Part 1 of NexTargeting Webinar: Building Audience Insights


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  • 02/16/11
  • Defining your audience in the digital world, specifically within RTB media exchanges is both part art and science. The science part, while complicated, is usually something most companies have down pat through some kind of “whiz bang” statistics. However the art of looking at the data, deciding what is meaningful, and then painting a holistic picture of a customers audience is often too easily overlooked and just not done well. 02/16/11
  • 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
  • Part 1 of NexTargeting Webinar: Building Audience Insights

    1. 1. Building Audience Insights The Data Value Pyramid Presented by Marc Rossen Director of Media Strategy and Analytics, [x+1] February 8, 2011
    2. 2. <ul><li>Before we start: </li></ul><ul><li>System requirements </li></ul><ul><li>Please press *6 to mute your phones </li></ul>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. 3. Marc Rossen [x+1] Director of Media Strategy & Analytics <ul><li>Marc Rossen is a marketing and business innovations strategy expert with more than a decade of experience helping companies innovate in digital marketing and analytics. At [x+1], he overseas thought leadership and innovation for agency and enterprise client relationships focused on audience insights and branding analytics in media. His work helps [x+1] clients transform the influx of data in digital marketing into actionable marketing gains. Before joining [x+1] in 2010, Marc served as Digital Analytics Lead for the Procter & Gamble account at digital agency MediaVest. He also has worked at Publicis Groupe agencies VivaKi and Digitas on web-based analytic solutions, strategy and analytics for major brands in consumer products, automotive and financial services verticals. He started his career working on supply chain, growth and innovation issues for Staples. </li></ul>
    4. 4. <ul><li>Webinar #1: The Data Value Pyramid </li></ul>
    5. 5. Using data to effectively build insights is challenging, but has huge payoffs <ul><li>My goal today is help you understand how to use data effectively to gain actionable insights </li></ul><ul><li>The news is, almost daily, flooded with articles on data and the effects is has on business and our personal lives </li></ul><ul><li>With this data influx a problem arises of how to narrow down your data set to drive insights for your business </li></ul><ul><ul><li>What data is useful? </li></ul></ul><ul><ul><li>How do I simplify the data I have to gain insight? </li></ul></ul><ul><ul><li>How do a paint a picture of my audience? </li></ul></ul>
    6. 6. <ul><li>Our Agenda Today </li></ul>
    7. 7. <ul><li>Understanding the value of data and building a value framework that leads to actionable business results </li></ul><ul><ul><li>Actionable data </li></ul></ul><ul><ul><ul><li>The role of context </li></ul></ul></ul><ul><ul><li>Findings </li></ul></ul><ul><ul><li>Insight </li></ul></ul><ul><ul><li>Action </li></ul></ul><ul><li>Use our new framework to build an effective audience analysis </li></ul><ul><ul><li>An approach to building audience insights </li></ul></ul><ul><ul><li>Looking at the detail to derive tactical value </li></ul></ul>Agenda Webinar #2
    8. 8. <ul><li>Where are we today? </li></ul>
    9. 9. We are still looking at data in a silos <ul><li>If organizations are silo’ed, data is probably silo’ed </li></ul><ul><ul><li>In today’s environment most people are looking at data in a silo without bringing multiple data points together to create a picture </li></ul></ul><ul><li>Context is king </li></ul><ul><ul><li>Therefore we are still struggling with deriving actionable data as we have context for the univariate data points we are looking at </li></ul></ul>
    10. 10. Data without Context… … Is like looking at one corner of a painting The Broader Context Puzzle Piece
    11. 11. How do we paint a picture of our audience? <ul><li>At [x+1], we start with two core beliefs: </li></ul><ul><li>Gather all the facts together to identify the cause </li></ul><ul><li>Any one fact alone can drive you down the wrong path </li></ul>
    12. 12. <ul><ul><li>With all the facts together we can identify the cause </li></ul></ul><ul><ul><li>Any one fact alone can drive you down the wrong path </li></ul></ul>How do we paint a picture of our audience? Metric Tool Set Frequency was up 2x for the week Spend was up 2x September 10 Los Angeles No creative changes Google Adx 2.0 Data without context <ul><li>Frequency </li></ul><ul><li>Spend </li></ul><ul><li>Exchange </li></ul><ul><li>Date </li></ul><ul><li>Geo </li></ul><ul><li>Day of Week </li></ul><ul><li>Etc… </li></ul>
    13. 13. The same applies true to us <ul><li>How do we identify performance changes? </li></ul><ul><li>To often one data point is chosen as the cause, however there is not context in place </li></ul>What happened?
    14. 14. Building a framework: The Data Value Pyramid
    15. 15. Building a value framework is imperative to build insights that lead to action Actionable Data Finding Insight Action Most organizations are here
    16. 16. Organizations are running before walking <ul><li>Most organizations look at a few data points and jump all the way to insight, take some action, and realize after some time performance has not changed. Why is this? </li></ul><ul><li>The [x+1] insight: Develop a roadmap that allows you to slow down and ask….do I have all the facts (e.g., data points) ? </li></ul>By using a value framework you are forced to walk then run. In other words, you now have an analytic framework to asses your data and determine if you have can paint an accurate picture
    17. 17. A simplified example of the framework <ul><li>The [x+1] insight: </li></ul><ul><li>Insights come from actionable data. </li></ul>Actionable Data Finding Insight Action RON CPA = $10 RMKT CPA = $5 RMKT performed 2x better than RON RMKT is more productive marketing tactic Invest more $ into RMKT to drive campaign efficiency
    18. 18. Step One – Actionable Data <ul><li>First, do not get overwhelmed by how much data you have! </li></ul><ul><li>Next, take stock of all the data you have available and make a list </li></ul>What happened on 12/7-12/8?
    19. 19. <ul><ul><li>With all the facts together we can identify the cause </li></ul></ul><ul><ul><li>Any one fact alone can drive you down the wrong path </li></ul></ul>How do we paint a picture of our audience? Metric Tool Set Frequency was flat Spend was up 2x December 7-8 CPM was flat No creative changes CPA down 2x Data without context <ul><li>Frequency </li></ul><ul><li>Spend </li></ul><ul><li>Exchange </li></ul><ul><li>Date </li></ul><ul><li>Geo </li></ul><ul><li>Day of Week </li></ul><ul><li>Etc… </li></ul>
    20. 20. Step two - Findings <ul><li>Once our data set is narrowed to what happened we can dig into univariates to derive findings that bring context </li></ul><ul><li>In our example its clear the data is telling us that our Google exchange inventory more than doubled and that this was caused by a large campaign ending </li></ul>
    21. 21. Step Three – Insights that drive action <ul><li>By taking stock of your findings you can derive actionable insights </li></ul>Finding Insight Action Campaign had no operational issues (no pixel/creative issues) Outside factors effected the campaign No action needs to be taken as the increase in CPA was actually still within client constraints'. Due to lower spend CPA was already well below goal this room was still available to increase CPA while increasing reach. Spend more than doubled while performance metrics dropped significantly Spend caps were not hit despite spend doubling due to supply constraints Exchange based inventory was effected for all exchanges Supply constraints were prevalent for all exchanges
    22. 22. The value of a framework <ul><li>Without a framework it’s common the following findings, Insights, and Actions would have taken hold </li></ul><ul><ul><li>Campaign frequency, spend, and/or inventory sourcing would have been optimized </li></ul></ul><ul><ul><li>Unnecessary time spent diagnosing an issue that was not an issue </li></ul></ul>By using a value framework you are forced to walk then run. By running in this case, nothing needed to be done as campaign CPA was still below goal!
    23. 23. <ul><li>Building Audience Insights </li></ul>
    24. 24. Putting our framework in action <ul><li>Taking a step back, we now understand: </li></ul><ul><ul><li>The value of using multiple data points for context </li></ul></ul><ul><ul><li>How we build a value framework to derive insights that lead to action to improve performance </li></ul></ul><ul><li>We are now ready to put our framework into action to build audience insights </li></ul>
    25. 25. Step one entails building our base set of actionable data Actionable Data Finding Insight Action
    26. 26. Audience insights are built by defining what makes your customers stand out <ul><li>What we are left with is defining characteristics that can paint a picture of who your customers are </li></ul>Your Customers The Internet Audience
    27. 27. These characteristics allow us to understand why performance trended the way it did and how it can be repeated <ul><li>Our CPA trend continually declined throughout the campaign as we optimized based on audience insights </li></ul>Source: DFP Audience Optimization Audience Optimization
    28. 28. <ul><li>We then find audience characteristics that best describe your best customers </li></ul>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: <ul><li>GeoData </li></ul><ul><li>Internet connection speed </li></ul><ul><li>Internet browser </li></ul><ul><li>Computer Operating System </li></ul><ul><li>Income Producing Assets </li></ul><ul><li>Income Class </li></ul><ul><li>Social Life Stages </li></ul><ul><li>Presence of Children </li></ul><ul><li>Home Owner </li></ul><ul><li>Gender </li></ul>
    29. 29. By aggregating data into multiple points we bring performance into context <ul><li>We use multiple characteristics to define individual groups of your customers as each segment represents a different affinity to your product </li></ul>People who live in Massachusetts, Florida, Georgia, South Carolina, California, and Pennsylvania Elite, High, and Moderate IPA Wealthy Income Class
    30. 30. Audience Segments aggregate multiple characteristics The client has 36 discreet audience segments across GEO, Income, and IPA Audience Segment
    31. 31. Key Takeaways <ul><li>Don’t get overwhelmed by the data you have available </li></ul><ul><li>Take stock of your data set </li></ul><ul><li>Bring context with your data by looking at all actionable data together </li></ul><ul><li>Paint a picture to define the problem you are solving </li></ul><ul><li>Assess your findings, derive insight, and define your actions </li></ul>Take Stock, Assess, Derive, and Define
    32. 32. <ul><li>Register for Webinar #2: </li></ul><ul><li>The Progression to Findings </li></ul><ul><li>Thursday, February 10 th </li></ul>