Track: Paid Acquisition Topic: PROGRAMMATIC ADVERTISING Title: Extreme Personalization in Digital Marketing Speaker: MARK TORRANCE, CTO, Rocket Fuel Most marketing is one-to-many: marketers have just a few messages, and they AB test them to figure out which one works best for general audiences. This blunt-force targeting doesn’t really work to move the marketing needle. Every customer is different; each person has different interests, behaviors, readiness to buy, and ability to be influenced at different times and in different contexts. Each person has different “buttons” to push that influence how they respond – emotional, psychographic, and personal triggers that motivate them. Everyone responds differently to certain images or messages. The most advanced digital marketing platforms, based on programmatic technology, can take the entire rich human web of behavior, experience, and personality into account to automatically target individuals with highly-relevant ads at the most opportune moments. This session will show how to combine the best of programmatic – AI, big data and machine learning – with a highly detailed understanding of consumer behavior to do ‘extreme ad personalization’. Learn clear strategies and actionable best practices to get such extreme personalization right. Well see case studies and live demos of this concept, sharing brand marketer ads targeted by personality, location, interests, demographics and context. You’ll get to witness data points illustrating the effectiveness of extreme-targeted ads compared to blunt-targeted ads. - See more at: http://sdama.org/events/2015-art-of-marketing-conference/#session-details

1. MARK TORRANCE, CHIEF TECHNOLOGY OFFICER
Extreme Personalization
in Digital Marketing

2. ARTIFICIAL
INTELLIGENCE
Ad Tech Company (public, 1000 staff, $450mm rev)
Customers = 1500+ Agencies and Large Marketers
Minimum spend = $10,000
Channels: Display, Video, Mobile, Facebook
Direct Response + Brand
Retargeting + Prospecting
Dynamic Creative + Site Personalization
DSP and/or DMP
SaaS or Managed Service
Automatic performance optimization
Machine learning, “moment scoring”
To bid on 90 billion opptys daily
You design the creative

3. Extreme Personalization
•Most marketing: one-to-many
•Targeting is overly broad
•Marketers AB test just a few options

4. Extreme Personalization
•But people respond differently in different contexts
•Time, Context (site or app), Home/work/travel
•Format (video, display, search), Device
•And they respond to different messages
•Interests, Behaviors, Readiness to be influenced or buy
•“Buttons” to push that influence how they respond
•Emotional, Psychographic, Personal triggers
•Images, Colors

5. Data

6. Data
•Third Party Data
•First Party Data from online (web, app interactions)
•First Party Data from offline (DMP, onboarding)
•Bid Request / Behavioral Data
Demo: Audience Inspector Segments

7. Creative Metadata

8. Creative Metadata
•What do we know about the content of the ad?
•Text, offer, product, messaging
•Colors, images, models
•Indoor / outdoor
•Call to action
•Price shown?
•Appeal on the basis of: what?

9. Opportunity

10. Opportunity
•How many campaigns have many ad concepts per size?
80% of campaigns have 5 or fewer variations
Most common: 1 variation.

11. Opportunity?
If we provided data to prove it would help, could we
get creative teams to produce more varieties, or to
produce modular assets that can be swapped into
templated designs?

12. Targeting

13. Targeting
•Retargeting
•Products or interests browsed or added to cart
•Prospecting
•Contextual category
•Behavioral category of recent activity
•Bid requests, pixels, or third party data

14. Moment Scoring instead of Segment Targeting
S E G M E N T M A R K E T I N G M A R K E T I N G I N T H E M O M E N T
Exclusion approach
with fixed price
Learn what factors work with a dynamic bid to achieve
overarching budget and goal
Women, 18-34
Fixed Bid $1.50
C O N T E X T U A LB E H A V I O R A LD E M O G R A P H I C S
Retargeting
Fixed Bid $6
In the market for a
new luxury sedan
Fixed Bid $3
Age
Gender
Income
Health
Family
Single
D E M O G R A P H I C S
Interest
Purchases
Sites
Time
Weekday
Home
Work
Travel
Device
Geo
Dynamic Bid $
C O N T E X T U A LB E H A V I O R A L

15. Models Learn, CPA Improves

16. Demos
•Demo:
•Globe
•Audience Inspector Moment Scoring
•Cross-Device
•Bid Landscape

17. Creative

18. How can we know more about each creative?
•Metadata while authoring
•Weak standards, time consuming, inconsistent
•Ads auditing feedback from Google + others
•Better targeting models when we use these Verticals
•Just ask people
•Amazon Mechanical Turk

21. Ad Attributes
•Offer
•Value Proposition
•Product shown in-use
•Product Position
•Text Position
•Visual
•Emotional
•Personal Connection
•Call to Action

22. Example Ads w/ Attributes: Vonage
Bg = Orange
Electronics or “other”
Logo present, lower-left
Effective = 4 or 5
Product = white
BG = white
Electronics or energy
Logo present, lower-left
Big text
Value, practical
Effective = 4 or 3
Bg = yellow or gray
Category = energy
Logo present, lower-left
Value
Indoors
Effective = 3 or 4

23. Example Ads w/ Attributes: “Clearly” aka Coastal Contacts
Bg = purple | orange | yellow
“other”
Contains products, special offer
Click + shop + get
Clever, fancy, calm, need
Practical, safe, value
Product = yellow | orange
Effective = 5 or 3
Age = young_adult
Bg = gray | green | yellow
Category = clothing | pharma
Contains people, products
Logo present, on right
Product shown in_use
Gender female
Effective = 3
Bg = gray | white
Clothing, products
Logo present, lower-right
“shop”, pricing, special offer
Sale, value, practical, trustworthy
Quality
Product = blue
Effective = 3 | 4 | 5

24. Creative Strategies
http://info.rocketfuel.com/Programmatic-Creative-Guide.html
Insights by vertical into what works in general
Advice about content, placement, colors, imagery

25. Vision

27. Vision
Show the right ad
to the right person
on the right device
at the right time
Understand each ad
Understand each person
Understand cross-device
Understand each moment

28. Vision
• How do your customers fit into 6-8 “personas” not just one?
• What is each persona like (demographics, behavior)?
• What features of ads cause the customers in each persona to respond?

29. Existing Insights about your customers

30. Future: Audience DNA
Demographics
Purchase Intent
Job / Occupation
Owned ProductsHousehold / Family
Income + Net Worth
Hobbies + Interests
Psychographics + Politics

31. Future: Audience DNA
Own sports car
Gambling
Adventure sports
Female
Kids at home
Time Savers
Male
18-24
Video games
Frozen food
35-44
Investing
Careful considerers
Ads: aspirational,
outdoors
Ads: emotional,
Give back,
Family, price shown
Ads: women,
red
Ads: male,
Fear of missing out,
Price not shown

32. Feature ideas
•Ad bg color x site
•Ad gender x user gender
•Ad attributes x user prev_response attributes

33. Extreme
Personalization
•Advanced Targeting
•Moment Scoring
•Cross-device
•Personalized Creative
•Customer, context,
opportunity
•Data drives iteration

34. Future: Audience DNA
Own sports car
Gambling
Adventure sports
Female
Kids at home
Time Savers
Male
18-24
Video games
Frozen food
35-44
Investing
Careful considerers
Ads: aspirational,
outdoors
Ads: emotional,
Give back,
Family, price shown
Ads: women,
red
Ads: male,
Fear of missing out,
Price not shown
100 2144 85

35. Practical
Agile
Strategies
• You get what you measure
• Use digital to measure what is
working, and for whom
• Iterate and optimize
• Rekindle Mad Men creativity
• Programmatic is not
“emotionless”

36. ARTIFICIAL
INTELLIGENCE
Ad Tech Company (public, 1000 staff, $450mm rev)
Customers = 1500+ Agencies and Large Marketers
Minimum spend = $10,000
Channels: Display, Video, Mobile, Facebook
Direct Response + Brand
Retargeting + Prospecting
Dynamic Creative + Site Personalization
DSP and/or DMP
SaaS or Managed Service
Automatic performance optimization
Machine learning, “moment scoring”
To bid on 90 billion opptys daily
You design the creative

37. QUESTIONS?
Thank You