1. University of Washington MBA Program Managing Customer Relationships through Direct Marketing ” “Financials and Budgeting” Instructor: Elizabeth Stearns

2. Simplified Example: $20 Order Average order, gross$22.00 Less returns (10%) $2.00 Average order, net$20.00 Merchandise cost (35%)$7.00 Order processing$1.50 Return processing & loss$1.00 Overhead (12.5%)$2.50$12.00 Contribution to promotion cost & profit $8.00 40% Net Sales 100% ($20.00) Contribution 40% ($8.00) Promotion Cost 25% ($5.00) Profit 15% ($3.00)

3. Break-Even and Profitability: $20 Order Contribution40%$8.00 Promotion cost target25%$5.00 Target profit15%$3.00 Magazine AdSolo MailingCatalog Promotion cost per 1000$16$400$500 Orders per 1000 required to break-even2.0.2% 50 5.0% 62.5 6.25% Net sales per 1000$40$1000$1250 Orders per 1000 required to make target profit and advertising cost 3.2.32% 80 8% 100 10% Net sales per 1000$64$1600$2000 Break-Even = promotion cost per 1000 / $8 25% selling cost target = promotion cost per 1000 / $5 Net sales per 1000 = orders * $20

4. R-F-M Recency –When was the last time they purchased? Frequency –How often do they purchase? Monetary Value –How much money do they spend?

5. Typically used to select likely profitable customers to receive direct marketing treatment It postulates that the most likely prospects are recent purchasers who have historically demonstrated more frequent than average purchase behavior in larger than average dollar amounts It is based on a correlation between RFM and response Historically it has proven to be an effective segmentation technique for many situations The variables RFM are frequently influential in many advanced statistical modeling techniques RFM Definition RFM is a behavioral segmentation technique

6. RFM Definition (cont’d) The variables must be interpreted within the context of product purchase dynamics –Durable –Consumables –Periodic Can be used to manage marketing investment by selecting target customers Can also be used to improve marketing performance by managing message/offer components

7. RFM Elements Segmentation Concept Behavioral Question Data Element/ Measurement RecencyWhen did they last buy? Date of last purchase Process: Sort by date Create groups by date range FrequencyHow often do they buy? # of purchases over time Options: Within recency group, months on file, # times mailed Monetary Value How much do they spend? $ value of purchases Options: Within recency group, months on file, # times mailed

8. RFM / Customer Treatment RFM segmentation can be used to manage customer treatment across functions Functional DimensionTreatment Implication Customer ServiceService Level BillingAdjustment Practices CollectionsMinor delinquency action CreditOver limit action MarketingIncentives/Premiums

9. Gains Chart for Responsiveness DecileQty. Mailed (000) Number Resp. (#) Percent Resp. (%) Resp. Gain Index* Revenue Generated (000) Mailing Cost (000) Total Profit (000) 160020,2503.37%225$506$240$266 21,20034,2002.85190855480375 31,80048,3302.681791,208720488 42,40060,8402.531691,521960561 53,00067,5002.251501,6871,200487 63,60076,6802.131421,9171,440477 74,20081,9001.951302,0471,680367 84,80084,9601.771182,1241,920204 95,40087,4801.621082,1872,16027 106,00090,0001.501002,2502,400-150 Mailing Cost: $400/thousandRevenue: $25/response *Index of Relative Responsiveness; Universe Indexed to 100. Source: Direct Marketing, November 1988

10. Sensitivity Analysis Three variables are often used to develop a picture that gives a range of possibilities to achieve financial objectives: –Response Rate (and the cost to achieve this) –Average Order Size (and requisite merchandise) –Circulation/Audience Size (and economies for volume) These are reviewed to give a reality check on Best/Worst/Most Likely scenaria, and provide a good basis for a monthly Cash Flow analysis.

11. Sensitivity Analysis Example

12. Cash Flow Analysis When looking at a start up, you need to look at monthly cash flow. You only have one year to succeed!! In Direct Marketing this is effected by, among other things (please refer to text): –Inventory assumptions –Fulfillment forecasts –Additional bribes –Response curves –Cost of Capital

13. Lifetime Value of a Customer The net profit that you will receive from transactions with a given customer during the time that this customer continues to buy from you. Can help you make marketing strategy decisions: –If an investment increases lifetime value, do it! –If an investment decreases lifetime value, don’t! Often the benefits of a marketing investment do not come in the first year. This does not make it a bad investment! Three ways to improve lifetime value: –Increased retention –Increased spending rate –Increased referrals

14. Customer Lifetime Value Year 1Year 2Year 3Year 4Year 5 Revenue A. Customers1,0004001809050 B. Retention Rate40%45%50%55%60% C. Avg. yearly sales$150 D. Total revenue$150,000$60,000$27,000$13,500$7,500 Costs E. Cost %50% F. Total costs$75,000$30,000$13,500$6,750$3,750 Profits G. Gross profit$75,000$30,000$13,500$6,750$3,750 H. Discount rate11.21.441.732.07 I. NPV profit$75,000$25,000$9,375$3,902$1,812 J. Cumulative NPV profit$75,000$100,000$109,375$113,277$115,088 K. Lifetime value (NPV) per customer $75.00$100.00$109.38$113.28$115.09

15. RevenueYear 1Year 2Year 3 R1. Customers10,0003,000900 (3) R2. Retention Rate30.00% R3. Spending Rate$120 R4. Total Revenue$1,200,000$360,000$108,000 (4) Variable Costs C1. Percent70.00% C2. Total Variable Costs$840,000$252,000$75,600 (5) Profits P1. Gross Profit$360,000$108,000$32,400 (6) P2. Discount Rate1.001.161.35 P3. NPV Profit$360,000$93,103$24,000 (7) P4. Cumulative NPV Profit $360,000$453,103 (1)$477,103 (8) L1. Customer Lifetime Value $36.00$45.31 (2)$47.71 (9) 1. $360,000 + $93,103 2. (1) / 10,000 3. 3,000 *.3 4. $120 * (3) 5. 0.6 * (4) 6. (4) – (5) 7. (6) / 1.35 8. (1) + (7) 9. (8) / 10,000 Table 10-1. Mary Anne’s Closet

16. RevenueYear 1Year 2Year 3 R1. Referral Rate8% R2. Referred Customers0 (1)800 (5)464 R3. Retained Customers10,0005,0002,900 R4. Total Customers10,0005,800 (6)3,364 R5. Retention Rate50% R6. Spending Rate$150 R7. Total Revenue$1,500,000$870,000$504,600 Variable Costs C1. Direct Percent70% C2. Direct Costs$1,050,000$609,000$353,220 C3. Birthday Club Mailing & Gift$50,000 (2)$29,000$16,820 C4. Birthday Discounts @ $4$40,000 (3)$23,200$13,456 C5. Referral Gifts @ $5$0 (4)$4,000$2,320 C6. Total Costs$1,140,000$665,200$385,816 Profits P1. Gross Profit$360,000$204,800$118,784 P2. Discount Rate1.001.161.35 P3. NPV Profit$360,000$176,552$87,988 P4. Cumulative NPV Profit$360,000$536,552$624,540 L1. Customer Lifetime Value$36.00$53.66$62.45 1. Assume referrals buy in Year 2. 2. $5 per customer: mailing & balloons. 3. 20% will use discount, spending average of $50 times 20% discount divided by total customers on database 4. No referrals in Year 1. 5. Referral rate of 8 percent times Year 1 total customers. 6. Retained customers plus referred customers. Assumptions: Table 10-2. Mary Anne’s Closet with the Birthday Club

17. Net Change in Customer Lifetime Value Year 1Year 2Year 3 Before the Club$36.00$45.31$47.71 After the Club$36.00$53.66$62.45 Change$0.00$8.35$14.74 Times 50,000 Customers$0$417,500$737,000

18. Modeling Definition A model can be thought of as an equation that predicts an outcome –A wide range of statistical techniques can be employed –Can consider the value of all available data to predict an outcome It is a rigorously disciplined approach to applying information to the marketing process

19. Modeling / Scoring Process In building a model An appropriate dataset is defined or created The variables that explain the outcome are determined The explanatory power, weight of each variable is identified The way the variables work together is studied and understood The variables are then used to build the model equation which is then balanced, tested, and tuned to best fit and predict the desired outcome

20. Modeling / Scoring Process Scoring defined Scoring is the process of applying a model equation to each observation or customer record –This is a mechanical process of looking at the model variable for each customer –Applying the weights for each variable –Calculating a numeric value for each customer –Using the resulting value to rank each customer –The ranked customer list is then divided into groups containing equal numbers of customers, often 10 groups, sometimes more –The top declines contain higher percentages of the best prospects and therefore perform better –The resulting increase in performance of these groups over the average is called lift or gain

21. Database Marketing Over the past few years, database marketing has been a very hot topic The cost of computer performance has rapidly decreased, and the functionality of software has increased Knowledge of how to apply statistics to direct marketing has grown Today, systems are being used to target prospects, profile customers, manage customer relationships, model behavior and measure risk Database marketing is a prerequisite for success in the 90’s

22. Elements of a Marketing Database Psychographic Overlays Geodemographic Overlays Promotional History Contact History Response History Frequency Monetary Value Recency Customer/Prospect External Overlays (Adverbs) Marketing Mgmt Info (Adjectives) Performance Metrics (Verbs) Customer Definition (Nouns)

23. Database Needs Analysis There are a variety of technical solutions that may be used to address database marketing requirements Considerations include: Applications Data Availability – internal and external On-line access Data retention Operations Decision Support Frequency of data changes or updates Existing hardware and software Organization and personnel Security

24. Customer Data Customer Characteristics Demographics Psychographics Household Composition Behavior Change Drivers Key Behavior Events Performance Metrics Recency Frequency Monetary Value Marketing Management Information Contact History Response History Promotion History Customers/ Prospects Customer-Level View Selection System Program Streams Pre-Purchase ProgramsRetention Programs Reporting And Analysis System Reports/ Evaluation

25. Response Modeling Process Prepare Research Files Score Files Rank Files by Descending Score Divide Files into Deciles Calculate Gains Tables Develop Response Model

26. Prepare Research Files Customer File Take sample of customer file for research Research Sample Append enhancement data such as demographics and response history Recode variables and calculate new variables (such as response rate) Enhanced Research Sample

27. Prepare Research Files Split sample into an analytic file and a validation file Enhanced Research Sample Analytic File: Used to build response model Validation File: Used to check accuracy of response model

28. Develop Response Model Build Regression Model Regression Equation: Y = Intercept + B1*Var1 + B2*Var2 … + Bn*Varn Where: Y = Probability of Response Bi = Linear regression coefficient for variable I Vari = Variables describing attributes of individual customers

29. Develop Response Model Example: Y = 0.257 +0.069 *Resrate +0.072 *Income range +0.009 *Age +0.159 *Male Intercept VariableDescription ResrateResponse rate to prior promotions Income RangeIncome (recoded – range is 1 to 4) AgeAge of customer MaleIndicates customer is male

30. Develop Response Model Select best model based on: Regression statistics Analytic and validation gains tables Intuitive appeal

31. Score Analytic File Apply equation to every customer record on file Example: Customer 1Customer 2 VariableCoefficient Value Variable Value Partial Score Variable Value Partial Score Intercept0.257 Resrate0.069150.250.0172880.0120.0008298 Income Range0.0715240.2860820.14304 Age-0.0087548-0.4225-0.21875 Male-0.159001 Total Score:0.140370.0231198

32. Score Analytic and Validation Files Analytic File Regression Equation Y=0.257 0.06915*Resrate 0.07152*Income Range -0.0875*Age -0.159*Male Scored Customer File Repeat scoring procedure for validation file

33. Rank Files by Descending Score Analytic Highest Score Most likely to respond Lowest Score Least likely to respond Customer 1 Customer 2 Repeat ranking for validation file

34. Divide Files into Deciles Analytic Customer 1 Customer 2 Highest Score Most likely to respond Lowest Score Least likely to respond Follow same procedure for validation file

35. Calculate Gains Tables Analytic Cumulative DecileMailedRespRateMailedRespRateCutoff Score 16508513%6508513%0.125 2650609%130014511%0.098 3650457%195019010%0.086 4650294%26002198%0.073 5650264%32502458%0.069 6650152%39002607%0.054 7650112%45502716%0.045 865051%52002765%0.035 965041%58502805%0.019 1065010%65002814%----

36. Calculate Gains Tables Validation Cumulative DecileMailedRespRateMailedRespRateCutoff Score 16507111%6507111%0.125 2650538%130012410%0.098 3650406%19501648%0.086 4650325%26001968%0.073 5650305%32502267%0.069 6650274%39002536%0.054 7650122%45502656%0.045 865061%52002715%0.035 965041%58502755%0.019 1065051%65002804%----

37. Produce Gains Table for Entire Customer File Score customer file using regression equation Rank file by score Divide file into deciles Calculate gains table

38. Customer File Gains Table Cumulative Net Total Profit DecileMailedRespRateMailedRespRateCutoff Score 1650085413%650085413%0.125$11,517.00 265006029%13000145611%0.098$17,238.00 365004557%19500191110%0.086$19,578.00 465002995%2600022109%0.073$18,330.00 565002644%3250024748%0.069$16,277.00 665001582%3900026327%0.054$11,786.00 765001122%4550027446%0.045$6,237.00 86500531%5200027975%0.035($669.00) 96500411%5850028385%0.019($7,851.00) 10650060%6500028444%----($15,838.00) Incorporating Financial Data Net total profit = (Number of responders * average profit per response) - (Number of pieces mailed * total cost per mailed piece) Total cost per mailed piece = $1.25; Profit per response = $23.00