Gaining Insights on Customer Best Record with Merge Logic

Objective

After completing this lesson, you will be able to identify how the customer best record is influenced by merge logic

Key Concepts

Now that you are familiar with the data model of SAP Marketing Cloud, let’s see what logic is applied to build a customer’s best record.

To better understand the merge logic in SAP Marketing Cloud, it is important to become familiar with the definition of one per contact, for identification, and priority.

One per Contact: specifies whether a contact can have multiple IDs per origin or only one. Priority: indicates how reliable an origin is considered as a data source. For Identification: specifies whether an ID can be used to uniquely identify a contact.

Example

One per contact: A contact can have only one SAP ERP Customer number, but more than one email address or phone number.

Priority is a value between 1 (high) and 99 (low) that is used to indicate that data from one source has more or less importance than data from another source.

Note

Do not enter the value 00 as a priority. If you do, it will be interpreted as 99 (lowest priority).

For identification: Typical examples of such IDs are IDs that cannot be shared by other contacts, such as a contact’s system ID or mobile phone numbers which are typically owned by one contact only. In contrast, email addresses or landline numbers might be shared by family members. This indicator plays an important role for the contact match and merge process and is used for both source system IDs and additional IDs. IDs flagged as relevant for identification can be used to uniquely identify contacts that can be merged.

The configuration of these three indicators can be done in the Configure Your Solution - Origins of Contact IDs, which can be found in the Manage Your Solution app. You should always keep in mind that the settings you make influence the match and merge process.

Match and Merge Process

The graphic below describes the standard logic implemented when the system processes imported contact data and uses it to build best records and enrich contacts.​

This graphic describes the standard logic implemented when the system processes imported contact data and uses it to build best records and enrich contacts.

Whenever contact data is imported into the marketing system, contacts are matched and merged in a two-step process. In the first step, the system tries to find matching contacts based on certain contact IDs. Then in the second step, any contacts that have been identified and flagged as potential merge candidates are merged where possible. For each record you load, exactly one contact can be updated or newly created.

Origin Configurations Influencing Match and Merge

This table lists the different possible combinations of for identification relevant and One per Contact attributes for ID Origins. The attributes influence the match and merge logic, and result in either a hard match criterion, a soft match criterion or in a no match.

This table lists the different possible combinations of for identification relevant and One per Contact attributes for ID Origins. The attributes influence the match and merge logic, and result in either a hard match criterion, a soft match criterion or in a no match.

Please take some time to go through the different combinations.

Match and Merge Examples

Let's look at some examples.

Watch this video to learn the match and merge process that contact data undergoes whenever new data is uploaded.

How to Configure Origins of Contact IDs

Now let’s learn how to configure origin IDs.

Watch this video to learn about what origin IDs are and how to configure them, and how these affect the contact match and merge process.

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