When you are deciding on a strategic partner to share data with, it is important to know how your customers match up, so that you know how useful that data share will be. Segment Match allows you to see the overlap with potential data partners before sharing any data. For more information, please see the documentation.
In this video, we’re going to walk through the Segment Match service pre share insights report. So first we’ll navigate over to the Segment Match service.
And here you’ll see in a previous video, we created a new feed. We have not actually shared this with any partner. But we’ll click into this to see what’s been set up. And as you can see, we’ve got two segments and various configurations that we went through in the previous video.
So here we’ll click edit to get into the feed creation workflow.
And from here, we’ll click through to the share step, where you’re able to select a partner to share your data with. And you’re also able to access the pre share insights.
So these pre share insights are done at the partner level. If we had multiple partners selected here, we’d have a button per partner.
Clicking into this, pulls up the pre share insight report. So one key thing to note, this information is based on profile sketches, which means no data has actually moved. And it’s an estimate of the potential overlap that you have per segment selected with a given partner.
Profile sketches are a default capability for all platform and real time CDP customers. These sketches are run when there’s a significant change to a customer’s profile store database. So that change can be triggered either by a set of new data being adjusted or a significant change to segment membership. Today, that threshold is roughly a 5% or greater change in the profile sort database. But in the future, we want to make sure that these sketches are run on a more frequent and regular basis. So within this report, customers are able to get a ton of information without actually having to share any data which is extremely valuable for evaluating the potential value of a partnership. So the first piece of information that we get, again, at a segment level, we’re able to see what are the total identities that qualify for this segment. We’re also providing permission information about those identities. So we’re able to get a breakdown of how many of those identities actually have consent to share versus how many don’t have consent to share. And here you can see it’s roughly a 50-50 split.
That permissions information is based on the standard consent and preferences schema, which means customers are able to adjust their preferences and permissions data along with their data sets. And we will surface this report based on that information. Another key thing to call out is that how we determine consent is based on the default opt-in model. So Segment Match has a default of an opt-out model. Meaning we assume that all identities have consent to share unless a customer has explicitly uploaded an opt-out signal for a given identity. That opt-in model can be changed. So if a customer wants to operate under an opt-in default, where we assume no identities have consent to share unless they’ve explicitly opted in, they can submit a ticket to our engineering team, and we can have that default updated for their org. Along with the permission information, we’re also giving insight into the overlap potential with the partner selected. So here you can see of the total, roughly 94,000 identities, about 81% of those have an overlap with this partner.
This report also gives a breakdown by the identifiers and you can see we have three different identifiers here. This is based on the identity setting configuration at the feed level. So of those 81% matched IDs, we can see how many of those are IDFA versus hash phone versus email. So customers are able to get a lot of information and a lot of insight before actually having to move any data. -