Leveraging Big Data to Make Cross-Channel Value Measurement a Reality
Big data has the power to lead marketers to more effective cross-channel value measurement. Today, online advertisers have the ability to track endless amounts of information, from impression data to click and "Like" numbers to time spent on sites and beyond. Despite, or perhaps because of, the mass quantity of data collected, marketers are still unable to reliably measure the value of their digital advertising.
Analysis becomes even more challenging as digital touchpoints proliferate and the ability to gather and measure data at a more granular level grows. In other words, while access to more granular "bigger" data holds promise, marketers are challenged to unlock it.
One of the biggest challenges when it comes to measuring digital return on investment is measuring cross-channel ad value — i.e., understanding the relationship between what happens in one channel and another. The digital advertising industry is at a crossroads as it deals with numerous challenges around the de facto (and ill-suited) standard for measuring cross-channel value: cookies. Mozilla's announcement this year to block third-party cookies, followed by a heated debate around cookies and privacy, reinforces the risk in relying on cookies to value the impact of attention-based, or brand, advertising.
Aside from the privacy debate, cookies fall apart when it comes to tracking across multiple devices, a fatal flaw in today's evermore mobile, multidevice world. As the industry looks for new ways to measure efficacy and assist advertisers in accomplishing their financial goals, marketers must look for new ways to mine the data they already have at their fingertips. In doing so, digital advertisers will move through this crossroads into meaningful cross-channel measurement that leads to optimized ROI across channels and, ultimately, comprehensive bottoms-up digital media mix optimization.
Advertising's big data is the product of tracking the impressions, clicks and conversions happening all the time in an advertiser's digital campaigns across a growing number of touchpoints, from paid search to Twitter to an advertiser's own website. Since most advertisers already have access to this data, it's "just" a matter of mining it properly. So how does one mine it properly?
Advances in multivariate modeling are paving the way for advertisers to mine this existing data in ways that allow them to not only better optimize within a given channel, but also connect the dots between what's happening in one channel and another, bypassing the well-known issues with cookies in addressing cross-channel, cross-device measurement.
Advanced multivariate modeling techniques enable advertisers to use algorithms that help predict future outcomes and solve the complex pricing, placement and allocation issues surrounding everything from paid search to display to Facebook advertising, including taking into account how an ad in one channel is influencing purchases in another.
Tapping big data through advanced modeling techniques to optimize individual channels isn't new. In recent years, this approach has helped advertisers solve complex bidding challenges for individual keywords that number into the tens of millions. Multivariate models factor in historical cost and conversion data, in addition to hundreds of variables influencing performance — everything from day of week and seasonality to weather forecasts and market factors — for every single individual keyword. The resulting insights enable individual bids to be set accordingly to maximize an advertiser's results toward their chosen goal.
This ability to tap the most granular or "atomic level" data to drive dramatic results at the channel level now also has the potential to measure cross-channel value. As with the paid search scenario described above, the requisite data is already at most advertisers’ fingertips. The trick is applying multivariate modeling to uncover the insights hiding within this sea of data.
For example, a goal that still eludes many a digital advertiser today: measuring the cross-channel value of brand advertising (e.g., display). To understand the cross-channel impact of display on paid search, for example, a particular display ad's impression volume becomes yet another variable fed into the multivariate models. With the ability to analyze at the atomic level (individual ad and keyword), the models can identify whether that particular display ad's impression volume is a top variable driving the predicted performance of a particular keyword.
By calculating how much of an impact it has on keyword performance, the models can measure the display ad's cross-channel value per impression (VPI). The ad's VPI then becomes a key input into setting the appropriate bid and overall allocation for a given display ad. Armed with this knowledge, advertisers can go from investing and bidding based on instinct to instead bidding each ad based on its cross-channel value.
As with big data in general, this type of granular analysis, which would be cumbersome if not impossible for an analyst, becomes eminently doable with automated and self-learning models that can measure value over time. Moreover, it can be applied across millions of potential relationships between ads and keywords to uncover the atomic-level, cross-channel relationships that, in sum, drive big value to the advertiser's bottom line.
The same approach can be used to measure the cross-channel value of other traditionally hard-to-measure channels, such as Facebook or Twitter. It can even go the next step to optimizing this data across channels, orchestrating each individual ad and keyword to maximize cross-channel performance and get advertisers to their chosen overarching financial goal.
In the midst of much uncertainty around cookies and continued measurement challenges, digital advertisers can take heart in knowing that there are new, alternative ways to get to cross-channel value measurement that render the cookie debate irrelevant. Indeed, advanced cross-channel modeling at the atomic level taps the big data advertisers already have and conquers the issues with cookies to (finally) get advertisers to the goal of cross-channel, cross-device value measurement and optimization. All of this leads to better pricing and ad allocation decisions and, ultimately, a truly optimized digital media mix.
Robert Cooley is the chief technology officer at OptiMine, a bid optimization software provider.