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Part 2 of NexTargeting Webinar: Building Audience Insights


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  • Painting a picture is traditionally done through an additive approach but like and gourmet chef knows reductionism produces fascinating results. We need to take a reductionist approach because we have to start by looking at the entire internet audience. Through our POE engine we determine which characteristics best define a customer. Bringing together these customer level characteristics allows us to “reduce” the entire internet population into your customer base. 02/16/11
  • Once you have the correct color palette selected (or in our case attributes that define a customer) you need to determine who to use the colors appropriately across the canvas. With an audience not all customers are created equal. Hence we need to group our attribute set into different groupings just like we might paint a house on the left side of a canvas with one grouping of colors and a lake on the right side of the canvas with a different grouping of the same color palette. 02/16/11
  • Transcript

    • 1. Building Audience Insights part 2 The Progression to Findings Presented by Marc Rossen Director of Media Strategy and Analytics, [x+1] February 10, 2011
    • 2.
      • Before we start:
      • System requirements
      • Please press *6 to mute your phones
      If you use this OS You can use these browsers Windows XP SP2 Internet Explorer 8, 7 or 6 SP2 Windows Vista Internet Explorer 8 or 7, Firefox 3.5.7, Firefox 3.0.6, Safari 4.0.4, Safari 3.2.2 Mac OS X v10.5.8 Safari 4.0.4 Full system requirements available at
    • 3.
      • Our Agenda Today
    • 4. Agenda Webinar #1
      • Understanding the value of data and building a value framework that leads to actionable business results
        • Actionable data
          • The role of context
        • Findings
        • Insight
        • Action
      • Use our new framework to build an effective audience analysis
        • An approach to building audience insights
        • Looking at the detail to derive tactical value
      Webinar #2
    • 5. Review from Webinar #1
      • Don’t get overwhelmed by the data you have available
      • Take stock of your data set
      • Bring context with your data by looking at all actionable data together
      • Paint a picture to define the problem you are solving
      • Assess your findings, derive insight, and define your actions
      Take Stock, Assess, Derive, and Define
    • 6. Step one entails building our base set of actionable data Actionable Data Finding Insight Action
    • 7. Putting our framework in action
      • Taking a step back, we now understand:
        • The value of using multiple data points for context
        • How we build a value framework to derive insights that lead to action to improve performance
      • We are now ready to put our framework into action to build audience insights
    • 8. Audience insights are built by defining what makes your customers stand out
      • What we are left with is defining characteristics that can paint a picture of who your customers are
      Your Customers The Internet Audience
    • 9. These characteristics allow us to understand why performance trended the way it did and how it can be repeated
      • Our CPA trend continually declined throughout the campaign as we optimized based on audience insights
      Source: DFP Audience Optimization Audience Optimization
    • 10.
      • We then find audience characteristics that best describe your best customers
      DMA We find actionable data by isolating the attributes that drive performance Attribute Tool Set Income Class Income Producing Assets (IPA) US Region Ideal customers defined by:
      • GeoData
      • Internet connection speed
      • Internet browser
      • Computer Operating System
      • Income Producing Assets
      • Income Class
      • Social Life Stages
      • Presence of Children
      • Home Owner
      • Gender
    • 11. By aggregating data into multiple points we bring performance into context
      • We use multiple characteristics to define individual groups of your customers as each segment represents a different affinity to your product
      People who live in Massachusetts, Florida, Georgia, South Carolina, California, and Pennsylvania Elite, High, and Moderate IPA Wealthy Income Class
    • 12. Audience Segments aggregate multiple characteristics The client has 36 discreet audience segments across GEO, Income, and IPA Audience Segment
    • 13.
      • Moving from Actionable Data
      • to Findings
    • 14. Moving from actionable data to findings
      • We have identified the right data attributes
      • We have brought context to the data attributes by applying multivariate approach
      • Now we dive into each data attribute to indentify why it drove performance thus identifying the findings which will eventually lead us to insight
    • 15. Step two entails using our data to build findings Actionable Data Finding Insight Action
    • 16. Looking at the attribute level performance we can better understand what drove performance DMA Income Class Income Producing Assets (IPA) US Region
    • 17. Variances exist in state performance however regional trends are not apparent
      • Midwest and midatlantic states are not well represented for this audience
    • 18. Urban centers drive performance
      • The client’s customers seem to be situated in urban geographies
    • 19. Income trends middle to upper income
      • It’s clear that our client is attracting wealthier individuals
    • 20. IPA trends are strong to middle and upper audiences
      • While the client’s audience indexes high with the $250K + audience, the $50-$100K audience produced the highest index.
      • This suggests that the $50-100K audience is the sweet spot for customer acquisition
    • 21. The campaign reached an older population
      • 46-55 age brackets represent the largest audience for the client
    • 22. Customers are predominantly home owners
      • The client’s audience greatly over indexes to home owners
    • 23. There is no strong evidence whether children are present in the audiences home
      • “ With Children” slightly skews positive however this is not strong enough to conclude whether children are present in the home
    • 24.
      • Insight to Action
    • 25. Moving from findings to insight and action
      • We have identified the right data attributes
      • We have brought context to the data attributes by applying multivariate approach
      • We have identified findings that describe certain data attributes drove performance
      • Now we will take our findings, translate them to insights, which lead us to our final step of action
    • 26. Step three entails using our data to take insights to actions Actionable Data Finding Insight Action
    • 27. Step Three – Insights that drive action
      • By taking stock of your findings you can derive actionable insights
      Finding Insight Action State and DMA level data drives campaign performance Coastal states and urban core DMA drove the most significant lift Future campaigns should focus targeting older consumers on coastal locations and urban cores with middle upper wealth class CPA decreased for Income and IPA data Middle Upper to Upper class consumers predominantly drove performance The campaign reached older people who look like home owners Older homeowners were reached by the campaign targeting
    • 28.
      • Takeaways for you today
    • 29. Three things you can do today
      • Take a campaign that recently ended and bring all your data into a spreadsheet
      • Define your actionable data:
        • What values changed dramatically over the life of the campaign?
          • Conversion rates? CPA? Frequency? CPM?
      • Build your value pyramid
        • Take your actionable data and bring context to it to assess findings
        • Derive your insights from findings
        • Define the actions your would have taken
      • Build a case study of your work and socialize it within your ___ organization
    • 30. Reach out. Learn more.
    • 31.
      • Thank you