catalog university pub talk: leveraging browsing behavior to improve catalog circulation planning
TRANSCRIPT
Using Web Behavior to Improve Catalog Response
Rates
1
A Brief History of Direct Marketing
EARLY DAYS
Demographics
Gender
Zip Code
Age
Surveys
2
A Brief History of Direct Marketing
EARLY DAYS
Demographics
Gender
Zip Code
Age
Surveys
TODAY
Transactions
Recency
Frequency
Products
Channel
3
A Brief History of Direct Marketing
EARLY DAYS
Demographics
Gender
Zip Code
Age
Surveys
TODAY
Transactions
Recency
Frequency
Products
Channel
4
Portraits of What Customers Look Like and Their Purchase History
A Brief History of Direct Marketing
EARLY DAYS
Demographics
Gender
Zip Code
Age
Surveys
TODAY
Transactions
Recency
Frequency
Products
Channel
5
FUTURE
Behavior
Browsing
Searching
Considering
Signaling
Portraits of What Customers Look Like and Their Purchase History
A Brief History of Direct Marketing
EARLY DAYS
Demographics
Gender
Zip Code
Age
Surveys
TODAY
Transactions
Recency
Frequency
Products
Channel
6
FUTURE
Behavior
Browsing
Searching
Considering
Signaling
Portraits of What Customers Look Like and Their Purchase History Intent
Intent is shown online
Individuals send signals with digital browsing activity, not just buying history!
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Capital Markets Understand the Value of Intent
Transactional Data Valuation
Abacus
Datalogix
$500-$750 million
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Capital Markets Understand the Value of Intent
Transactional Data Valuation
Abacus
Datalogix
$500-$750 million
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Intent Data Valuation
$350-$400 BILLION
Transactional Data
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Browsing Data (Intent)
The Circulation Challenge
Difficult to connect browsing data to individuals
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The Solution
Capture web browsing data at the individual level
Connect it to individual customer profiles
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The Solution
Capture web browsing data at the individual level
Connect it to individual customer profiles
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Circulation Applications
4 Strategies for Browsing Behavior
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Circulation Applications
4 Strategies for Browsing Behavior
Supercharge reactivation
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Circulation Applications
4 Strategies for Browsing Behavior
Supercharge reactivation
Reduce Catalog Mailings
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Circulation Applications
4 Strategies for Browsing Behavior
Supercharge reactivation
Reduce Catalog Mailings
Source of Prospects
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Circulation Applications
4 Strategies for Browsing Behavior
Supercharge reactivation
Reduce Catalog Mailings
Source of Prospects
Use product & category browsing data in selection
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Supercharge reactivation
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Client 1
Client 3
Client 2
Client 4
Reduce Catalog Mailings
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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$
Reduce Catalog Mailings
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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$
Reduce Catalog Mailings
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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$
Browsers as Prospects
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Browsing activity can open up large universes!
Browsers as Prospects
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Browsing activity can open up large universes!
Model browsing data to identify most responsive leads
Add product browsing activity into selection
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Add product browsing activity into selection
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Last 4 products viewed online
TWO CASE STUDIES
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Case Study #1 – Women’s Fashion Apparel
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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
Case Study #1 – Women’s Fashion Apparel
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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 $7512 8,185 315 8,500 4.9 2.20 $3163 7,942 558 8,500 6.4 1.85 $2364 6,718 1,782 8,500 8.4 1.77 $2125 4,937 3,563 8,500 11.5 1.76 $219
SAMPLE
Case Study #1 – Women’s Fashion Apparel
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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/BookPlanned 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
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
Case Study #2 – Workwear
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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 $952 26,788 3,212 30,000 4.7 4.09 $803 26,231 3,769 30,000 8.0 3.56 $754 25,931 4,069 30,000 11.0 3.39 $745 25,631 4,369 30,000 14.2 3.26 $73
SAMPLE
Case Study #2 – Workwear
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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/CustPlanned 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)
Thank you!
Questions
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Travis Seaton, VP Client Services
Jude Hoffner, VP Digital Products