Working with Digital Browsing Behavior to Improve Customer Response Incorporating digital predictors into a mailing strategy can help you make better print mailing decisions, and can increase revenue by two to three times. CohereOne is pioneering overlaying online browsing behavior on traditional circulation planning to target customers who would traditionally be considered unmailable in catalog circulation planning yet may actually be responding well within the response range needed to be profitable. Smart multichannel merchants are leveraging their customer’s online behavior to see if they are still actively engaged with the brand even though they may not have made a recent catalog purchase. the information gleaned can also identify segments that will not respond and should be suppressed. Increasing catalog circulation to customers who are likely to make a catalog purchase and suppressing customers who are no longer engaged is the one-two punch that can really improve your bottom line. Two Case Studies CohereOne shares two case studies showing how they found opportunities for both increasing reactivation circulation and suppressing unproductive names. One retailer incorporated digital predictors and reactivated their older buyers by 83%. That’s pretty significant when you consider the cost of acquiring new buyers. Join Travis Seaton, Vice President of Client Services, and Jude Hoffner, Vice President of Digital Product Management at CohereOne, as they explore how traditional selection criteria in circulation management (recency, frequency, monetary) is making room for a more targeted and ecommerce-centric approach.

1. Using Web Behavior to Improve Catalog Response
Rates
1

2. A Brief History of Direct Marketing
Demographics
Gender
Zip Code
Age
Surveys
2

3. A Brief History of Direct Marketing
Demographics
Gender
Zip Code
Age
Surveys
Transactions
Recency
Frequency
Products
Channel
3

4. A Brief History of Direct Marketing
Demographics
Gender
Zip Code
Age
Surveys
Transactions
Recency
Frequency
Products
Channel
4
Portraits of What Customers Look Like and Their
Purchase History

5. A Brief History of Direct Marketing
Demographics
Gender
Zip Code
Age
Surveys
Transactions
Recency
Frequency
Products
Channel
5
Behavior
Browsing
Searching
Considering
Signaling
Portraits of What Customers Look Like and Their
Purchase History

6. A Brief History of Direct Marketing
Demographics
Gender
Zip Code
Age
Surveys
Transactions
Recency
Frequency
Products
Channel
6
Behavior
Browsing
Searching
Considering
Signaling
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
Transactional Data Valuation
Abacus
Datalogix
8

9. Capital Markets Understand the Value of Intent
Transactional Data Valuation
Abacus
Datalogix
9
Intent Data Valuation
Google

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
Potential to suppress catalog contact based on device preference
Segment Mailed Customers Sales $/customer
SEGMENT 5-7 Desktop Preference yes 12,439 55,354$ 4.45$
SEGMENT 5-7 Desktop Preference no 3,732 7,986$ 2.14$
SEGMENT 5-7 Tablet Preference yes 7,312 28,882$ 3.95$
SEGMENT 5-7 Tablet Preference no 2,194 7,283$ 3.32$
SEGMENT 5-7 Smart Phone Preference yes 3,415 8,367$ 2.45$
SEGMENT 5-7 Smart Phone Preference no 1,025 2,623$ 2.56$
SEGMENT 5-7 Mixed Preference yes 1,823 7,383$ 4.05$
SEGMENT 5-7 Mixed Preference no 547 1,537$ 2.81$
SEGMENT 8-10 Desktop Preference yes 14,613 58,160$ 3.98$
SEGMENT 8-10 Desktop Preference no 4,384 8,987$ 2.05$
SEGMENT 8-10 Tablet Preference yes 8,692 27,988$ 3.22$
SEGMENT 8-10 Tablet Preference no 2,608 6,910$ 2.65$
SEGMENT 8-10 Smart Phone Preference yes 4,880 13,030$ 2.67$
SEGMENT 8-10 Smart Phone Preference no 1,464 3,060$ 2.09$
SEGMENT 8-10 Mixed Preference yes 2,067 7,317$ 3.54$
SEGMENT 8-10 Mixed Preference no 620 2,139$ 3.45$

21. Reduce Catalog Mailings
21
Potential to suppress catalog contact based on device preference
Segment Mailed Customers Sales $/customer
SEGMENT 5-7 Desktop Preference yes 12,439 55,354$ 4.45$
SEGMENT 5-7 Desktop Preference no 3,732 7,986$ 2.14$
SEGMENT 5-7 Tablet Preference yes 7,312 28,882$ 3.95$
SEGMENT 5-7 Tablet Preference no 2,194 7,283$ 3.32$
SEGMENT 5-7 Smart Phone Preference yes 3,415 8,367$ 2.45$
SEGMENT 5-7 Smart Phone Preference no 1,025 2,623$ 2.56$
SEGMENT 5-7 Mixed Preference yes 1,823 7,383$ 4.05$
SEGMENT 5-7 Mixed Preference no 547 1,537$ 2.81$
SEGMENT 8-10 Desktop Preference yes 14,613 58,160$ 3.98$
SEGMENT 8-10 Desktop Preference no 4,384 8,987$ 2.05$
SEGMENT 8-10 Tablet Preference yes 8,692 27,988$ 3.22$
SEGMENT 8-10 Tablet Preference no 2,608 6,910$ 2.65$
SEGMENT 8-10 Smart Phone Preference yes 4,880 13,030$ 2.67$
SEGMENT 8-10 Smart Phone Preference no 1,464 3,060$ 2.09$
SEGMENT 8-10 Mixed Preference yes 2,067 7,317$ 3.54$
SEGMENT 8-10 Mixed Preference no 620 2,139$ 3.45$

22. Reduce Catalog Mailings
22
Potential to suppress catalog contact based on device preference
Segment Mailed Customers Sales $/customer
SEGMENT 5-7 Desktop Preference yes 12,439 55,354$ 4.45$
SEGMENT 5-7 Desktop Preference no 3,732 7,986$ 2.14$
SEGMENT 5-7 Tablet Preference yes 7,312 28,882$ 3.95$
SEGMENT 5-7 Tablet Preference no 2,194 7,283$ 3.32$
SEGMENT 5-7 Smart Phone Preference yes 3,415 8,367$ 2.45$
SEGMENT 5-7 Smart Phone Preference no 1,025 2,623$ 2.56$
SEGMENT 5-7 Mixed Preference yes 1,823 7,383$ 4.05$
SEGMENT 5-7 Mixed Preference no 547 1,537$ 2.81$
SEGMENT 8-10 Desktop Preference yes 14,613 58,160$ 3.98$
SEGMENT 8-10 Desktop Preference no 4,384 8,987$ 2.05$
SEGMENT 8-10 Tablet Preference yes 8,692 27,988$ 3.22$
SEGMENT 8-10 Tablet Preference no 2,608 6,910$ 2.65$
SEGMENT 8-10 Smart Phone Preference yes 4,880 13,030$ 2.67$
SEGMENT 8-10 Smart Phone Preference no 1,464 3,060$ 2.09$
SEGMENT 8-10 Mixed Preference yes 2,067 7,317$ 3.54$
SEGMENT 8-10 Mixed Preference no 620 2,139$ 3.45$

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, 3rd 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
• 3rd 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
Segment 0-12 13+
Grand
Total
Avg Mnth
Last
Avg LTD
Order
Avg LTD
$
1 8,345 155 8,500 3.2 4.64 $751
2 8,185 315 8,500 4.9 2.20 $316
3 7,942 558 8,500 6.4 1.85 $236
4 6,718 1,782 8,500 8.4 1.77 $212
5 4,937 3,563 8,500 11.5 1.76 $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
Mail Qty Orders Demand Contribution Resp % AOV $/Bk Cont/Book
Planned Mail 343,578 3,722 $441,553 $64,930 1.08% $119 $1.29 $0.19
Non Planned Mail with Web 23,598 347 $40,873 $10,523 1.47% $118 $1.73 $0.45

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, 3rd 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
• 3rd 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
Segment 0-12 13+
Grand
Total
Avg Mnth
Last
Avg LTD
Order Avg LTD $
1 27,053 2,947 30,000 1.4 5.19 $95
2 26,788 3,212 30,000 4.7 4.09 $80
3 26,231 3,769 30,000 8.0 3.56 $75
4 25,931 4,069 30,000 11.0 3.39 $74
5 25,631 4,369 30,000 14.2 3.26 $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
Mail Qty Orders Demand Contribution Resp % AOV $/Bk Cost/Cust
Planned Reactivation 75,291 409 $50,412 ($13,179) 0.54% $123 $0.66 ($32.23)
Non Planned Reactivation with Web 25,740 240 $23,805 ($126) 0.93% $99 $0.92 ($0.53)

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