There is no industry norm or consensus on what is a “good” match rate because they deliberately vary by type of campaign, brand industry category or first-party data used.
Say a brand wants its attribution provider to connect people who were exposed to TV and digital ads with retail foot traffic via a location analytics vendor. Stringent attribution requirements would call for a highly deterministic data set and a match rate greater than 90%, said Auren Hoffman, the former CEO of LiveRamp who now helms a startup called SafeGraph.
An automotive marketer more concerned with long-term exposure frequency and greater reach, however, may be happy with a more probabilistic set matching 30% to 40%.
This is why straightforward questions like “What are your match rates?” can be counterproductive.
Marketers are frustrated, but it’s a frustration born of optimism, said Kate Clough, a media-planning VP at MRM//McCann.
“It used to be that (cross-device) plans were breaking down in the execution. Now we see the opportunity to provide those connected experiences, but it can be pretty daunting because buyers and sellers speak very different languages,” she said.
Morgan Digital Ventures and MRM//McCann are pilot partners in an initiative launched recently by the DMA to standardize cross-device RFP templates and establish the basics with a glossary of industry jargon. Terminology can have different meaning for marketers and cross-device vendors, and it leads to confusion over results.
For example, marketers may use the words “accuracy” and “precision” interchangeably, but those are discrete terms in the cross-device space. “Accuracy” is the percentage of correctly identified matches plus correctly identified non-matches – so a campaign with a low match rate could actually have high accuracy. “Precision” is the percentage of all possible matches correctly identified by the vendor.
“Recall” is a media-buying metric to judge an ad’s impact on consumers, but a cross-device vendor considers recall to be the percentage of overall identified matches divided by all known true matches.
“Agencies typically have a clear idea of how they’d like match rates to be calculated, but if there’s any ambiguity in their guidance to the supplier the results can be inflated,” Clough said. “Are you talking about matched devices or unique individuals? In some cases it seemed accidental, but it left people vulnerable to potential manipulation.”
Michael Schoen, VP of marketing services at Neustar, was less charitable about some linkage claims: “As with everything in ad tech, where there’s a financial incentive, there’s fraud.”
It’s now common for a third-party measurement firm like comScore to verify cross-device graph results when they’re applied in advertising, said Yael Avidan, VP of product at the mobile DSP Adelphic.
Even with verification, some marketers still aren’t convinced the results are accurate.
“If I see match results I don’t like – they seem too high maybe or I’m just not confident what I’m seeing is correct – comScore verification isn’t going to change that,” said one major toy brand marketer who said she couldn’t comment publicly due to NDAs with multiple data-matching vendors.
Much of the overall confusion over match rates is due to a lack of vendor transparency, said LiveRamp Chief Product Officer Anneka Gupta.
“There are lots of people out there saying they do deterministic matching, but you have to be careful about what’s being layered into the data,” Gupta said.
Many cross-device vendors and data aggregators regularly pay publishers to help them connect a customer’s data to web traffic or email sign-ups. The inconsistencies plaguing publisher data monetization – bot farms juicing numbers with fake emails or actual people using throwaway addresses on non-billing accounts – can be passed on to device graphs.
To shake buy-side concerns about finding quality data at scale, some firms are “moving toward a shared, authoritative match set,” said Jay Stocki, SVP of data and product at Experian.
Stocki alluded to Experian’s partnership with Neustar. The two longstanding titans in cross-channel identification began selling a shared profile-matching product earlier this year.
Acxiom, another legacy giant, pursued a similar goal when it purchased LiveRamp in 2014. The European telco Telenor and Oracle each bought a cross-device ID firm this year (Tapad and Crosswise, respectively).
Executives from Acxiom/LiveRamp, Oracle and Experian/Neustar each attributed a steep growth in match rates over the past two years to industry consolidation and cooperation.
Clough anticipates more consolidation as the wider ecosystem seeks enough shared scale to mollify marketers and offset the advantages enjoyed by Google, Facebook and Amazon, all of which have their own proprietary cross-device data.
“[Cross-device vendors] are willing to work with a variety of partners,” Clough said, “because that’s actually the only way for them to differentiate.”