catalog university pub talk: leveraging browsing behavior to improve catalog circulation planning

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

8

Capital Markets Understand the Value of Intent

Transactional Data Valuation

Abacus

Datalogix

$500-$750 million

9

Intent Data Valuation

Google

$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

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$

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

tseaton@cohereone.com

Jude Hoffner, VP Digital Products

jhoffner@cohereone.com