Watch: Demystifying Match Rates

demystifyingmatchrates

In this webinar, Katie Coghlan of Live Ramp and I break down user match rates in adtech. It’s a pretty good primer for people who need the basics, but also want to understand some of the subtleties of identity on the Internet.

Advertisements

Match Game 2015

 

Match game

Ask me what my match rates are. I have no clue, and neither do you.

 

If you work in digital marketing for a brand or an agency, and you are in the market for a data management platform, you have probably asked a vendor about match rates. But, unless you are really ahead of the curve, there is a good chance you don’t really understand what you are asking for. This is nothing to be ashamed of – some of the smartest folks in the industry struggle here. With a few exceptions, like this recent post, there is simply not a lot of plainspoken dialogue in the market about the topic.

Match rates are a key factor in deciding how well your vendor can provide cross-device identity mapping in a world where your consumer has many, many devices. Marketers are starting to request “match rate” numbers as a method of validation and comparison among ad tech platforms in the same way they wanted “click-through rates” from ad networks a few years ago. Why?

As a consumer, I probably carry about twelve different user IDs: A few Chrome cookies, a few Mozilla cookies, several IDFAs for my Apple phone and tablets, a Roku ID, an Experian ID, and also a few hashed e-mail IDs. Marketers looking to achieve true 1:1 marketing have to reconcile all of those child identities to a single universal consumer ID (UID) to make sure I am the “one” they want to market to. It seems pretty obvious when you think about it, but the first problem to solve before any “matching” tales place whatsoever is a vendor’s ability to match people to the devices and browser attached to them. That’s the first, most important match!

So, let’s move on and pretend the vendor nailed the cross-device problem—a fairly tricky proposition for even the most scaled platforms that aren’t Facebook and Google. They now have to match that UID against the places where the consumer can be found. The ability to do that is generally understood as a vendor’s “match rate.”

So, what’s the number? Herein lies the problem. Match rates are really, really hard to determine, and they change all the time. Plus, lots of vendors find it easier to say, “Our match rate with TubeMogul is 92%” and just leave it at that—even though it’s highly unlikely to be the truth. So, how do you separate the real story from the hype and discover what a vendor’s real ability to match user identity is? Here are two great questions you should ask:

What am I matching?

This is the first and most obvious question: Just what are you asking a vendor to match? There are actually two types of matches to consider: A vendor’s ability to match a bunch of offline data to cookies (called “onboarding”), and a vendor’s ability to match a set of cookie IDs to another set of cookie IDs.

First, let’s talk about the former. In onboarding—or matching offline personally identifiable information (PII) identities such as an e-mail with a cookie—it’s pretty widely accepted that you’ll manage to find about 40% of those users in the online space. That seems pretty low, but cookies are a highly volatile form of identity, prone to frequent deletion, and dependent upon a broad network of third parties to fire “match pixels” on behalf of the onboarder to constantly identify users. Over time, a strong correlation between the consumer’s offline ID and their website visitation habits—plus rigor around the collection and normalization of identity data—can yield much higher offline-to-online match results, but it takes effort. Beware the vendor who claims they can match more than 40% of your e-mails to an active cookie ID from the get-go. Matching your users is a process, and nobody has the magic solution.

As far as cookie-to-cookie user mapping, the ability to match users across platforms has more to do with how frequently the your vendors fire match pixels. This happens when one platform (a DMP) calls the other platform (the DSP) and asks, “Hey, dude, do you know this user?” That action is a one-way match. It’s even better when the latter platform fires a match pixel back—“Yes, dude, but do you know this guy?”—creating a two-way identity match. Large data platforms will ask their partners to fire multiple match pixels to make sure they are keeping up with all of the IDs in their ecosystem. As an example, this would consist of a DMP with a big publisher client who sees most of the US population firing a match pixel for a bunch of DSPs like DataXu, TubeMogul, and the Trade Desk at the same time. Therefore, every user visiting a big publisher site would get that publisher’s DMP master ID matched with the three separate DSP IDs. That’s the way it works.

Given the scenario I just described, and even accounting for a high degree of frequency over time, match rates in the high 70 percentile are still considered excellent. So consider all of the work that needs to go into matching before you simply buy a vendor’s claim to have “90%” match rates in the cookie space. Again, this type of matching is also a process—and one involving many parties and counterparties—and not just something that happens overnight by flipping a switch, so beware of the “no problem” vendor answers.

What number are you asking to match?

Let’s say you are a marketer and you’ve gathered a mess of cookie IDs through your first-party web visitors. Now, you want to match those cookies against a bunch of cookie IDs in a popular DSP. Most vendors will come right out and tell you that they have a 90%+ match rate in such situations. That may be a huge sign of danger. Let’s think about the reality of the situation. First of all, many of those online IDs are not cookies at all, but Safari IDs that cannot be matched. So eliminate a good 20% of matches right off the bat. Next, we have to assume that a bunch of those cookies are expired, and no longer matchable, which adds another 20% to the equation. I could go on and on but, as you can see, I’ve just made a pretty realistic case for eliminating about 40% of possible matches right off the bat. That means a 60% match rate is pretty damn good.

Lots of vendors are actually talking about their matchable population of users, or the cookies you give them that they can actually map to their users. In the case of a DMP that is firing match pixels all day long, several times a day with a favored DSP, the match rate at any one time with that vendor may indeed be 90-100%–but only of the matchable population. So always ask what the numerator and denominator represent in a match question.

You might ask whether or not this means the popular DMP/DSP ”combo” platforms come with higher match rates, or so-called “lossless integration” since both the DMP and DSP carry an single architecture an, therefore, a unified identity. The answer is, yes, but that offers little differentiation when two separate DMP/DSP platforms are closely synched and user matching.

In conclusion

Marketers are obsessing over match rates right now, and they should be. There is an awful lot of “FUD” (fear, uncertainty, and doubt) being thrown around by vendors around match rates—and also a lot of BS being tossed around in terms of numbers. The best advice when doing an evaluation?

  • Ask what kind of cross-device graph your vendor supports. Without the fundamental ability to match people to devices, the “match rate” number you get is largely irrelevant.
  • Ask what numbers your vendor is matching. Are we talking about onboarding (matching offline IDs to cookies) or are we talking about cookie matching (mapping different cookie IDs in a match table)?
  • Ask how they are matching (what is the numerator and what is the denominator?)
  • Never trust a number without an explanation. If your vendor tells you “94.5%” be paranoid!
  • And, ask for a match test. The proof is on the pudding!