Data Cloud / Marketers / Marketing Cloud

6 Steps to Unified Data for Personalized Marketing (With Salesforce Data Cloud and Marketing Cloud)

By Duncan Chrystal

It’s a marketer’s dream to have all the up-to-date information you need at your fingertips to deliver timely and relevant messages to your subscribers. And it’s even better when you can automate those messages for minimal effort. 

However, in a world of diverse engagement channels, multiple systems and a lot of data, a dream is typically where this remains. But it doesn’t have to. 

In this article we will get hands-on with a practical example of how we can bring together first-party data about a single individual from multiple platforms to create targeted segments and highly-personalized content. Such approaches are becoming all the more important as we look towards a future without third-party cookies

We’ll use Data Cloud and Marketing Cloud to get to the end goal of this article (shown below):  a dynamic email with a personalized message and call-to-action driven by unified and trusted information from multiple sources.

This example is for a nonprofit organization, but it’s applicable to use cases for other industries too.

Six Steps, Two Clouds

From CRMs and digital platforms, to social apps and spreadsheets of event leads – you likely have multiple “sources of truth” about individuals interacting with your organization. This is where Data Cloud comes in. In the first four steps of the process, we ingest and transform data, calculate insights, and create segments for downstream marketing. 

Then Marketing Cloud takes over, we use the Journey Builder and Content Builder to create highly-personalized and timely communication.  And finally, we bring click and open data back in to rinse and repeat the process!

1. Import and Transform Data

In our nonprofit example, we have data drawn from Salesforce CRM about volunteering and a separate source of extracted data from a fundraising system via Amazon S3. Our first challenge is getting all data in a format that we can work with for creating unified profiles and segments. This can be done in three sub-steps:

  1. Import your data as data streams from Salesforce CRM and Amazon via connectors.
  2. Use Formula Fields and data transforms to do some data cleanup.
  3. Map to a target data model using a drag-and-drop builder.

Data Streams and Bundles

Data Cloud comes out-the-box with connectors for both Salesforce CRM and Marketing Cloud:

Handily, these also come with “data bundles” – pre-package bundles of common objects around certain use cases:

These data bundles will automatically create data streams to pull data from CRM objects. Additionally, we are drawing in data from an Amazon S3 with CSV files of data. As of the Summer ’23 release, you are also able to use your own SFTP server

Formula Fields and Data Transforms 

Within a data stream we can do some straightforward data clean-up with Formula Fields:

There are a range of useful transformation options and a feature to quickly test the output of your formula.

For more sophisticated transformation we can use a data transform, which allows you to use SQL to combine multiple raw data sources:

Mapping to a Target Data Model 

The target data model forms what is known as a ‘canonical model’, a common data structure and format for data from multiple sources. The handy drag-and-drop mapping tool enables you to easily map from your source data streams to the data model.

Importantly, there are a set of standard ‘profile’ objects necessary for identity resolution. Central to this is the ‘individual’ object where you map any data about people and the contact point (channel) objects for mapping individual channel contact information.

2. “Unifying” Profiles

After importing data from multiple sources, we want to identify and reconcile data about an individual to arrive at a unified profile of that individual:

Here we have drawn data from Salesforce CRM, AWS, and Marketing Cloud into a single page view, along with our calculated insights. This unified profile is customizable within the Lightning Page Builder, enabling you to tailor the layout to meet your particular organizational needs.


To arrive at a unified profile, we must identify appropriate attributes and fields to identify the same individuals in source systems. Data Cloud matching operates similarly to Salesforce CRM Contact and Lead matching rules, except it happens over a collection of several profile objects and with a broader range of matching methods for more sophisticated profile matches.

The resolution summary helps to evaluate the performance of your matching criteria and gives advice on how it might be improved:

Identity resolution rulesets can be used for different purposes depending on your organizational needs, here we have created a “Test” and “Live” ruleset to enable us to test rule changes before modifying our existing Unified Profiles that drive our segmentation.

Reconciliation Rules

Next, where we are sourcing the same attributes on our unified profiles from different sources we need to tell Data Cloud which values to use. There are options for the last updated value, the most frequent across systems, and a priority that you define:

3. Generate Insights

Data Cloud has a drag-and-drop builder for creating complex insights. In our example, we are using several nested insights to create an “Optimal Ask Amount” for our unified profiles to use in our email call to action:

Excitingly, we will soon also be able to integrate Data Cloud with AI tooling for ever more sophisticated insights and propensity modeling.

4. Segment & Activate

Segments specify who goes into your segment and Activations specify where the segment will be published and what data you want contained in your segment. Both utilize drag-and-drop builders where you can reference:

  • Direct attributes on your unified profile. 
  • Any data you have related in your Data Model.
  • Calculated insights.

We want to build a segment of “One-Off Donors” and, using another calculated insight, those that are 25-44:

For our activation we want to publish to Marketing Cloud and include direct attributes we are going to use for personalization as well as our “Last Gift Amount” and our “Optimal Ask Amount” calculated insights.

 5. Create Engaging Marketing Content and Journeys

It takes just a few clicks to publish our segment to a Marketing Cloud data extension that we can then use as entry data for a multi-step and multi-channel journey:

We use our unified data to personalize our email body and a bit of AMPScript to create a highly-personalized donation call-to-action.

We use Einstein Send Time Optimisation to make sure we are asking at the optimal time. 

6. Re-Import … & Repeat

The final step is bringing engagement data back into Data Cloud. This is again made easy with a Marketing Cloud data bundle that maps engagement data for us:

We can use this data in a new “Abandoned Cart” segment of those that clicked the email but did not donate:

Or we can combine the data for further calculated insights and the process begins again!


The point-and-click data import, profile unification, and segmentation features of Data Cloud coupled with the automation power of Marketing Cloud offers a potent toolset for delivering timely, personal messaging. 

This article has only skimmed the surface but has hopefully shown a bright future for leveraging all the first-party data your organization works hard to collect in your marketing. New features and possibilities are rapidly developing for both clouds so be sure to stay up to date!

The Author

Duncan Chrystal

Head of Marketing Solutions for, a multi-award winning non-profit Salesforce consultancy.

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