Using Big Data and Audience Expansion Techniques to Find Your Next Customer
1:1 audience targeting is a reality today with Big Data enabling marketers to target specific users at scale. However, many marketers are still struggling with the deluge of data and how to best integrate multiple data sources and targeting techniques. This presentation will provide a framework for aligning your campaign objectives with the appropriate data and audience targeting techniques. We will discuss best practices on how Big Data and predictive modeling can create scaled lookalike and act-alike audiences that avoid the scale/accuracy dilemma of basic segment and cluster targeting. Finally, we'll share findings on how one marketer used a lookalike audience to prospect new, high-value customers.
Presenter: David Dowhan, President, TruSignal
1. Using Big Data and Audience Expansion
to Find Your Ideal Audience
June 21, 2013
David Dowhan
@daviddowhan
President, TruSignal
2. Confidential & Proprietary
Big Data Powered Targeting Future is Here…
Big Data lets target specific users at scale
1:1 digital marketing requires data signals
Challenge—sifting through all of the data to
discover the right signals for your specific goals
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Lots of Data—Most of it useless…
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Profile Data
Demographics
Past Purchases
Financials
Geography
Hobbies
Census
Assets
Household
Behavioral
Intenders
Search Terms
Contextual
Web Navigation
Retargeting
“In-Market”
Social Likes
Technographic
Time of Day
Device Type
Device Speed
Day of Week
Site Index
Ownership
1st
Party
2nd
Party
3rd
Party
Audience
Segments
Clusters
Genetic Algo’s
Lookalikes
Act-alikes
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Key Ingredients for Successful Audience Targeting
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Start with the right raw data
Repeatable process with scale and efficiency
Portable – usable across multiple touch points
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Right Data Depends Upon Marketing Goals
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Days
Conversion
Convert Existing
Demand
Weeks
Prospecting
Generate
New Demand
Targeted Branding
MonthsBuild Awareness
and Future Demand
TimingCampaignGoalsData Type
PROFILE
DATA
BEHAVIORAL
DATA
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ProfileBehavioral
Creating Audiences of Scale and Efficiency
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Raw Data Points Audiences
•Demographics
•Financial
•Lifestyle
•Interests
•Census
High Scale, Low Signal
•Search Term
•Web navigation
•Contextual site visit
•Lifestage event
•Visited your website
Low Scale, High Signal
Act-alike Models
Inferred Segments
Intenders
Boost scale, without losing signal
Lookalike Models
Segment Combinations
Prebuilt Clusters
Boost signal, without losing scale
Combine
Expand
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Case Study: Improve Targeting Efficiency
Branding
Prospecting
Converting
Targeting For Efficiency
65%
Improvement
in targeting
accuracy
Large Scale
20M
‣ Luxury auto brand launch
‣ RTB, premium, video, and social
‣ Existing demo targeting
‣ Age 35-64
‣ Income $150k+
‣ Males
‣ College Educated
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Old Way—Scale and Accuracy Problems
Age: 36-64
126,000,000
Users
Total
Population
Gender: Male
115,000,000
Users
Education: College
37,000,000
Users
Income: $150+
32,000,000
Users
Small
Scale!
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Custom Predictive Audience Model
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Which data signal matter?
How they relate to each other?
Relative importance of each signal
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Step 1: Find the Right Data
Analyzed owners of : Audi A6, BMW 5, Infiniti M, Cadillac XTS,
Jaguar XF, Lincoln MKS with 40 sources of offline profile data
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Step 1: Find the Right Data
Select Predictive Factors
•Income
•Household purchasing power
•Age
•Interest: Money making, DIY, finances
•Hobbies: RV Travel, camping, cooking
•Ethnicity
•High mortgage credit
•Credit card balances
•Occupation
•Mail order buyer (prefers Amex)
•Past Purchases: jewelry,
children’s goods
•Pet owner
124 predictive factors from 10 different data sets
Contribution by Data Category
4%
3%
3%
2%
23%
21%
21%
19%
7%
9%
4%
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Premium
Publishers
Activate custom
audiences directly
within DoubleClick
for Publishers
Trading Desks
AD AGENCY
INDEPENDENT
Step 3: Port Audience to Media Access Points
Ad Networks
DSP’s
Top Portals
RTB Exchanges
Video
Audience POOL
News feed
Mobile
DoubleClick for
Publishers
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Demographic vs TruSignal Comparison
40,000 sample customers
Best demographic
targeting
•Males
•Age 35-64
•$150k+ income
•College educated
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Same Scale – Bigger Reach
Scale Reach Efficiency
Criteria
Targeted
Audience
% Actual
Customers
Efficiency
Gender, Age, Income, Education 8,300,000 26%
3.0
TruSignal 8th
Percentile 8,000,000 43% 5.4
For the same impression levels, TruSignal
improved the total audience reach by 65%
Hold Scale Constant
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Same Reach – Less Budget $$
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Scale Reach Efficiency
Criteria
Targeted
Audience
% Actual
Customers
Efficiency
Gender, Age, Education 25,700,000 40% 1.8
TruSignal 7th %tile 7,000,000 40% 5.7
To achieve the same reach as demo targeting,
TruSignal only needs to use 27% of the impressions!
Hold Reach Constant
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Key Take Aways
Big Data powers more efficient technique that move
way beyond demographics and pre-built clusters
Campaign objectives determine appropriate raw data
and audience development methodology
A well-executed custom approach can produce a
scalable, portable, and efficient audience
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19. Using Big Data and Audience Expansion
to Find Your Ideal Audience
June 21, 2013
David Dowhan
@daviddowhan
President, TruSignal
Editor's Notes
Right data means discovering the data that has a lot of signal to help you pinpoint your audience. No amount of fancy math is going to transform the wrong data into a great audience. Different data can help address different marketing challenges.
Unlocking value from Big Data requires a complete alignment of all aspects of execution = All starting with the campaign goal and impacting every aspect of execution.
Profile Data – no single data point has much predictive power. Need to combine data from multiple place to get enough signal. Data point by itself can be very strong signal. How can we extend the scale without diluting the signal too much
So what happens to the accuracy when you use demos alone?
Clusters are a form of unsupervised learning. They are created by identifying a handful of variables that frequency occur together and using these combinations to define a grouping of users. A prebuilt cluster was designed without any reference to your specific population or marketing objective. Each cluster is designed to maximize separation from the other cluster – not to maximize the likeness of your target population. You typically see some correlation with clusters, but there is a lot of wasted ad dollars. The wrong predictive data leads to an inefficient audience targeting solution. Prebuilt clusters are designed to maximize the differnence between the various clusters according to some predefined data criteria, such as income, urbanicity, education levels, et cetera. The data used to define the clusters is predetermined without any regard for your particular customer base or marketing objectives. So you can get good scale and better efficiency, but the underlying data is not a good match for your target audience