In January, cross-device tracker BlueCava merged with intent data targeting platform Qualia.
Today, that company — called Qualia — is announcing a new partnership with mobile attribution firm NinthDecimal that it says points to one of the key benefits of the merger.
The Qualia/NinthDecimal collaboration allows a marketer to track a visit to an offline retailer back to the motivating ad and to the data indicating buying intent, on any of several devices a user might own. It describes this new collaboration as “the first cross-device offline attribution that includes intent [data],” in which intent signals and cross-device tracking come from the same provider.
For instance, a visit by someone to a Honda showroom might be tracked back to an ad for Honda sale shown on that person’s desktop computer, and to the person’s download of a Honda brochure.
While other collaborations might similarly connect offline visits to a specific ad on multiple devices, and might overlay and match intent-to-buy signals (e.g., the brochure download) from an external provider, Qualia says that its recently integrated intent data/cross-device tracking produces a more reliable understanding of what paid media or marketing drove an offline action.
Generally speaking, Chief Product Officer Manish Ahuja told me, this higher quality can lead to a 30 to 40 percent greater attribution accuracy than other solutions. The key reason, he said, is that Qualia has access to the raw data behind both intent and cross-device tracking, and it has an integrated matching and verification process, so it can maintain quality control at a very granular level.
Another mobile attribution collaboration that involves cross-device matching, he said, might overlay intent data from an external provider, but that data has already been processed and grouped into segments of users. Although the segments can similarly be matched against cross-device user data, he indicated, the imported external data is removed from its source data and may include poor quality data or tenuous assumptions about matching intent signals to specific users.