Factoring offline search stimuli into an attribution model.
Credit should be given to media and marketing touch points where credit is due. The fundamental premise behind attribution models and more sophisticated econometric marketing mix models is that influence delivered by a marketing, media, or advertising touch point can often be measured. If it can be measured, then the value of influence in changing attitudes or driving sales can be quantified. Then that data can be modeled and an optimal marketing mix model can be arrived at. It's the Holy Grail of marketing.
I applaud all the marketers who are taking steps to further understand the impact of all the digital touch points under their control. I'll touch on some significant challenges regarding use of even the digital-only touch points shortly because, after all, we assume that digital media is eminently measurable, in comparison to offline media. However, let's look at offline media and marketing first, because digital media and in particular, search engine marketing (SEM), does not exist in a silo. Search behavior is highly sensitive to external stimuli. Searchers are people and people don't search in a vacuum. Something stimulates a high percentage of search behaviors. For commercial searches (those with commercial intent), the stimuli are often marketing and media messages, both offline and online.
Offline search stimuli are very difficult to put into an attribution model. Search itself may be one of your best metrics to determine that stimulus occurred. Not all external search stimuli can be tracked like a mention in the press. Consider for the moment the following events where curiosity stimulates search behavior (immediate, perhaps on a mobile device or lagged search behavior):
Clearly, you'll never be able to capture most of these offline touch points in an attribution model, so they add a significant amount of noise to the data you are doing a deep dive into.
Of course, the promise of digital is that the data points are much more trackable, particularly the paid media touch points, such as paid search clicks and display media impressions and clicks. As a matter of fact, view-through searches, view-through visits, and view-through conversions are an important way to understand and quantify the impact of your display campaigns. As long as you aren't paying a vendor on view-through conversions and you structure experiments to understand incremental lift, you indeed have taken a step in the right direction. It's starting to sound like you can indeed close the loop in paid digital media. But what about earned social media? Facebook and Twitter data are available, but for most advertisers, you'd need a very viral success to break through the statistical noise of the other data in your campaigns.
Clearly, a lot of noise is made about the importance of measuring lift in view-through conversions for paid media, and these days, even earned social media such as forwarded tweets, shared posts, and "likes" are being considered for attribution models and even for marketing mix models. What about paid and organic high-position search engine results page (SERP) impressions? Research has shown that even if the consumer doesn't click on a link in the SERP, they often recall a company/domain being there. More leaky data. Add to the leaky SERP impressions the fact that many ads are being enhanced to make them pop visually (Plus Box, Images, Reviews, Pushpins, Sitelinks, and the "Rich Ads in Search" all contribute to "branding and influence" within the SERP). Search in this case isn't getting full credit.
Also, seasoned SEM managers using good campaign management technology have for years been factoring in the early buy funnel terms in providing influence that may result in a latter (last click) search conversion later on. Therefore, when building any attribution models, understand the often significant limitations to the models and to the transformation of the attribution models into a marketing mix model.
Kevin Lee, Didit cofounder and executive chairman, has been an acknowledged search engine marketing expert since 1995. His years of SEM expertise provide the foundation for Didit's proprietary Maestro search campaign technology. The company's unparalleled results, custom strategies, and client growth have earned it recognition not only among marketers but also as part of the 2007 Inc 500 (No. 137) as well as three-time Deloitte's Fast 500 placement. Kevin's latest book, "Search Engine Advertising" has been widely praised.
Industry leadership includes being a founding board member of SEMPO and its first elected chairman. "The Wall St. Journal," "BusinessWeek," "The New York Times," Bloomberg, CNET, "USA Today," "San Jose Mercury News," and other press quote Kevin regularly. Kevin lectures at leading industry conferences, plus New York, Columbia, Fordham, and Pace universities. Kevin earned his MBA from the Yale School of Management in 1992 and lives in Manhattan with his wife, a New York psychologist and children.
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