We use cookies. You have options. Cookies help us keep the site running smoothly and inform some of our advertising, but if you’d like to make adjustments, you can visit our Cookie Notice page for more information.
We’d like to use cookies on your device. Cookies help us keep the site running smoothly and inform some of our advertising, but how we use them is entirely up to you. Accept our recommended settings or customise them to your wishes.

Attribution Myths vs Reality: Part 1) Statistical Limits

The "Big Data" revolution is at its root much more about the power of statistical modeling than it is about data volume. The incredible decrease in the cost of data storage and simultaneous increases in processing power and techniques have allowed industries, the NSA, and marketers to aggregate massive amounts of data stitched together from disparate sources in ways that allow statistics software to find interesting/unexpected correlations that are sometimes actionable. That last sentence, properly unpacked, explains why statistical modeling is so valuable in auction-based advertising, and why it generally fails to answer the critical questions in attribution. In the case of auction-based advertising we want statistics to provide a critical input: historically, what is a click or impression worth given the context (in paid search that might be the keyword; in display, the page and domain on which it's served), the device, the geography, the time of day and day of week, the proximity of the person to a physical location, the user's past behavior, etc. Quality paid search platforms look beyond the keyword to understand characteristics of the keyword: categories and subcategories, brands, themes (like 'discount' or 'promo' or 'seasonal' terms), specificity, landing page types, length, etc. In the world of Enhanced Campaigns we can also look beyond the geography to characteristics of the geography: urban vs rural vs suburban, population density, average household income, etc. We ask statistics to find correlations between characteristics and combinations of characteristics that are most predictive of traffic value and blend that with the data we have about the specific ad and tell us what this advertiser should be willing to spend on this user in this context. We marry that historical insight with current business intelligence that could affect the calculated value: news events, promotions, inventory levels, our current best understanding of the diminishing returns curve, etc. Most importantly, this analysis gives us a clear-as-a-bell action to take that impacts the P&L: Bid X right now!. Well constructed programs with smart use of characteristic tagging and powerful algorithms will generate more revenue for the advertiser within their efficiency needs than will poorly tagged programs with low-rent algorithms. Statistics identifies correlations well, and correlations tell us what we want and need to understand in auction-based advertising bidding decisions. Statistics matters when you can apply the results of those calculations to a real world problem and change outcomes as a result. Attribution data all too often does not exhibit either of these traits. The fact that someone who has first visited the site through a display ad and then comes to the site through a brand search ad converts better than someone who just visits through a display ad leads to what action? Bid more for brand search ads if the user has been to your site through a display ad? Oh, you're already at the top of the page. Put a banner on the landing page for those visiting through a display ad that reads: "Please leave the website, go to your search bar and search for our brand name!" so that we increase conversions? We can't really force people down a particular attribution path. The problem here is that correlations do not answer the need in cross-channel attribution. We want to know which ads cause users to buy from us, to request more information, to sign up for a newsletter, to download an app, to engage with our brand in ways that make us money now or later. Correlations don't necessarily tell us that.

From Wikipedia

Brand ads and brand organic listings often (but not always) fail to generate incremental value, but clicking on them is hugely correlated with success. Same for affiliate links particularly those affiliate ads served after the user has been on your website immediately prior to the affiliate interaction and immediately before the sale. Ads to your Facebook fans, emails, and retargeted display and search ads will often be highly correlated with success for reasons that have little to do with causality. Your Facebook fans are your loyal customers, as are your email subscribers, as are some fraction of the people retargeted with display, and now, search ads. Many past customers will buy from you again for reasons of satisfaction with your brand and unrelated to subsequent advertisement. This is not to say advertising to them is a bad idea, it's a great idea. It's just that you have to understand how much lift the advertising creates over expected repeat purchase rates. The correlation calculations cannot tease that out absent control testing, and will over-credit the influence of these channels left to their own devices. What is also important to understand is that even when the model is smart and as well tuned as possible based on control test results (which we can do at RKG), in fact, even if you had a system that gave you 100% certainty of what each advertising vehicle does for you, it is often the case the number of levers that advertisers can pull to optimize media mix meaningfully is more limited than many realize, and there are often better ways of understanding and using those controls than what attribution provides. More on that in Part 2: Limited Controls Would love to get feedback from folks on this topics. Similar experiences? Different ones?
Join the Discussion