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Value for Customers Valuing Customers

Value for Customers Valuing Customers. Measuring value for customers Measuring value of customers (CLV) Targeting based on customer value. 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.

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Value for Customers Valuing Customers

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  1. Value for Customers Valuing Customers • Measuring value for customers • Measuring value of customers (CLV) • Targeting based on customer value

  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 Buying Process Behaviors Ignore Present State Postpone Perceived and Psychological Needs Functional and Economic Needs Engage in Purchase Process • Search for options • Evaluate options • Choose option • Purchase Option • Use Option Desired State · Motivation Customer Value Measurement Approaches Perceptual Measures of Value Objective Measures of Value Behavioral Measures of Value Customer Needs andCustomer Value Measurement

  4. Definition of ObjectiveCustomer Value The hypothetical price for a supplier’s offeringat which a particular customer would be at overall economic break-evenrelative to the best alternative availableto that customer for performing a set of functions. This approach to measuring customer value is typically useful in B2B situations.

  5. Customer Value Potential Value Lost Value Distributed or Economic Driving Force Perceived Value Value Created Price Margin Value Added Total Cost Cost of Goods and Services Cost, Price, and Customer Value

  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. High Carbon (HC) Steel in stamped automotive part…  Long Coil  “Riverside” plant “Partsco” Application  Etc... HC Steel - 2 lbs/part @ 60¢ lb. - 25% scrap rate - 80¢ Machine time Rapid Form - 2lbs @ ??¢ 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…

  9. RF: (2 x VRF) + .70 (1 - .05) = 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…” HC: (2 x .60) + .80 (1 - .25) .92¢/lb = VRF

  10. Customer Value Perceived Value Total Cost External Purchases Costs, Prices and Values $0.92 Probably could have priced higher $0.68

  11. Potential Value Lost Value Created Margin Value Added Cost, Price, and Customer Value Customer Value (92¢) Value Distributed or Economic Driving Force Perceived Value Price Total Cost Cost of Goods and Services

  12. Perceived ValueChoosing a Value Assessment Method * 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.

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

  16. Choice Models VersusNeeds Surveys With standard survey methods . . . preference/ importance choice ï weights ´perceptionsñññ predict observe/ask observe/ask But with choice models . . . importance choice ï weights ´ perceptionsñññ observe infer observe/ask

  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. Score each customer/prospect based on the drivers identified in Step 2 - the higher the score, the more likely is the predicted response. Step 3 Step 4 Classify customers into deciles (or smaller groupings) based on their scores. Step 5 Based on profitability analyses, determine the top deciles to which a marketing action (e.g., mailing of brochure) will be targeted.

  18. 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. 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. Step 2 ExampleRFM Model Scoring Criteria Implement using Nested If statements in Excel

  21. 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. 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. 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. 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. Brand Performance Quality Variety Value A 0.7 0.5 0.7 0.7 B 0.3 0.4 0.2 0. C 0.6 0.8 0.7 0.4 D (new) 0.6 0.4 0.8 0.5 Estimated ImportanceWeight 2.0 1.7 1.3 2.2 Step 2 Example: Choosing Among Three Brands

  26. Step 2 Example Computations (a) (b) (c) (d) (e) Sum of Share ShareBrand weight  estimate Draw value (Aij) without with (c)–(d) new brand new brand A 4.70 109.9 0.512 0.407 0.105 B 3.30 27.1 0.126 0.100 0.026 C 4.35 77.5 0.362 0.287 0.075 D 4.02 55.7 0.206 (c) computed, for example, as

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

  28. 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.

  29. 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% $31.00 0.70 6.51 2 2% $143.00 0.60 1.72 3 10% $54.00 0.67 3.62 4 5% $88.00 0.62 2.73 5 60% $20.00 0.58 6.96 6 22% $60.00 0.47 6.20 7 11% $77.00 0.38 3.22 8 13% $39.00 0.66 3.35 9 1% $184.00 0.56 1.03 10 4% $72.00 0.65 1.87 Average expected purchase per customer = $3.72

  30. Step 4 Decile Classification Customer 1 Score 1.00 Customer 2 Score 0.99 …. Customer 230 Score 0.92 Customer 2300 Score 0.00 • 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 Decile1 ….. ….. Decile10

  31. Step 4 Decile Classification Example Decile Customer(s) $ 1 5 6.96 2 1 6.51 3 6 6.20 4 3 3.62 5 8 3.35 6 7 3.22 7 4 2.73 8 10 1.87 9 2 1.72 10 9 1.03 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)?

  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. Step 5 Develop Lift Charts and ChooseModel for Implementation

  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

  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 # 2577-3274-3 Loc # 33-47-2178 Join Date - 4/6/95 Mary L. Smith Cust # 2577-3274-3 Loc # 33-47-2178 Join Date - 4/6/95 Age 35 – 44 Occupation – Professional Income - $50k – 75k Education – College grad Mary L. Smith Cust # 2577-3274-3 Loc # 33-47-2178 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 # 2577-3274-3 Loc # 33-47-2178 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

  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.

  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 base profit acquisition cost Time

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

  40. (Expected) Revenue Cash Flow (minus) (Expected) Cost to Serve Cash Flow Loyalty Expected Profit Cash Flow Risk Adjustment Risk Adjusted Cash Flow Economic Lifetime Value Calculation  Lowers  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)

  42. Objectives for CLV-BasedManagement • 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-BasedCustomer Portfolio Analysis High Relationship Value 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?

  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% 15% credit card attrition 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 1 2 3 4 5 Credit Card Issuance & Servicing (21)* 42 44 49 55 Industrial Laundry 144 166 192 222 256 Industrial Distribution 45 99 121 141 168 Auto Servicing 25 35 70 88 88 *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.

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