Data equals dollars


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  • In this talk we’re going to look at audience data from the point of view of web publishers. In the first part, we’ll discuss why it is we believe publishers should be look at developing audience data. Ultimately, we believe this is a big revenue opportunity for publishers.In the second part, we’ll look at the practical steps publishers can take to develop their audience data. We’ll really be looking at how this works at a conceptual level.In the third part, Matt Davidson at OpenX will outline some of the features in OpenX Enterprise which provide publishers with a powerful toolset for developing their audience data. He may also cut in earlier in the talk, if it makes sense to talk about specific features in relation to specific steps we’ll be discussing.
  • Data = dollars, and the value of data is growing. Data is playing an increasingly important role in the buying decisions that advertisers are making.For many years: advertisers used demographic and behavioraldata to buy audience segmentsToday, ad exchanges and DSPs make it possible for advertisers to buy highly targeted audience on impression-by-impression basis. This has accelerated the trend to using data to drive ad buying decisions.We see in the graph shown (these are IAB numbers for ad spend in the US) that much of the growth in value in online ad spend is advertisers spending additional ad dollars on specific audience segments. This spend is not at the expense of current spend on site-specific buys. It is additional spend. New intermediaries have grown up to take advantage of the opportunity presented by the growing value of audience data. e.g. Audience Science (behavioural targeting co), BlueKai (data exchange)However, publishers have been slower to realize these opportunities.
  • Contrast with the case of intermediaries in the advertising value chain who develop data assets butDo not have a direct relationship with their audience -> no understanding by consumerDoes not understand consumer before raw clickstream data that collects on usersExample: Amazon recommendation system
  • Developing audience data is an art, not a science.The steps that a two different publishers will go through might look very different becauseThere are so many possible ways of segmenting audienceDifferent methods are appropriate to different sites and different types of advertiserThe audience data a publisher can collect varies from publisher to publisher e.g. A travel site gets a very different view of its users than a newspaper or banking siteAs a result, we’re going to outline a high level process that should provide a guide to any type of publisher, and highlight elements of best practice
  • In general, publishers know a great deal (more than they might expect) about their own audience. Indeed, being a successful publisher means understanding your audience and successfully delivering them information or other products and services that matters to themUse this knowledge as a starting point. People often neglect tacit and anecdotal knowledge, but this is sometimes incredibly valuable and a great place to start. Examples: a financial publisher might know that they have two very different sorts of reader – one who belongs to a financial institutions, and people who invest just their own or their families money, and whilst they have a lot of cross over, there are types of content that only interest one of the two groupsPublishers will also have formal, quantiative knowledge, typically stored in one or more databases. When starting, publishers should be expansive and willing to consider as wide a range of sources of knowledge on their userbase, to give them the best starting point to develop their audience data as possible
  • Based on the knowledge a publisher already has (formal and informal, qualitative and quantitative), the publisher should make a hypothesis about the behaviour of different segments of its users.
  • Once you have your hypothesis, you need to test it. To do this, the first step is to define an audience segment that corresponds to the group of people the hypothesis relates to. We’ll call this the “test segment” You’ll then need to define a control group. This will be the group against which you compare the behaviour / response rates of the test segment against. We’ll call this the “control segment”You’ll decide on one or a selection of ad campaigns to test the hypothesis againstLastly, you’ll need to decide on a way to measure the response of the two different segments. This will be the results you compare.This is the most important step! Measurement is keyTempting to concentrate on click through rates, but this is a very limited way to measure the effectiveness of a targetting segmentSome other possibilities include indexing the segment against a vertically focused metric (e.g. Likelihood to purchase a luxury car). This requires a 3rd party source of data (more on this later). One example source: comScore
  • Run the tests! Setup the segments in your ad server. Setup the campaigns. Run the campaigns and collect the relevant data
  • Run the tests! Setup the segments in your ad server. Setup the campaigns. Run the campaigns and collect the relevant data
  • Other examples: travel site: divide customer base by those who are interested in the deal that “saves the most money” vs those that want to “spend the least” on a holiday package
  • You want to be able to offer advertisers large, high value audience segments. So when iterating your audience segments, you need to think about ways to grow its size (who else on your sites might fit in them) and how to make them more valuable. There is a tension between these two, and that is one of the things that makes developing audience data an art
  • Ultimately, the segments that publishers develop will be sold to advertisers, so it is important that the publishers keep this in mind when developing their audience dataThis impacts the complete, iterative, data-development cycleE.g. Hypotheses should relate to audience behaviours that advertisers are interested in / valueTests should be designed around audience segments that a publisher wants to sell to advertisers Results should be analysed using metrics advertisers are committed to. This might mean using “uplift in brand awareness / purchase intent” rather than more traditional, but cruder measures like “click throughs”
  • Data equals dollars

    1. 1. Audience data = dollars<br />How can you use your data to grow your revenue?<br /> Presented by Yali Sassoon, Principle, Keplar, LLP<br />Twitter Hashtag: #OXwebinar<br />
    2. 2. Audience data<br />Why should publishers collect and develop audience data?<br />How can publishers collect and develop audience data?<br />How can OpenX Enterprise help?<br />9/29/11<br />Audience data<br />1<br />Don’t miss! <br />Next OpenX Enterprise Demo Oct 5th at 10 am PST. Go to to register<br />
    3. 3. Audience data is valuable<br />9/29/11<br />2<br />Audience data<br />CAGR<br />2009-11f<br />5%<br />39%<br />Where the growth is<br />Source: The Jordan Edmiston Group IAB Mix 2010 report (Sept 2010)<br />
    4. 4. Publishers should leverage audience data<br />9/29/11<br />3<br />Direct relationship<br /><ul><li>Audience publisher relationship is direct
    5. 5. Audiences are comfortable with data collection from publishers</li></ul>+<br />Knowledge of audience<br /><ul><li>Audience insights drive product development already…lots of experience to draw on</li></ul>Audience data<br />
    6. 6. Use audience data to grow revenue<br />9/29/11<br />4<br />Grow <br />sell-through<br /> rates<br /><ul><li>Make ‘run-of’ or secondary inventory more targeted and attractive</li></ul>Grow <br />CPMs<br /><ul><li>Better response rates means advertisers are willing to pay more</li></ul>Audience data<br />
    7. 7. How do I develop my audience data?<br />Start with what you already know of your audience<br />Take a hypothesis-led, iterative approach to developing your audience data<br />Work in partnership with your advertisers<br />Use 3rd party data to develop your 1st party data<br />9/29/11<br />Audience data<br />5<br />
    8. 8. What do you already know about your audience?<br />9/29/11<br />Audience data<br />6<br />Tacit & anecdotal knowledge<br />Formal, quantitative knowledge<br />CRM<br />BI<br />Web analytics<br />
    9. 9. Examples:<br /><ul><li>People who spend time reading the financial section after 11am are more likely to be wealthy senior business people or wealthy individuals</li></ul>Form an initial hypothesis<br />9/29/11<br />Audience data<br />7<br />Hypothesis<br />Design<br />Analyse<br />Test<br /><ul><li>Customers who are referred to an auto site from an affiliate site are more cost conscious than those referred from other sources (e.g. review sites, car magazines, search)</li></li></ul><li>Design a way to test your hypothesis<br />9/29/11<br />Audience data<br />8<br />Use your data to define an audience segment (“test segment”)<br />Define a control group<br />Decide on one or more ad campaigns to test<br />Decide how to measure the response of the test segment against the control segment eg:<br /><ul><li>Click through rate(Limited)
    10. 10. Brand impact / awareness
    11. 11. Index against a 3rd party vertically-focused metric (e.g. propensity to buy a luxury car)</li></ul>Hypothesis<br />Design<br />Analyse<br />Test<br />
    12. 12. Run the test: run campaign against two segments (1/2)<br />9/29/11<br />Audience data<br />9<br />1. Segments defined in ad server<br />Hypothesis<br />Design<br />1. Consumer enters website<br />4. Consumer shown ad<br />Publisher website<br />Analyse<br />Test<br />My Website<br />Consumer<br />Web browser<br />Cookie ID<br />Ad creative<br />3. Ad server determines if user is in target or control segments and serves ad accordingly<br />2. Ad request made to ad server, includes web page, visitor geoIP transmitted to ad server<br />Ad server<br />Publisher Systems<br />
    13. 13. Run the test: run campaign against two segments (2/2)<br />9/29/11<br />Audience data<br />10<br />2. Segments defined in separate system<br />Hypothesis<br />Design<br />7. Consumer shown ad<br />1. Consumer enters website<br />4. Cookie identified on consumer’s web browser<br />Publisher website<br />Analyse<br />Test<br />3. Cookie dropped on browser to identify the consumer as belonging to particular audience segment<br />My Website<br />Consumer<br />Web browser<br />Ad creative<br />Cookie ID<br />Cookie ID<br />6. Ad server responds by targeting ad (based on cookie ID) and sending the appropriate creative to the website<br />2. Publisher system (e.g. CRM) determines if consumer belongs in a particular target or control segment<br />Ad server<br />CRM<br />5. Ad request made to ad server, includes cookie ID<br />Publisher Systems<br />
    14. 14. Analyse the results<br />9/29/11<br />Audience data<br />11<br /><ul><li>How did the audience segment perform relative to the control group?
    15. 15. Significant difference in response rate / brand awareness / index relative to vertical indices?
    16. 16. Consistent differences? (E.g. across different ad campaigns / parts of the site / geographies / other variables?)</li></ul>Hypothesis<br />Design<br />Analyse<br />Test<br />
    17. 17. <ul><li>How valuable is the segment to advertisers?
    18. 18. Are there ways to grow its value e.g. introduce new criteria?</li></ul>Iterate the segment -> develop new hypotheses<br />9/29/11<br />Audience data<br />12<br /><ul><li>How big is the segment?
    19. 19. Are there ways we can expand its size? (E.g. introduce broader criteria, or look for similar characteristics elsewhere?)</li></ul>Hypothesis<br />Design<br />Analyse<br />Test<br />
    20. 20. Work in partnership with advertisers throughout the process<br />9/29/11<br />Audience data<br />13<br />What audience segments matter to your advertisers?<br />What test results would impress an advertiser?<br />Hypothesis<br />Design<br />Analyse<br />Test<br />Are the results compelling for your advertisers?<br />
    21. 21. Use 3rd party data to augment 1st party data<br />Identify more highly targeted segments<br />Users that match criteria defined using combination of 1st and 3rd party data e.g. Regular visitors to my site (1st party) AND 21-35 year old female (3rd party)<br />9/29/11<br />Audience data<br />14<br /><ul><li>Validate segments created using 1st party data
    22. 22. Index segment generated using your 1st party data against a 3rd party source trusted by your advertiser to demonstrate the value of your new segment</li></li></ul><li>Live Demo<br />Audience Segmentation<br />