September 2, 2016
By Manish Ahuja
By now we have heard plenty of talk about “match rate” – that presumably all-important number that has been positioned to validate the quality of cross-device data. It is supposed to tell us how many audience members we can identify or match across various devices, so we know we are reaching that same person on different screens and can plan and measure campaigns effectively.
But there is a hidden pitfall to this apparently rational approach to cross-screen targeting that is keeping marketers from a far greater opportunity.
Ultimately, by focusing too much on match rates and the deceptive value of instant results generated from a matched set, marketers are actually standing in their own way, preventing the next big wave in targeting cross-screen audiences from taking off: consumer ID targeting.
Proof of ID
As we have come to learn, all IDs are not created equal, nor are all audiences and segments, for that matter.
Many marketers and technology companies judge their partners and vendors based on how well they can match within their ID pool when, instead, the focus should be on the quality of the profile being built of the consumer himself – not just the ability to match IDs.
A few difficult-to-match IDs can negatively affect the overall match rate performance and be very misleading to a marketer trying to evaluate the integrity of a data set.
For years, many companies have sought to tie the most meaningful and actionable data to browser- or application-level IDs to improve and optimize ID targeting.
However, the industry should be looking beyond IDs and devices, and focus more on the consumers themselves.
Targeting an appropriate set of consumers, using the best possible data is the right thing to do.
But in our sometimes-singular quest to match a maximum number of IDs, we often forget that reaching the right consumer at the right time on the right device is the true Holy Grail of digital advertising.
We get lost in our pre-occupation with match rates and forget the individual consumer value.
How the cookie crumbles
Any company offering an active cookie pool of hundreds of millions realizes that it is really only dealing with a couple of hundred million devices and tens of millions of consumers.
The attrition from total cookie pool to total number of actual consumers is dramatic and the goal should be to understand and fully engage with those real consumers as opposed to matching cookie-level IDs.
Ultimately, clinging to an audience match rate when determining data effectiveness means clinging to cookies – an obsolete measurement now far surpassed by today’s robust data profiling.
Assume a Web site has 1 million visitors in a month. These “visitors” are really just distinct cookie-level IDs. If a company could match 250,000 actual consumers from that 1-million pool, that is a match rate of only 25 percent.
By numbers and industry standards alone, 25 percent is not very much.
However, a 25 percent match rate on any cookie-level ID could very well represent a match rate upwards of 50 percent at a consumer level, considering the fact that the average consumer uses two to three devices, each with three to four cookies per device, in a month.
The mere mention of a 25 percent match rate would make many marketers run away, when instead they should be considering if they would like to know everything there is to know about 50 percent of their consumer base: how many devices they own, how many touch points they have with the brand, and the time of day and location they use each device to consume Web content.
That is what knowing your consumer is really about. And what available data, which goes far beyond the cookie ID, is able to tell them.
KNOWING EVERYTHING about 50 percent of your consumer base is better than knowing little to nothing about 100 percent of your cookie-based IDs, even if they do come with a high match rate.
We need to move on from our love affair with ID-level match rates and focus instead on who our consumers are. That is where the love is real.
After all, it is the consumer who buys the products and engages with the brand, not the ID.
Manish Ahuja is chief product officer of Qualia, New York. Reach him at firstname.lastname@example.org.