SlideShare a Scribd company logo
From Wheelbarrows to Macbeth –
Behaviour Modelling for Publishers
Martin Goodson(Skimlinks)/Emma Martin (ESI Media)
Skimlinks stats
When Adverts Don’t Work
Skimlinks statsA Common Approach to Targeting Ads
• Tag pages on your ‘Camera Lens’ forum
• Show ads for camera lenses to anyone who visits a page tagged with
‘Camera Lens’
• Expensive and inaccurate
Publisher Partners
Merchant Partners
Skimlinks statsSkimlinks stats
Skimlinks Method
Datastore
Build Daily
profiles
Intermediate Data
Structure (parquet)
Events (1 TB)
Daily Profiles
1.8 GB
Build Monthly
profiles
Generate Audience SegmentsCustomers
Monthly Aggregate
40 GB
The Pipeline
Datastore
Build Daily
profiles
Intermediate Data
Structure (parquet)
Events (1 TB)
Daily Profiles
1.8 GB
Build Monthly
profiles
Generate Audience SegmentsCustomers
Monthly Aggregate
40 GB
ML Enrichment
The Pipeline
Building a Model: Women’s Bags
Product visit: Accessories - Women’s Bags
Interest: Accessories - Women’s Bags
Interest: Media/Entertainment – Celebrity Gossip
Interest: Food/Cooking
Page Impression: handbagblogsite.com
Page Impression: contentsite.com/women
Building a Model: Theatre Tickets
Product Visit: Theatre Tickets
Interest: Art/Culture
Interest: Gardening Equipment
Page Impression: editorialsite.co.uk/culture
Building a Model: Motorbike Accessories
Product Visit: Automotive - Motorbike Accessories
Page Impression: editorialsite.com/autos
Page Impression: hondarebelblog.com
Page Impression: www.motorcycleforumsite.net
Page Impression: editorialsite.com/science
Page Impression: editorialsite.com.com/divorce
Page Impression: Notebookforumsite.com
Testing Model Performance
Tech Fashion Toys
Toys 1 0 100
Fashion 2 100 3
Tech 100 1 1
Number of clicks in
the following month
Segment names
Testing Model Performance
Tech Fashion Toys
Toys 1 0 100
Fashion 2 100 3
Tech 100 1 1
Want high values
on the diagonal
Number of clicks in
the following month
Segment names
Testing Model Performance
Recommendations: Barkbox.com
Product sold: High end gifts for Dogs
Segment Ratio
Pet Supplies 697.5
Specialist Food 59.59
Children’s Toys 15.25
Beauty Products 9.51
Health Foods/vitamins 3.45
Gourmet Food 4.18
Men’s Fashion 2.05
Cosmetics 3.04
Business & Finance 1.78
Education Careers 1.93
Recommendations: Petmountain.com
Product sold: Pets Supplies sold in volume
Segment Ratio
Pet Supplies 46.29
Grocery 4.2
DIY Home Improvement 3.3
Summary
Publisher data is a powerful tool for targeting online ads:
Create models to predict user intent
Validate model prediction
Recommend the right models for a specific campaign
ESI’s Love Affair with Data
At ESI, we believe in the power of our data. By utilising it effectively we can…
• Increase understanding of our users by mapping their journeys
• Improve consumer interaction with our brands by;
- offering a personalised experience on web, mobile and email
- serving more relevant, useful & engaging advertising
• Help our advertising partners speak to consumers in the most appropriate tone, at the right time
and in a fitting context
Audience Targeting
FIRST PARTY DATA
Browsing behaviour, content shared or liked, search terms
Registered user data; gender, age, postcode, interests, likes
+
SECOND PARTY DATA
eCommerce / advertiser data; previous purchase history
+
THIRD PARTY DATA
Intent or interest data, purchase histories, demographics, psychological types
Not all third party data is equal
Accuracy Reach Versatility
The Media Owner’s View
Accuracy Reach Versatility
The Media Owner’s View
Title: Open Sans 100 px
• Subtitle: Open Sans 48 px
Title: Open Sans 100 px
• Subtitle: Open Sans 48 px

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FROM WHEELBARROWS TO MACBETH – BEHAVIOUR MODELLING FOR PUBLISHERS - MARTIN GOODSON & EMMA MARTIN

  • 1. From Wheelbarrows to Macbeth – Behaviour Modelling for Publishers Martin Goodson(Skimlinks)/Emma Martin (ESI Media)
  • 4. Skimlinks statsA Common Approach to Targeting Ads • Tag pages on your ‘Camera Lens’ forum • Show ads for camera lenses to anyone who visits a page tagged with ‘Camera Lens’ • Expensive and inaccurate
  • 5.
  • 10. Datastore Build Daily profiles Intermediate Data Structure (parquet) Events (1 TB) Daily Profiles 1.8 GB Build Monthly profiles Generate Audience SegmentsCustomers Monthly Aggregate 40 GB The Pipeline
  • 11. Datastore Build Daily profiles Intermediate Data Structure (parquet) Events (1 TB) Daily Profiles 1.8 GB Build Monthly profiles Generate Audience SegmentsCustomers Monthly Aggregate 40 GB ML Enrichment The Pipeline
  • 12.
  • 13.
  • 14.
  • 15. Building a Model: Women’s Bags Product visit: Accessories - Women’s Bags Interest: Accessories - Women’s Bags Interest: Media/Entertainment – Celebrity Gossip Interest: Food/Cooking Page Impression: handbagblogsite.com Page Impression: contentsite.com/women
  • 16. Building a Model: Theatre Tickets Product Visit: Theatre Tickets Interest: Art/Culture Interest: Gardening Equipment Page Impression: editorialsite.co.uk/culture
  • 17. Building a Model: Motorbike Accessories Product Visit: Automotive - Motorbike Accessories Page Impression: editorialsite.com/autos Page Impression: hondarebelblog.com Page Impression: www.motorcycleforumsite.net Page Impression: editorialsite.com/science Page Impression: editorialsite.com.com/divorce Page Impression: Notebookforumsite.com
  • 18. Testing Model Performance Tech Fashion Toys Toys 1 0 100 Fashion 2 100 3 Tech 100 1 1 Number of clicks in the following month Segment names
  • 19. Testing Model Performance Tech Fashion Toys Toys 1 0 100 Fashion 2 100 3 Tech 100 1 1 Want high values on the diagonal Number of clicks in the following month Segment names
  • 21. Recommendations: Barkbox.com Product sold: High end gifts for Dogs Segment Ratio Pet Supplies 697.5 Specialist Food 59.59 Children’s Toys 15.25 Beauty Products 9.51 Health Foods/vitamins 3.45 Gourmet Food 4.18 Men’s Fashion 2.05 Cosmetics 3.04 Business & Finance 1.78 Education Careers 1.93
  • 22. Recommendations: Petmountain.com Product sold: Pets Supplies sold in volume Segment Ratio Pet Supplies 46.29 Grocery 4.2 DIY Home Improvement 3.3
  • 23. Summary Publisher data is a powerful tool for targeting online ads: Create models to predict user intent Validate model prediction Recommend the right models for a specific campaign
  • 24. ESI’s Love Affair with Data At ESI, we believe in the power of our data. By utilising it effectively we can… • Increase understanding of our users by mapping their journeys • Improve consumer interaction with our brands by; - offering a personalised experience on web, mobile and email - serving more relevant, useful & engaging advertising • Help our advertising partners speak to consumers in the most appropriate tone, at the right time and in a fitting context
  • 25. Audience Targeting FIRST PARTY DATA Browsing behaviour, content shared or liked, search terms Registered user data; gender, age, postcode, interests, likes + SECOND PARTY DATA eCommerce / advertiser data; previous purchase history + THIRD PARTY DATA Intent or interest data, purchase histories, demographics, psychological types
  • 26. Not all third party data is equal Accuracy Reach Versatility
  • 27. The Media Owner’s View Accuracy Reach Versatility
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  • 32. Title: Open Sans 100 px • Subtitle: Open Sans 48 px
  • 33. Title: Open Sans 100 px • Subtitle: Open Sans 48 px