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