This is part 1 of a multipart series on data onboarding.
What is a “match rate”?
A match rate refers to the percent of users from a file that an onboarder is able to find and anonymously tag with data. As data onboarding becomes a core part of every marketer’s toolbox, knowing the match rate for your user set is critical for understanding the size of your online audience.
Let’s say your CRM has 3 million email addresses. If the match rate is 40%, you can expect your online audience, the number of users you can target with online display, to be around 1.2 million users.
How is match rate calculated?
It’s important to clarify what defines “match rate” because it can be calculated in different ways. At LiveRamp, we calculate match rate with the following formula:
# of input records that matched to at least one cookie/ total # of input records.
The numerator, it’s important to note, is not the total number of cookies – rather, it’s the number of input records matched to a cookie. Often, there are multiple cookies per record because users browse the internet across multiple devices and browsers. So by counting cookies instead of records matched, you are actually inflating the match rate. Additionally, the “record” needs to be defined consistently – if you input a list of addresses (so the denominator is measured in households), but the match is being done at the name and postal address (so the numerator is measured in individuals), you are again going to get an inflated match rate because there are multiple individuals per household.
Here at LiveRamp, our match rates are between 30-40% (and growing) and vary by file.
What drives match rates up and down?
The number of users that a data onboarder sees in its match network is what primarily drives match rates. The match partner within the network sends hashed registration information, such as a hashed email address, which is then used to match back to a offline data such as purchase history and demographics. So the number of users found via the match network directly drives match rates up or down.
The second biggest driver of match rate is the precision of the match. A data onboarder can dial-down precision, increasing the number of “false positives” (bad matches) in the cookie pool. This will lead to greater scale and a perceived greater match rate. Some ways to do this include looser match criteria (such as a Name & City instead of an exact address match), neighborhood (i.e. Zip or Zip+4) level matching, or IP address matching. For some marketers, this is perfectly acceptable (imagine a supermarket trying to reach people in a certain neighborhood). However, for marketers trying to reach their actual customers, loosening the precision for greater scale is a terrible trade-off as it dilutes the audience, makes it impossible to measure the success of the cross-channel campaign, and wastes online ad spend. There are plenty of other ways to achieve online scale (such as modeled audience extension off of exact matches). But without the accurate match, none of the established use cases of onboarding, such as retargeting and analytics, can be used appropriately. As they say in the data world: “garbage in, garbage out”.
What can you do to ensure high match rates without compromising precision?
As a marketer, you should understand this trade off as you begin to bring your offline databases online. We’ve talked to too many marketers who have been duped by an onboarding solution that promised a “high match rate” only to find that the data was entirely inaccurate or the match rate was being calculated in a wonky way that inflated numbers. Talk to an experienced, neutral party, such as your DMP, and ask their opinion on onboarders. And probe the data onboarder on how they calculate match rate, where they get their online matches (i.e. do they have direct relationships with high quality publishers?), and what their accuracy standards are.