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Sponsor Breakfast Presentation by TruSignal


Using Big Data and Audience Expansion Techniques to Find Your Next Customer …

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

Published in Business , Technology
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  • 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


  • 1. Using Big Data and Audience Expansionto Find Your Ideal AudienceJune 21, 2013David Dowhan@daviddowhanPresident, TruSignal
  • 2. Confidential & ProprietaryBig Data Powered Targeting Future is Here…Big Data lets target specific users at scale1:1 digital marketing requires data signalsChallenge—sifting through all of the data todiscover the right signals for your specific goals2
  • 3. Confidential & ProprietaryLots of Data—Most of it useless…3Profile DataDemographicsPast PurchasesFinancialsGeographyHobbiesCensusAssetsHouseholdBehavioralIntendersSearch TermsContextualWeb NavigationRetargeting“In-Market”Social LikesTechnographicTime of DayDevice TypeDevice SpeedDay of WeekSite IndexOwnership1stParty2ndParty3rdPartyAudienceSegmentsClustersGenetic Algo’sLookalikesAct-alikes
  • 4. Confidential & ProprietaryKey Ingredients for Successful Audience Targeting4Start with the right raw dataRepeatable process with scale and efficiencyPortable – usable across multiple touch points
  • 5. Confidential & ProprietaryRight Data Depends Upon Marketing Goals5DaysConversionConvert ExistingDemandWeeksProspectingGenerateNew DemandTargeted BrandingMonthsBuild Awarenessand Future DemandTimingCampaignGoalsData TypePROFILEDATABEHAVIORALDATA
  • 6. Confidential & ProprietaryProfileBehavioralCreating Audiences of Scale and Efficiency6Raw Data Points Audiences•Demographics•Financial•Lifestyle•Interests•CensusHigh Scale, Low Signal•Search Term•Web navigation•Contextual site visit•Lifestage event•Visited your websiteLow Scale, High SignalAct-alike ModelsInferred SegmentsIntendersBoost scale, without losing signalLookalike ModelsSegment CombinationsPrebuilt ClustersBoost signal, without losing scaleCombineExpand
  • 7. Confidential & Proprietary7Case Study: Improve Targeting EfficiencyBrandingProspectingConvertingTargeting For Efficiency65%Improvementin targetingaccuracyLarge Scale20M‣ Luxury auto brand launch‣ RTB, premium, video, and social‣ Existing demo targeting‣ Age 35-64‣ Income $150k+‣ Males‣ College Educated
  • 8. Confidential & Proprietary8Old Way—Scale and Accuracy ProblemsAge: 36-64126,000,000UsersTotalPopulationGender: Male115,000,000UsersEducation: College37,000,000UsersIncome: $150+32,000,000UsersSmallScale!
  • 9. Confidential & ProprietaryPrebuilt Clusters - Convenient but Inefficient940%AudienceReach!Need to buy 25%of all segments
  • 10. Confidential & ProprietaryCustom Predictive Audience Model10Which data signal matter?How they relate to each other?Relative importance of each signal
  • 11. Confidential & ProprietaryStep 1: Find the Right DataAnalyzed owners of : Audi A6, BMW 5, Infiniti M, Cadillac XTS,Jaguar XF, Lincoln MKS with 40 sources of offline profile data11
  • 12. Confidential & Proprietary12Step 1: Find the Right DataSelect 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 owner124 predictive factors from 10 different data setsContribution by Data Category4%3%3%2%23%21%21%19%7%9%4%
  • 13. Confidential & ProprietaryStep 2: Apply Model to Build Scale13
  • 14. Confidential & ProprietaryPremiumPublishersActivate customaudiences directlywithin DoubleClickfor PublishersTrading DesksAD AGENCYINDEPENDENTStep 3: Port Audience to Media Access PointsAd NetworksDSP’sTop PortalsRTB ExchangesVideoAudience POOLNews feedMobileDoubleClick forPublishers
  • 15. Confidential & Proprietary15Demographic vs TruSignal Comparison40,000 sample customersBest demographictargeting•Males•Age 35-64•$150k+ income•College educated
  • 16. Confidential & Proprietary16Same Scale – Bigger ReachScale Reach EfficiencyCriteriaTargetedAudience% ActualCustomersEfficiencyGender, Age, Income, Education 8,300,000 26%3.0TruSignal 8thPercentile 8,000,000 43% 5.4For the same impression levels, TruSignalimproved the total audience reach by 65%Hold Scale Constant
  • 17. Confidential & ProprietarySame Reach – Less Budget $$17Scale Reach EfficiencyCriteriaTargetedAudience% ActualCustomersEfficiencyGender, Age, Education 25,700,000 40% 1.8TruSignal 7th %tile 7,000,000 40% 5.7To achieve the same reach as demo targeting,TruSignal only needs to use 27% of the impressions!Hold Reach Constant
  • 18. Confidential & ProprietaryKey Take AwaysBig Data powers more efficient technique that moveway beyond demographics and pre-built clustersCampaign objectives determine appropriate raw dataand audience development methodologyA well-executed custom approach can produce ascalable, portable, and efficient audience18
  • 19. Using Big Data and Audience Expansionto Find Your Ideal AudienceJune 21, 2013David Dowhan@daviddowhanPresident, TruSignal