Skip to main content

Create Unified Profiles

Learning Objectives

After completing this unit, you’ll be able to:

  • Describe how to create unified profiles.
  • Describe how to analyze your data and get it ready for unified profiles.

Creating a Unified Profile

So, how does it work? It’s helpful to understand the following steps and concepts before you begin your data modeling and mapping. So let’s review the implementation steps to get you from raw data to a unified profile.

Step

Description

Ingest raw data from data sources.

Data is added from bundles, data extensions, Amazon Simple Storage Service (S3), and other systems as is. After raw data is added into the Data Cloud as a data stream, the data needs to be mapped to the data model.

Map and model data. 

The Customer 360 Data Model is the behind-the-scenes tool that allows data from multiple sources to be standardized into a readable format that can be easily mapped. Data from your data stream needs to be mapped to objects, like Party Identification and Individual, in order for identity resolution rulesets to work. 

Create identity resolution rulesets.

After modeling and mapping steps are complete, create an identity resolution ruleset. Match and reconciliation rules tell identity resolution what types of data to match and how to unify profiles across your various data streams.

Create and use unified profiles.

When identity resolution runs a ruleset, it creates unified profiles that can be used for segmentation and in activations. For example, add activation filters that filter out audience members based on their unified attributes.

Discuss Your Data

Now that you understand the concept behind unified profiles, what’s next? To be successful, it is important to spend time analyzing the data you want to use in Data Cloud.  A team gathered around a table and whiteboard to discuss data mapping.

Grab your team, a whiteboard, and discuss the following questions. 

  • Where is your data located?
    • List all locations including spreadsheets, S3, Salesforce CRM, Marketing Cloud Engagement, and so on.
    • Do you have an asset inventory created for each data source?
  • How do you identify individuals in each of your data sources?
    • Do you use email, name, birthday, or a system ID?
    • Do you use contact keys, lead IDs, or subscriber keys as a unique system identifier?
  • What data is shared across systems?
    • Are you consistently using first names, last names, or email addresses?
  • What does your customer journey look like?
    • Have you mapped out every customer interaction?
    • What data do you need for each of those interactions?
    • What data do you truly need for audience segmentation?
  • How is the data quality in each source?
    • Are there misspelled words?
    • What data is often missing (birthdays, phone numbers, or something else)?

Don’t skip this part! We promise it’s worth your time. Understanding your data is key to a successful Data Cloud implementation. In the next unit, we cover important data mapping considerations in order to create identity resolution rulesets. 

Resources

Share your Trailhead feedback over on Salesforce Help.

We'd love to hear about your experience with Trailhead - you can now access the new feedback form anytime from the Salesforce Help site.

Learn More Continue to Share Feedback