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Transcript

In this video, I’ll review the use cases for partner data support in the real-time customer data profile with specific focus on the enrichment of known audiences with data partner attributes use case. These are the topics I’ll cover. If you have not already done so, I encourage you to review the partner data support overview video that provides a good background on the deprecation of third-party cookies and the emergence of newer technologies to support the key use cases impacted. Data partners have had several years to pivot their technology to fill some of the gaps as a byproduct of third-party cookie deprecation. The good news is that key acquisition and enrichment use cases can be supported by both data partners and ÃÛ¶¹ÊÓƵ’s real-time CDP in this new era of cookie lists. There are separate videos that cover each of these use cases in depth. This video focuses on enriching known audiences with data partner attributes for cross-sell or upsell initiatives. I’ll start with showing a diagram and explaining the workflow for this use case. It starts with some initial planning, such as the methods and locations for exporting and importing the audience files from the real-time CDP, as well as the identifiers that are expected by the data vendor so that they include additional attributes. Once that part is figured out, there’s licensing to address between you and the data partner. After the licensing is addressed, extend your real-time CDP profile data model to accommodate the attributes that come back from your data partner. You’ll need to update the data governance model for these partner attributes as well. Next, you’ll export the audiences selected for enrichment with partner data. These audiences are keyed off input identifiers, such as email or name, or whatever is decided upon between you and the data partner. Finally, you’ll ingest the attributes that come back from the data partner into the first-party customer profiles in the real-time CDP. Now I’ll hop into the experience platform and show you how to handle the implementation of this use case. I’ll start with the schema. I’ll select Schemas under Data Management, and I’ll open the Partner Data Enrichment schema. First, notice this is based on the XDM Individual Profile, which is the first-party, or deterministic profile, in real-time CDP. There are two field groups included in this schema. The first is Personal Contact Details and is based on first-party customer attributes. The second is Partner Enrichment. These define the fields that will be supplied by the data partner. If you viewed the video about the off-site prospecting use case, you’ll see that the partner attributes are like those used in that example. The key difference between this use case and that one is that the attributes used for this use case are aligned to a first-party profile based on a match the partner does prior to ingestion. Let’s look at the Personal Contact Details attributes first. I’ll expand the node for Personal Email. Notice the address field is configured as an identity field. Not only that, but it’s the primary identity for this schema based on the email namespace. Now let’s look at the Partner Enrichment attributes. I’ll expand the node for USAAM and notice the General Demographic and Industry Specific Demographic nodes. Notice the Partner ID field is present and it’s configured as an identity field, specifically using a Partner ID namespace. The significance of the Partner ID namespace is that the identity can be included in a profile for activation, but it will not be part of the identity graph. That’s because of the churning nature of partner data. It’s an intentional guardrail by our system to protect the fidelity of your first-party profiles. Now I’ll click Labels for this schema. Notice the third-party data governance policy has been applied to all the field group nodes for partner attributes. I’ll select Policies under Privacy to show you that label. If there are different policies and marketing actions that apply for partner attributes versus first-party attributes, you’d have that granular level of control by applying this label in the schema. Now that the schema and data governance labels are in place, let’s have a look at the dataset. One has already been created in My Experience Platform instance, so we’ll have a look at that one. Once in the dataset, I’ll select the last 30 days so that I can see more activity. I noticed some uploads on July 28th and below I can see the status of the batches. This is the same as viewing batches for other types of datasets in Experience Platform. I’ll click through one of them to see the details. Okay, so now we’re ready to ingest the partner data. We can use any file-based pathway to get partner data into Platform. I’ll show you where these are in the interface. First, I’ll go to Sources under Connections. When I click on Cloud Storage, it advances to the available connectors for that category. You’d more than likely be using an S3 right here, or at the end of this group, there’s an SFTP option here. From Workflows, you can select Map CSV to XDM Schema if you’re working with small files or doing testing. I’ll launch the workflow so I can show you what it looks like. On the Detail step, make sure you choose the correct dataset name. On the Next step, you can either choose a file on your computer or drag and drop one to the drop zone right here. After that, you’d go through a mapping step, but I’m going to cancel out of this workflow since I won’t be ingesting any new partner data. Now let’s jump to viewing prospect profiles. Because we’re ingesting partner data that’s matched to an existing first-party profile, I’ll use the Profile Lookup in the Customer Accordion. I’ll select Profiles underneath. Next, I’ll search for a profile based on the email namespace. Now I’ll type in the email address in the Identity Value text box and click View. I’ll select the profile ID to open it. The first area I want to direct your attention to is the Linked Identities section. Notice the absence of partner ID. Again, this is because partner data coming in with a partner ID namespace is not included in the identity graph for good reasons. However, at the bottom, there’s a section for partner enrichment data. These are the attribute values coming back for this customer from the data partner. Now I’ll show you an audience that includes first-party and third-party attributes. Again, since the partner attributes are included in the first-party profile, I’ll use the Audiences link beneath the Customer Accordion. Once this loads, I’ll see the existing audiences in the Browse list. I’ll open the High Earnings Homeowner Prospects audience. Now I’ll select Edit Audience so that we can see the definition in the builder. This audience includes all first-party profiles who have taken out a recent mortgage, have a high net worth, and do not have a financial advisor. I can use this audience to market to qualified customers for the new financial advisor program. Last, we can activate this audience to any downstream marketing or advertising destinations that accept the IDs used. I’ll select Destinations under Connections and locate the Facebook Customer Audience destination card. Then I’ll select Data Flows from the 3-picker next to it. I’ll use the first connection to activate an audience. On the Select Audiences step in the workflow, I’ll locate High Earning Homeowner Prospects. The remaining workflow is the same in terms of mapping and scheduling. After that, daily batch jobs run and will be sent out. I’ll cancel out of this since I won’t be activating my audience. This concludes the video for the Partner Data Enrichment for Known Audiences use case in the Real-Time CDP. Hopefully you feel confident getting this set up for your organization. Thanks and good luck!