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1
Affiliate Marketing:
Why Data Matters
Kristen Pulver
Director of Affiliate Marketing
Horizon Media, Inc.
Dave Stewart
Chief Technology Officer
CAKE
2
Content
 Introduction
 Why data matters
 Implementing data
 Data transparency
 Optimization
3
About
Dave Stewart
Chief Technology Officer
CAKE
Kristen Pulver
Director of Affiliate Marketing
Horizon Media, Inc.
4
What Data Should Be Collected/Analyzed?
 Demographic/Technographic
Consumer Data
 Purchase acquisition information
 Campaign attribution
 Backend performance data
 Fraud scores
5
Understand your audience
- CTA optimization
- Messaging
1 Determine where to
find your audience
2
Basic Consumer Demographic Insight
6
Purchase Acquisition Data
 Understand consumer interests
 Leverage mechanisms for
retargeting
– Email, Ad Exchanges, etc…
 Gain insights into audience
segmentation from different
sources
7
Attribution
 Customer Journeys + touchpoints
 Attributing credit to
“correct” sources
 Shared attribution models
equals greater accuracy
8
 Lead generators:
– close rate
– milestones
 Retail:
– customer loyalty
– return purchases
 Subscriptions  lifetime value
 Cohort reporting
Client KPI’s
9
Share Data with Partners
 Identify primary client KPIs
to determine quality
 Implement feedback loop
10
Fraud
 Display/CPM fraud
 Click/CPC fraud
 Conversion/lead fraud
11
Combat Fraud
 Crucial tools
 Greater transparency
 True business value
Key Takeaways
 Attribution: know your publishers & understand your
traffic patterns.
 Audience Identification & Targeting: let your media results
tell the story.
 Advertisers: identify quality indicators & provide vendor
level feedback. Ensure your buyers use this data.
 Fraud: be vigilant in holding publishers accountable.
13
Thank you

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Affiliate Marketing: Why Data Matters

  • 1. 1 Affiliate Marketing: Why Data Matters Kristen Pulver Director of Affiliate Marketing Horizon Media, Inc. Dave Stewart Chief Technology Officer CAKE
  • 2. 2 Content  Introduction  Why data matters  Implementing data  Data transparency  Optimization
  • 3. 3 About Dave Stewart Chief Technology Officer CAKE Kristen Pulver Director of Affiliate Marketing Horizon Media, Inc.
  • 4. 4 What Data Should Be Collected/Analyzed?  Demographic/Technographic Consumer Data  Purchase acquisition information  Campaign attribution  Backend performance data  Fraud scores
  • 5. 5 Understand your audience - CTA optimization - Messaging 1 Determine where to find your audience 2 Basic Consumer Demographic Insight
  • 6. 6 Purchase Acquisition Data  Understand consumer interests  Leverage mechanisms for retargeting – Email, Ad Exchanges, etc…  Gain insights into audience segmentation from different sources
  • 7. 7 Attribution  Customer Journeys + touchpoints  Attributing credit to “correct” sources  Shared attribution models equals greater accuracy
  • 8. 8  Lead generators: – close rate – milestones  Retail: – customer loyalty – return purchases  Subscriptions  lifetime value  Cohort reporting Client KPI’s
  • 9. 9 Share Data with Partners  Identify primary client KPIs to determine quality  Implement feedback loop
  • 10. 10 Fraud  Display/CPM fraud  Click/CPC fraud  Conversion/lead fraud
  • 11. 11 Combat Fraud  Crucial tools  Greater transparency  True business value
  • 12. Key Takeaways  Attribution: know your publishers & understand your traffic patterns.  Audience Identification & Targeting: let your media results tell the story.  Advertisers: identify quality indicators & provide vendor level feedback. Ensure your buyers use this data.  Fraud: be vigilant in holding publishers accountable.

Editor's Notes

  1. B1: Dave B2: Dave B3: Kristen B4:Dave/Kristen B5: Dave
  2. Kristen Slide
  3. Dave slide
  4. Dave slide
  5. Kristen slide
  6. Dave slide
  7. Dave/Kristen slide