Sponsor Breakfast Presentation by TruSignal
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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
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Sponsor Breakfast Presentation by 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 2
- 3. Confidential & Proprietary Lots of Data—Most of it useless… 3 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
- 4. Confidential & Proprietary Key Ingredients for Successful Audience Targeting 4 Start with the right raw data Repeatable process with scale and efficiency Portable – usable across multiple touch points
- 5. Confidential & Proprietary Right Data Depends Upon Marketing Goals 5 Days Conversion Convert Existing Demand Weeks Prospecting Generate New Demand Targeted Branding MonthsBuild Awareness and Future Demand TimingCampaignGoalsData Type PROFILE DATA BEHAVIORAL DATA
- 6. Confidential & Proprietary ProfileBehavioral Creating Audiences of Scale and Efficiency 6 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
- 7. Confidential & Proprietary 7 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
- 8. Confidential & Proprietary 8 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!
- 9. Confidential & Proprietary Prebuilt Clusters - Convenient but Inefficient 9 40% Audience Reach! Need to buy 25% of all segments
- 10. Confidential & Proprietary Custom Predictive Audience Model 10 Which data signal matter? How they relate to each other? Relative importance of each signal
- 11. 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 11
- 12. Confidential & Proprietary 12 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%
- 13. Confidential & Proprietary Step 2: Apply Model to Build Scale 13
- 14. Confidential & Proprietary 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
- 15. Confidential & Proprietary 15 Demographic vs TruSignal Comparison 40,000 sample customers Best demographic targeting •Males •Age 35-64 •$150k+ income •College educated
- 16. Confidential & Proprietary 16 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
- 17. Confidential & Proprietary Same Reach – Less Budget $$ 17 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
- 18. 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 18
- 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