Customer Acquisition & Modeling

1. Customer Acquisition & Modeling Arthur Middleton HughesVP Solutions ArchitectKnowledgeBase Marketing

2. The tone of a good direct mail letter is as direct and personal as the writer’s skill can make it. Even though it may go to millions of people, it never orates to a crowd but rather murmurs into a single ear. It’s a message from one letter writer to one letter reader. – Harry B. Walsh Customer Acquisition

3. The best way to acquire customers • Trick question– there is no one best way. • TV, Radio, Print, Store Promotions, Direct Mail all work. • Let’s begin with direct mail.

4. Direct Response TV, Radio, Print or Direct Mail Piece Telesales Database Customer info put into a database These names are Responders. Customercalls or visits a store She RESPONDS Retail Web

5. Begin with a list • There are 40,000 lists of consumers and businesses available for rent. • Use a list broker. Pay from $50 to $250 per thousand names for a single use. • Two types: response lists and compiled lists. • Response lists always work better because half of US households never buy anything by mail. • For a large mailing of 2 million names, you may rent 200 different lists. You rent 3 million names because you will find a lot of duplicates.

6. How a direct mail campaign is created Marketers plan their campaign List Brokers supply names Service Bureau processes the names Typical Job: 300 Lists 3 million names Mail Shop Prints the names on letters and cards Letters are sent

7. CASS DPV Duplicate Consolidation AEC DSF2 Suppression LACS 2 Million Same Format NCOALink DCOA Traditional Merge Purge 300 Lists 3 million 3 Million Same Format Reformatting Coding Accuracy Support Delivery Point Validation Address Element Correction Delivery Sequence File Locatable Address Conversion Dynamic Change of Address Outgoing Mail File Mail Shop

8. Processing saves money

9. DSF Improves Response Rate Delivery Sequence File

10. Some Direct Response Rules • Choice kills: give them one offer – take it or leave it. • Always make every mailing a test so you learn from it. • Divide your list in half: send a test and a control. • The control is your best offer from the past. • Keep trying to beat your control. When you do, that offer becomes your control.

11. What kind of response will you get? • 2% is a good general rule. This means that 98% will throw your mail piece in the trash. • Some companies make a profit with ¼ of 1% response. • The DMA sells a book listing response rates in various industries. • The Wall Street Journal has used the same letter successfully for more than forty years…

12. THE WALL STREET JOURNAL Dear Reader: On a beautiful late spring afternoon, twenty-five years ago, two young men graduated from the same college. They were very much alike, these two young men. Both had been better than average students, both were personable and both – as young college graduates are – were filled with ambitious dreams for the future. Recently these men returned to their college for their 25th reunion. They were still very much alike. Both were happily married. Both had three children. And both, it turned out, had gone to work for the same Midwestern service company after graduation and were still there. But there was a difference. One of the men was manager of a small department of that company. The other was its president.

13. Acquiring Retail Customers • Reverse phone append works. • The Sports Authority captured phone numbers in their stores before Christmas. • They reverse appended the name and address. • They got 11% of those to whom they sent post cards to come back once again before Christmas.

14. SPAM is out • Never acquire customers by sending emails to unknown people. • It is unethical. You will acquire the wrong kind of customer. • You may send emails to partners if the recipients have given permission.

15. Principles behind modeling • Models permit you to predict how people will react to your communications. By knowing this, you can send promotions to those most likely to respond, and omit those less likely. • Modeling is also used for predicting who is most likely to defect. • Prospects and customers in many segments react in predictable ways. • Clues to expected behavior can sometimes be discerned in people’s previous behavior and demographics • Predictive models are developed from responses to previous promotions.

16. How to begin a model • Do a promotion, or use an already completed promotion. • Keep both the respondents and those who did not respond. • To be valid, you should have at least 500 respondents. • Determine the size of a test by dividing the expected response rate into 500: • Response Rate 2% Test Size = 500/.02 = 25,000 • Assume you have 500 responses (buyers) and 24,500 who did not buy. * • Append demographics to your entire file of 25,000

17. Divide your data into two parts Build Model with this group

18. Discard the outliersBuild the model • There are always weird records – someone who bought 50 times more than the average. • Discard these folks, as they will unbalance your model. • Use SAS or SPSS to build your model.

19. A model will determine the weight of each variable.

20. Develop an Algorithm • An algorithm is a mathematical routine that creates a score for every computer record, based on the model.

21. Score the file and divide into deciles Index = Responses/Average Response times 100 Index of decile 2 = 640/366 * 100 = 175

22. Now, score the validation group

23. If the model picks out the responders from the validation group it is a good model Validation Test These are almost the same. It is a good model.

24. What if it doesn’t work? • In many cases, a model does not work. It cannot accurately predict responders. Why not? • Because the available data (behavior and demographics) cannot predict which people will respond. • Example: predict which people will buy Windows XP if you send them a promotion. It could be that nothing you can append to their record will help you to predict this well enough to pay for the cost of the model. • If it does not work, then you have to give up on modeling for this situation. • Successful models are profitable because you avoid mailing people who are unlikely to respond.

25. Getting better and better Deciles

26. Who responds to mailings? • The highest deciles may be so enthusiastic about the product that they buy without being mailed • The lowest deciles may not buy at all • In some cases, the promotion should be directed at the middle deciles, since they are on the fence and need stimulus to buy. • In such cases we may be wasting money mailing to the top deciles. • While all of this may be true, it is difficult to know for a fact. • Fleet Bank discovered it could not profitably send promotions to its Gold customers. It concentrated on the Silver group.

27. Insurance Company Mailing

28. A model raises the cost of a mailing • A typical response list will cost you about 12 cents per name. • In this case the model, though productive, will not be cost effective

29. Using AmeriLINK will make this model cost effective • AmeriLINK already has the data appended. • You can select the right names directly from AmeriLINK

30. KBM Case Study • Software manufacturer wanted to get previous customers to buy upgrades. • The Low Probability customers were not worth mailing. • Using the model the response rate went from .85% to .95%, an increase of 12%.

31. Modeling to predict churn permits a risk revenue matrix

32. Modeling using CHAID(Chi-square Automatic Interaction Detection)

33. CHAID gains chart • Gains Charts let you decide how deeply to go into a prospect file. • You can use these charts to create customer segments. • The top 3 segments are 28% of the file with average profit of $1.75 per household.

34. KBM CHAID Case Study - Segments

35. Modeling to Reduce Churn • A phone company had a high defection rate. KBM was asked to analyze who was leaving and why. They used modeling. • Developed 68 models in all • Rated the models based on performance versus a control group • The key findings of the neural network model were: • Two-thirds of all defections occurred within 15 months • Approximately 4 out of 10 defections were preventable • 53% of preventable defections occurred before the seventh month

36. Quadrant Analysis

37. Strategies based on the models • Identify key customer segments – Focus on Group A. • Allocate marketing investment based on revenue and profit • Provide different treatment for each segment within the loyalty program • Provide super services to the best customers • Provide individual loyalty rewards based on a customer’s life stage, needs, and value • Use models to trigger proactive communications to customers with high attrition risk • Establish a system to detect problems and resolve them before the customer headed for the door.

38. Success from using the strategies • The program generated a return on investment of $2.09 for every $1 invested. • Attrition of those customers receiving the rewards communications was 1.27 points lower than those in a control group. • Average revenue ($1,412) in the rewards test group was 5% higher than in the control group ($1,358). • There was an increase of $19.6 million dollars in annual sales to those 13,881 customers who were retained by the loyalty program (compared to a control group).

39. Profiling

40. Education Profile

41. How to use profiles • Profiles tell you who to contact, when you do not have the information necessary to build a model • Profiles help you in designing your promotion copy: if they are all PhD’s your text would be different than if none had finished high school.

42. Prospect Databases

43. Mail These Merge Purge De-dupe $$$ High Scores 300 response lists 5,000,000 names every month Low Scores Mailing Universe $$$ $$$ $$$ Score records Throw away Append Data Why you can’t afford to append data to build models with monthly rented names

44. Prospect Database saves money, increases response rate. Model Scoring Prospect Database 250,000,000 names Hot Line and Other Names Mail Top Deciles • No wasted names • Lower monthly processing • Selection based on demographics and history • Output sent weeks earlier Compiled Names Used to supply names and to append attributes Promotion History

45. Mail selection process – done fast Prospect Database Select based on Model scores Suppress existing customers and others Marketing Staff Selected Records Merge Purge Process, Segmentation & Final Suppression Hot Line Names To mail shop

46. Suppression boosts response. • Suppress previous responders • Suppress deceased, prison, nursing homes. • Suppress DMA and other lists • Mail only good names

47. Example: How a prospect database can save list rental costs

48. Advantages of a Prospect Database • Reduce list rental costs • Increase response rates to initial mailing • Target by behavior and demographics rather than by list and age • Target based on previous promotion history • Cut up to four weeks off mailing prep time – more rapid access to hot lines • Increase percent of long term loyal customers • Target mailing to high retention segments • Read results right after responses arrive. Use them to plan the next campaign. • One Annual fee, not CPM. You can plan ahead.

49. Summary: Prospect Database • Compiled and vertical names rented for a year, stored in a database and scored with many attributes • Promotion and response history stored in prospect database • Monthly models will permit selecting high responding, high converting, high retention loyal customers • Models permit use of compiled names at lower cost • Result: higher response, conversion, retention • Significant increases in net revenue and reduction in costs

50. Thank You