume Margin = A ´ B ´ C
1 30% $ % $ % $ % $ % $ % $ % $ % $ % $ % $
Average expected purchase per customer = $3.72

30. Decile Classification
Step 4
Decile Classification
Standard Assessment Method
Apply the results of approach and calculate the “score” of each individual (calibration versus test sample)
Order the customers based on “score” from the highest to the lowest
Divide into deciles
Calculate/graph hit rate and profit
Customer Score 1.00
Customer Score 0.99 ….
Customer Score 0.92
Customer Score 0.00
Decile1
…..
…..
Decile10

31. Decile Classification Example
Step 4
Decile Classification Example
Decile Customer(s) $
The line is often called the BZ or below zero line—the customers below it are unprofitable to target for the firm.
If the marketing cost to reach a customer is $3, at what decile will you will stop your targeting effort? How is this targeting plan different from one based on average purchases of customers ($3.72)? 59 59

32. Step 5
Determine Targeting Plan (Example shows potential profitability of mailing to the top 6 deciles)
Compute profit/ROI for the models based on the number of mailings recommended by each model and compare that to mailing to the entire list (equivalently to a randomly selected list of the same size).

33. Develop Lift Charts and Choose Model for Implementation
Step 5
Develop Lift Charts and Choose Model for Implementation
In the random model, customers are randomly selected from the list for the targeting effort..
For the BBBC case, the maximum profit is achieved when the company sends brochures to top 4 deciles (40%) of the customers.

34. Applying the MNL Model in Customer Targeting
Key idea: Segment on the basis of probability of choice—
1. Loyal to us
2. Loyal to competitor
3. Switchables: losable/winnable customers
This is the major idea driving the ABB Electric case 57 57

35. Database Approach to Targeting
Appended Data
RFM & Lifetime Value
Other data
(e.g., demographics)
Model Scores
Operational Database
Marketing Database
Sales, Shipments, Payments
Marketing Communications
Promotions & Responses
Surveys & Preferences
Transactions
General Ledger
Source: Arthur Hughes

36. Mary L. Smith
Cust #
Loc #
Join Date - 4/6/95
Mary L. Smith
Cust #
Loc #
Join Date - 4/6/95
Age 35 – 44
Occupation – Professional
Income - $50k – 75k
Education – College grad
Mary L. Smith
Cust #
Loc #
Join Date - 4/6/95
Age 35 – 44
Occupation – Professional
Income - $50k – 75k
Education – College grad
Number in HH – 4
Uses PC & Internet
Purchase data
Mary L. Smith
Cust #
Loc #
Join Date - 4/6/95
Age 35 – 44
Occupation – Professional
Income - $50k – 75k
Education – College grad
Number in HH – 4
Uses PC & Internet
Detailed transactions data
Multichannel access data
CLV
We get a better picture of a customer as we accumulate more information about that customer. Ideally, the information collected should include demographics, behavior, and psychographics,

37. The Downside of Behavior-Based Targeting
By following the behavior-based targeting approach over a long-period of time, a firm may systematically eliminate potentially valuable customers, who may not deliver high economic value in the short term, but may offer substantial value in the long term. It pays to view customers through more lenses than just economic value.
Use the Rhenania video to illustrate to students the downsides of blindly following a targeting approach based on short-run economic value of a customer. This is particularly compelling after doing the BookBinders case.

38. Customer Lifetime Value (CLV) “present value of a stream of revenue a customer produces”
Focus on long-term relationship, not a single transaction
relationship value
cost savings
price premium
Annual Profit
demand increase
The idea here is that a customer can be viewed as a property with an associated earnings stream—rents come from the property but investments are needed to keep the property up. And the “property” may have values and costs beyond pure economics
base profit
acquisition cost
Time

39. CLV: Customer Lifetime Value
Total Lifetime
Value of
Customer
Economic Value:
(Risk Adjusted) Revenue
Flow Less Cost-to-Serve
Relationship Value:
Reference
Referral
Learning
Innovation, etc.
This chart highlights two aspects of customer value.

40. Economic Lifetime Value Calculation
(Expected) Cost to Serve Cash Flow
Expected Profit Cash Flow
Risk Adjustment
Risk Adjusted Cash Flow
(minus)
Loyalty
(Expected) Revenue Cash Flow
 Lowers
The key idea here is that a “risky” customer (one with “erratic” earnings, should be viewed as a risky investment is—future earnings are riskier and a higher discount rate is applied. So loyalty lowers cost to serve and also lowers the discount rate of a loyal customer’s stream of revenue to the firm.
 Lowers

41. Customer Relationship Value
Reference Accounts (Give us prestige, high credibility)
Referral Accounts (Give us high-quality leads)
Learning Accounts (Help us refine our offerings/beta testers)
Innovation Accounts (Help us to develop new offerings)
Customers can provide several other kinds of value to a supplier; they might be unprofitable on a direct cash flow basis, but provide other, very valuable characteristics that help support overall market success. This chart illustrates some of those values.

42. Objectives for CLV-Based Management
Increase customer retention (costs/ benefits of customers)
Improve customer selectivity (Who to serve? How to increase CLV?)
Meet competitive imperatives (Drive or be driven?)
Boost cost efficiency (“A”, “B”, “C” customers? Do we know true costs?)

43. CLV-Based Customer Portfolio Analysis
High
Relationship
Value
This approach can be implemented using the GE/McKinsey portfolio model in Marketing Engineering. A good point of discussion is to determine what an appropriate portfolio of customers should look like for a firm.
Low
Low
High
Economic
Value

44. Approaches to Increasing CLV (Implemented via CRM)
Reduce rate of defection
Increase longevity
Enhance share of wallet
Attempt to alter behavior of low-profit customers
Focus more effort on high-profit customers

45. Questions…What is the lifetime value of a….
Walmart customer?
AMEX customer?
Ritz Carlton customer?
Sony customer?
Singapore Airline customer?
An MBA student?
You might adjust these for your environment—questioning students on how they would go about the calculation—sources of data and related benefits associated with the calculation –can be a fruitful class discussion.

46. Credit Card Rewards Programs Have Had a Direct Impact on Lowering Churn
Rewards Cards and Card Attrition
Reward Card Penetration
Industry Attrition Rate
80%
Card Industry Attrition Rates
35%
70%
30%
69%
29%
28%
30%
62%
26%
60%
Reward Card Penetration
56%
24%
25%
50%
50%
45%
21%
20%
40%
% of credit card holders with rewards card
40%
credit card attrition
15%
30%
10%
20% 5%
10% 0% 0%
2000
2001
2002
2003
2004
2005
Source: Celenet Analysis

47. Customer Acquisition, Retention & Lifetime Analysis
Customer Profit Patterns Over Time, Selected Service Industries
Profit per Customer
(in dollars) by Year of Relationship
Industry
Credit Card Issuance & Servicing (21)*
Industrial Laundry
Industrial Distribution
Auto Servicing
Students might dispute these numbers. Instead, ask them to assume they are correct and ask why that might be so and what the implications are for customer portfolio management.
*Figures in parenthesis denote losses
Source: Based on data from Reicheld and Sasser

48. Summary of Customer Value Assessment
Customer value is hidden, but can be assessed using several different techniques.
A company generates “value from customers” by understanding the value of its offerings to its customers.
Behavior-based targeting can generate incremental short-term profits for a company.
To generate long-term and sustainable profits from customers, a company has to understand and manage Customer Lifetime Value (CLV), which includes both the economic value and the relationship value associated with a customer.