sponsor breakfast presentation by trusignal
DESCRIPTION
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, TruSignalTRANSCRIPT
Using Big Data and Audience Expansion to Find Your Ideal AudienceJune 21, 2013
David Dowhan@daviddowhanPresident, TruSignal
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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Confidential & Proprietary
Lots of Data—Most of it useless…
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Profile Data
DemographicsPast Purchases
FinancialsGeography
HobbiesCensusAssets
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
Confidential & Proprietary
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
Confidential & Proprietary
Right Data Depends Upon Marketing Goals
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DaysConversion
Convert Existing Demand
Weeks
Prospecting
Generate New Demand
Targeted BrandingMonthsBuild Awareness
and Future Demand
TimingCampaignGoalsData Type
PROFILEDATA
BEHAVIORALDATA
Confidential & ProprietaryP
rofil
eB
ehav
iora
lCreating 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
Confidential & Proprietary
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Case Study: Improve Targeting Efficiency
Branding
Prospecting
Converting
Targeting For Efficiency
65%Improvement in targeting accuracy
Large Scale20M
‣ Luxury auto brand launch‣ RTB, premium, video, and social‣ Existing demo targeting
‣ Age 35-64‣ Income $150k+‣ Males‣ College Educated
Confidential & Proprietary
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Old Way—Scale and Accuracy Problems
Age: 36-64
126,000,000Users
TotalPopulation
Gender: Male
115,000,000Users
Education: College
37,000,000 Users
Income: $150+
32,000,000Users
SmallScale!
Confidential & Proprietary
Prebuilt Clusters - Convenient but Inefficient
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40% Audience Reach!
Need to buy 25% of all segments
Confidential & Proprietary
Custom Predictive Audience Model
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Which data signal matter?
How they relate to each other?
Relative importance of each signal
Confidential & Proprietary
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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Confidential & Proprietary
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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%23%
21%21%
21%21%
19%19%
7%7%
9%9%
4%
Confidential & Proprietary
Step 2: Apply Model to Build Scale
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Confidential & Proprietary
Premium Publishers
Activate custom audiences directly within DoubleClick
for Publishers
Trading DesksAD AGENCY
INDEPENDENT
Step 3: Port Audience to Media Access Points
Ad Networks
DSP’s
Top Portals
RTB Exchanges
Video
Audience POOL
News feedMobile
DoubleClick for Publishers
Confidential & Proprietary
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Demographic vs TruSignal Comparison
40,000 sample customers
Best demographic targeting
•Males•Age 35-64•$150k+ income•College educated
Confidential & Proprietary
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Same Scale – Bigger Reach
Scale Reach Efficiency
CriteriaTargeted 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
Confidential & Proprietary
Same Reach – Less Budget $$
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Scale Reach Efficiency
CriteriaTargeted Audience
% Actual Customers
Efficiency
Gender, Age, Education 25,700,000 40% 1.8TruSignal 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
Confidential & Proprietary
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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Using Big Data and Audience Expansion to Find Your Ideal AudienceJune 21, 2013
David Dowhan@daviddowhanPresident, TruSignal