Resonate enables our customers to leverage an open ecosystem approach to using Resonate data and the Resonate platform. One of our key services includes matching offline customer records to Resonate cookie and device data for activation purposes. To accomplish this offline-to-online matching for our clients, Resonate leverages Acxiom’s LiveRamp Connect platform.
One of the primary reasons Resonate partners with LiveRamp for matching services is to maintain our strict privacy policies. Offline matching most often begins with Personally Identifiable Information (PII) as the key field and Resonate has strict privacy policies against transmitting, storing, or even working with private information.
Resonate has done extensive testing with all the leading matching solution providers. Through comprehensive testing Resonate has concluded that LiveRamp is the leading matching service provider based on both the scale of the LiveRamp footprint and performance of their matching algorithm.
How Matching Services Work
LiveRamp parses our customer’s offline data beginning with a primary record which is usually an email address, phone number, postal address or some other unique but personal piece of information. They match these records to their own database of known online users through a proprietary process. Once matched to their records, LiveRamp links their records to a table of known Resonate IDs. The matching service transmits the matched records as a real-time data feed to a secure repository where Resonate ingests the data. Resonate matches unique records in the incoming data feed to our universe comprised of hundreds of millions of anonymous modeled user IDs.
In our experience, matching partners concentrate on providing a match with the greatest scale for activation purposes. In-house testing at Resonate has shown that matching partners will provide either household-level or individual-level matching solutions. Wherever possible, individual-level matching is preferable and should be specifically requested. When a matching partner cannot find a match at the individual-level, it will often refer to a household-level match to maintain the most scale possible. When a household-level match is used, the matching provider will pull in other user IDs associated with that household, which may or may not be on-target for the desired audience.
Matching solution providers will often incorporate cross-device graphing to maintain the most scale possible. During this step, the matching partner will traverse a cross-device graph to connect other related IDs to the original ID. Industry averages can be as many as five additional IDs for every original user ID. While these related devices may be associated with the original users, they are often not on-target and can be responsible for a significant deviation from the original audience.