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Building Audience Insights part 2 The Progression to Findings Presented by Marc Rossen Director of Media Strategy and Analytics, [x+1] February 10, 2011
[object Object],[object Object],[object Object],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  http://office.microsoft.com/en-us/live-meeting/microsoft-off
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Agenda Webinar #1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Webinar #2
Review from Webinar #1 ,[object Object],[object Object],[object Object],[object Object],[object Object],Take Stock, Assess, Derive, and Define
Step one entails building our base set of actionable data Actionable Data Finding Insight Action
Putting our framework in action ,[object Object],[object Object],[object Object],[object Object]
Audience insights are built by defining what makes your customers stand out ,[object Object],Your Customers The Internet Audience
These characteristics allow us to understand why performance trended the way it did and how it can be repeated ,[object Object],Source: DFP  Audience Optimization Audience Optimization
[object Object],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: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
By aggregating data into multiple points we bring performance into context  ,[object Object],People who live in Massachusetts, Florida, Georgia, South Carolina, California, and Pennsylvania Elite, High, and Moderate IPA Wealthy Income Class
Audience Segments aggregate multiple characteristics  The client has 36 discreet audience segments across GEO, Income, and IPA Audience Segment
[object Object],[object Object]
Moving from actionable data to findings ,[object Object],[object Object],[object Object]
Step two entails using our data to build findings Actionable Data Finding Insight Action
Looking at the attribute level performance we can better understand what drove performance DMA Income Class Income Producing Assets (IPA) US Region
Variances exist in state performance however regional trends are not apparent ,[object Object]
Urban centers drive performance ,[object Object]
Income trends middle to upper income ,[object Object]
IPA trends are strong to middle and upper audiences ,[object Object],[object Object]
The campaign reached an older population ,[object Object]
Customers are predominantly home owners  ,[object Object]
There is no strong evidence whether children are present in the audiences home ,[object Object]
[object Object]
Moving from findings to insight and action ,[object Object],[object Object],[object Object],[object Object]
Step three entails using our data to take insights to actions Actionable Data Finding Insight Action
Step Three – Insights that drive action ,[object Object],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
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Three things you can do today ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reach out. Learn more.  ,[object Object],[object Object],[object Object],[object Object],[object Object]
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Part 2 of NexTargeting Webinar: Building Audience Insights

  • 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.
  • 3.
  • 4.
  • 5.
  • 6. Step one entails building our base set of actionable data Actionable Data Finding Insight Action
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. Audience Segments aggregate multiple characteristics The client has 36 discreet audience segments across GEO, Income, and IPA Audience Segment
  • 13.
  • 14.
  • 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.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. Step three entails using our data to take insights to actions Actionable Data Finding Insight Action
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.

Notas del editor

  1. 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
  2. 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