1. Using Web Behavior to Improve Catalog Response Rates 1

2. A Brief History of Direct Marketing 2

3. 3

4. 4 Portraits of What Customers Look Like and Their Purchase History

5. A Brief History of Direct Marketing 5 Portraits of What Customers Look Like and Their Purchase History

6. A Brief History of Direct Marketing 6 Portraits of What Customers Look Like and Their Purchase History Intent

7. Intent is shown online Individuals send signals with digital browsing activity, not just buying history! 7

8. Capital Markets Understand the Value of Intent 8

9. 9

10. Transactional Data 10 Browsing Data (Intent)

11. The Circulation Challenge Difficult to connect browsing data to individuals 11

12. The Solution Capture web browsing data at the individual level Connect it to individual customer profiles 12

13. The Solution Capture web browsing data at the individual level Connect it to individual customer profiles 13

14. Circulation Applications 4 Strategies for Browsing Behavior 14

15. Circulation Applications 4 Strategies for Browsing Behavior Supercharge reactivation 15

16. Circulation Applications 4 Strategies for Browsing Behavior Supercharge reactivation Reduce Catalog Mailings 16

17. Circulation Applications 4 Strategies for Browsing Behavior Supercharge reactivation Reduce Catalog Mailings Source of Prospects 17

18. Circulation Applications 4 Strategies for Browsing Behavior Supercharge reactivation Reduce Catalog Mailings Source of Prospects Use product & category browsing data in selection 18

19. Supercharge reactivation 19

20. Reduce Catalog Mailings 20

21. Reduce Catalog Mailings 21

22. Reduce Catalog Mailings 22

23. Browsers as Prospects 23 Browsing activity can open up large universes!

24. Browsers as Prospects 24 Browsing activity can open up large universes! Model browsing data to identify most responsive leads

25. Add product browsing activity into selection 25

26. Add product browsing activity into selection 26 Last 4 products viewed online

27. TWO CASE STUDIES 27

28. Case Study #1 – Women’s Fashion Apparel 28 Company profile  Multichannel retailer with an established brand for over 40 years  Target customer: Affluent women in her 50’s and 60’s  Revenues in 2014: $25 million  Estimated Catalog Circulation in 2014: 10 million  Promotion/Channel: Catalog, Online, 3 rd Party, Wholesale  Seasonality: Spring, Summer, Fall, Winter Business Situation  Retailer sells women’s apparel direct to customers Ecommerce website and print catalog marketing channels  Retailer sells women’s apparel indirectly 3 rd Party Marketplace (i.e. Amazon) and Wholesale  Catalog is the primary demand driver in the business Accounts for 80%-90% of direct demand

29. Case Study #1 – Women’s Fashion Apparel 29 Marketing Strategy  Transaction based scoring model Recency, Frequency, Average Order and Product  Model identifies only +/-30% of customer database to mail profitably  Up to 70% of the customer file does not qualify for mailing All have not purchased in at least one year Segment0-1213+ Grand Total Avg Mnth Last Avg LTD Order Avg LTD $ 18,3451558,500 3.24.64$751 28,1853158,500 4.92.20$316 37,9425588,500 6.41.85$236 46,7181,7828,500 8.41.77$212 54,9373,5638,500 11.51.76$219

30. Case Study #1 – Women’s Fashion Apparel 30 Solution  Capture individual browsing activity on ecommerce site  Combine with the transactional history at the individual customer level  Customer’s digital behavior is utilized when developing audiences for catalog mailings Six Month Longitudinal Testing  Mailed customers with digital behavior who did not qualify to be mailed based upon their transaction score Non Planned Mail with Web  Result was an additional 6% in catalog circulation  Web Behavior scored names outperformed all other Planned Mail names combined

31. Case Study #2 – Workwear 31 Company profile  Multichannel retailer - Market leader the past 30 years  Target customer: 35-50 years of age who is buying personally, for use at work  Revenues in 2014: $30 million  Estimated Catalog Circulation in 2014: 9 million  Promotion/Channel: Catalog, Online, 3 rd Party  Seasonality: Spring, Summer, Fall, Holiday, Winter Business Situation  Retailer sells workwear, both private label and national brands Ecommerce website and print catalog marketing channels  Retailer sells indirectly 3 rd Party Marketplace (i.e. Amazon)  Catalog is the primary demand driver in the business Accounts for 70%-80% of direct demand

32. Case Study #2 – Workwear 32 Marketing Strategy  Transaction based scoring model Recency, Frequency, Average Order, Profession, Address Type  Model identifies only +/-40% of customer database to mail profitably  Up to 60% of the customer file does not qualify for mailing All have not purchased in at least one year Segment0-1213+ Grand Total Avg Mnth Last Avg LTD OrderAvg LTD $ 127,0532,94730,000 1.45.19$95 226,7883,21230,000 4.74.09$80 326,2313,76930,000 8.03.56$75 425,9314,06930,000 11.03.39$74 525,6314,36930,000 14.23.26$73

33. Case Study #2 – Workwear 33 Solution  Capture individual browsing activity on ecommerce site  Combine with the transactional history at the individual customer level  Customer’s digital behavior is utilized when developing audiences for catalog mailings Quarterly Season Testing  Mailed customers with digital behavior who did not qualify to be mailed based upon their transaction score Non Planned Reactivation with Web  Result was an additional 35% in catalog circulation  Web Behavior scored names outperformed all other Planned Mail names combined

34. Thank you! Questions 34 Travis Seaton, VP Client Services tseaton@cohereone.com Jude Hoffner, VP Digital Products jhoffner@cohereone.com