This presentation is from Affiliate Summit East 2016 (July 31-August 2, 2016 in New York City, NY). Session description: Learn how attribution modeling and other cross channel insights can help you more intelligently manage your affiliate program and determine which affiliates are driving the highest value.
1. Affiliate Summit East 2016
Attribution and Affiliate Marketing
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT
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2. W E L C O M E
● Founding Team, Commission Junction: 1998 - 2006
● VP Sales & Business Development, Digital River,
oneNetworkDirect : 2006 - 2008
● VP Strategic Initiatives, Impact Radius : 2008 - present
Todd Crawford
3. THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
During this session I will provide a simple explanation of how just a
few attribution data points can be leveraged to optimize your affiliate program.
PRESENTATION OBJECTIVE
Provide information in the most simple and actionable
way possible so you can get the most from this presentation - avoiding
industry jargon and long winded explanations.
4. HOW CAN AFFILIATE MANAGERS EASILY LEVERAGE ATTRIBUTION
DATA TO OPTIMIZE THEIR AFFILIATE PROGRAM?
● Better understand the consumer
journey (cross channel/device)
Through the adidas
affiliate program, you will
earn generous
commissions by
promoting a well-known,
global brand and adidas’s
wide variety of product
offerings including shoes,
apparel, accessories, and
more.www.adidas.com/product
1
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
5. HOW CAN AFFILIATE MANAGERS EASILY LEVERAGE ATTRIBUTION
DATA TO OPTIMIZE THEIR AFFILIATE PROGRAM?
● Better understand the consumer
journey (cross channel/device)
● Understand how affiliates interact with
other channels
Through the adidas
affiliate program, you will
earn generous
commissions by
promoting a well-known,
global brand and adidas’s
wide variety of product
offerings including shoes,
apparel, accessories, and
more.www.adidas.com/product
1
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
6. HOW CAN AFFILIATE MANAGERS EASILY LEVERAGE ATTRIBUTION
DATA TO OPTIMIZE THEIR AFFILIATE PROGRAM?
● Better understand the consumer
journey (cross channel/device)
● Understand how affiliates interact with
other channels
● Properly rewarded affiliates for their
contributions beyond last click
Through the adidas
affiliate program, you will
earn generous
commissions by
promoting a well-known,
global brand and adidas’s
wide variety of product
offerings including shoes,
apparel, accessories, and
more.www.adidas.com/product
1
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
8. AFFILIATE TRACKING: CREDITING LOGIC
● Typically last click
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9. AFFILIATE TRACKING: CREDITING LOGIC
● Typically last click
● Usually has no cross channel data
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
10. AFFILIATE TRACKING: CREDITING LOGIC
● Typically last click
● Usually has no cross channel data
● Should dedupe against other channels
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SEO
SEM
Affiliate
SEM
?d ?d ?d ?d
11. AFFILIATE TRACKING: CREDITING LOGIC
● Typically last click
● Usually has no cross channel data
● Should dedupe against other channels
● Only one channel should ever get credit
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12. AFFILIATE TRACKING: CREDITING LOGIC
● Typically last click
● Usually has no cross channel data
● Should dedupe against other channels
● Only one channel should ever get credit
● Affiliate reporting = credited revenues
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14. THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
It is a way of determining
the contribution or value of
a channel
across multiple
conversions and journeys.
15. MODEL TYPES
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First Click Last Click Position-based Linear Time Decay Algorithmic
5%
25%
15%
45%
10%
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✓ Introducers
✓ Influencers
✓ Closers
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Attributed revenues = Modeled by attribution solution
19. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
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Affiliate
Credited
Revenues
Coupon $500,000
Loyalty $250,000
Content 1 $80,000
Content 2 $50,000
Content 3 $10,000
20. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
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Affiliate
Credited
Revenues
Attributed
Revenues
Coupon $500,000 $250,000
Loyalty $250,000 $175,000
Content 1 $80,000 $92,000
Content 2 $50,000 $75,000
Content 3 $10,000 $20,000
21. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
Affiliate
Credited
Revenues
Attributed
Revenues
Δ
Coupon $500,000 $250,000 -50%
Loyalty $250,000 $175,000 -30%
Content 1 $80,000 $92,000 15%
Content 2 $50,000 $75,000 50%
Content 3 $10,000 $20,000 100%
22. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
Affiliate
Credited
Revenues
Attributed
Revenues
Δ
Introducer Influencer Closer
Coupon $500,000 $250,000 -50% 1.4% 2.6% 96%
Loyalty $250,000 $175,000 -30% 10% 4% 86%
Content 1 $80,000 $92,000 15% 15% 17% 68%
Content 2 $50,000 $75,000 50% 19% 24% 57%
Content 3 $10,000 $20,000 100% 35% 50% 15%
23. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
Affiliate
Credited
Revenues
Attributed
Revenues
Δ
Target
Commission
Rate
Revised
Commission
Rate
Coupon $500,000 $250,000 -50% 8% 4%
Loyalty $250,000 $175,000 -30% 8% 5%
Content 1 $80,000 $92,000 15% 8% 9%
Content 2 $50,000 $75,000 50% 8% 12%
Content 3 $10,000 $20,000 100% 8% 16%
24. EXAMPLE DATA SHOWING: ATTRIBUTED AND CREDITED
REVENUES COMPARISONS
THIS PRESENTATION CONTAINS CONFIDENTIAL INFORMATION - DO NOT DISTRIBUTE
Affiliate
Credited
Revenues
Old Commissions New Commissions
Δ
Coupon $500,000 $40,000 $20,000 -50.0%
Loyalty $250,000 $20,000 $12,500 -37.5%
Content 1 $80,000 $6,400 $7,200 12.5%
Content 2 $50,000 $4,000 $6,000 50.0%
Content 3 $10,000 $800 $1,600 100.0%
Totals $890,000 $71,200 $47,300 -33.6%
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● Compared credited (affiliate tracked) to attributed (modeled) revenues.
● Determine which partners are over and under-valued.
● Adjust commission rates to align more closely with attributed values.
● Periodically re-run data and adjust when necessary (quarterly?).
SUMMARY