New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Data equals dollars
1. Audience data = dollars How can you use your data to grow your revenue? Presented by Yali Sassoon, Principle, Keplar, LLP Twitter Hashtag: #OXwebinar
2. Audience data Why should publishers collect and develop audience data? How can publishers collect and develop audience data? How can OpenX Enterprise help? 9/29/11 Audience data 1 Don’t miss! Next OpenX Enterprise Demo Oct 5th at 10 am PST. Go to OpenX.com to register
3. Audience data is valuable 9/29/11 2 Audience data CAGR 2009-11f 5% 39% Where the growth is Source: The Jordan Edmiston Group IAB Mix 2010 report (Sept 2010)
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7. How do I develop my audience data? Start with what you already know of your audience Take a hypothesis-led, iterative approach to developing your audience data Work in partnership with your advertisers Use 3rd party data to develop your 1st party data 9/29/11 Audience data 5
8. What do you already know about your audience? 9/29/11 Audience data 6 Tacit & anecdotal knowledge Formal, quantitative knowledge CRM BI Web analytics
11. Index against a 3rd party vertically-focused metric (e.g. propensity to buy a luxury car)Hypothesis Design Analyse Test
12. Run the test: run campaign against two segments (1/2) 9/29/11 Audience data 9 1. Segments defined in ad server Hypothesis Design 1. Consumer enters website 4. Consumer shown ad Publisher website Analyse Test My Website Consumer Web browser Cookie ID Ad creative 3. Ad server determines if user is in target or control segments and serves ad accordingly 2. Ad request made to ad server, includes web page, visitor geoIP transmitted to ad server Ad server Publisher Systems
13. Run the test: run campaign against two segments (2/2) 9/29/11 Audience data 10 2. Segments defined in separate system Hypothesis Design 7. Consumer shown ad 1. Consumer enters website 4. Cookie identified on consumer’s web browser Publisher website Analyse Test 3. Cookie dropped on browser to identify the consumer as belonging to particular audience segment My Website Consumer Web browser Ad creative Cookie ID Cookie ID 6. Ad server responds by targeting ad (based on cookie ID) and sending the appropriate creative to the website 2. Publisher system (e.g. CRM) determines if consumer belongs in a particular target or control segment Ad server CRM 5. Ad request made to ad server, includes cookie ID Publisher Systems
16. Consistent differences? (E.g. across different ad campaigns / parts of the site / geographies / other variables?)Hypothesis Design Analyse Test
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19. Are there ways we can expand its size? (E.g. introduce broader criteria, or look for similar characteristics elsewhere?)Hypothesis Design Analyse Test
20. Work in partnership with advertisers throughout the process 9/29/11 Audience data 13 What audience segments matter to your advertisers? What test results would impress an advertiser? Hypothesis Design Analyse Test Are the results compelling for your advertisers?
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Editor's Notes
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”
