I used Salesforce Audience Builder and here are my thoughts…

An unbiased review from a Salesforce professional!

I used Salesforce Audience Builder and here are my thoughts…
Photo by Denys Nevozhai / Unsplash

Disclaimer: The post is solely intended to share information based on experience without the notion to promote sale or advertisement.

A few years ago, Salesforce announced Audience Builder (AB), a cloud application as an extension tab in Marketing Cloud and just like Journey BuilderContact Builder, Content Builder, the nomenclature prevails.

Last year I had the chance to experience AB after it was already implemented in Salesforce Marketing Cloud. I recall using the application reasonably standard, however, it was the architecture that had limited and vague information anywhere. Despite the limited resources, I acquired the bulk of the information through help pages and StackExchange posts.

Therefore to shed some light, I decided to share my experience in 5 minutes with 3 principal segments:

  • synopsis — short description and contents
  • implementation — use case and examples
  • criticism — its power and vulnerability


Audience Builder is simply a powerful data segmentation application to create an audience within Salesforce Marketing Cloud. An audience represents a collection of contacts grouped based on attributes to receive a message. These attributes and dimensions (calculated attribute values) are the core of the application.

In plain language, a raw attribute could be Business_Join_Date whereas Customer_Churn_Score could be a calculated attribute and values as the dimensions.

These dimensions and attributes are grouped within the attribute library through which segmentation and filtering are done.

In my opinion, the tabs with a drag-and-drop canvas provide ease to less tech-savvy users to build an audience without needing to learn Standardised Query Language (SQL).

Audience Builder Overview by Northern Trail Outfitter

After you filter and segment your audience in Audience Builder, you can publish your audience as data extensions (created automatically) including the filter and segment as additional fields.

The canvas, dimensions, and attribute library are all configurable parts of the tool accessibly only to Professional Services team and configured during the original implementation or Discovery stage.


With some basics, here are some peculiarities of the tool where I think it delivered value and dominance during the project. These could be a good use case in your business and some might not be relevant at all.

  • ScalableAssuming a large global retail label ABCRetail wants to use its transactional data into SFMC for their marketing campaigns. In most cases with growing data, the Extract, Transform and Load (ETL) process using standard queries fails to deliver consistent performance over time causing time-outs and delayed runs. Complex queries that would time outin-store on a traditional system come back in just seconds using the new analytical architecture in Audience Builder. Moreover, Audience Builder typically publishes at one million contact records per minute.
As a standard practice, professional services recommends one refresh of Audience Builder data per day.
  • Accessible: From the strategy meeting, ABCRetail wants to send a rich content message from SFMC to consumers who live in Colorado asking them to purchase ski equipment in-store. Also, it needs to exclude consumers who have purchased any ski products past year and who live in the connecting suburbs. This may be a simple query to a platform developer or a data analyst, however, this isn’t feasible without an intermediate to advanced SQL experience.
Audience Builder takes away technical complexity with sophisticated conditions while defining audience and segment.
  • InclinedABCRetail wants to personalise the rich content message for a campaign to increase engagement. Typically, this requires setting up a lookup relationship to a different DE using AMPScript/SSJS or the sendable DE with related attributes which then can drive the dynamic personalisation. Also, AB provides the segments, dimensions and attributes within published Data Extension making it less technical to reference in messages.
Use WYSIWYG editor to build dynamic content and personalisation from the published DEs during the message creation process.


Throughout the project, as I interacted more with Audience Builder, I came to realise that it doesn’t tick all the boxes. It is an obvious fact that every tool has its purposes and these purposes have a definite way.

there is a caveat? Although AB has the power to process millions of records and execute complex queries to build dimensions, I believe there are two major aspects that could unlock a great value in the tool:

  • the refresh cycle
  • the dimension model.

The Refresh Cycle

As a standard practice, the professional services team recommends the Audience Builder refresh to run once a day. With this approach, the data isn’t fed frequently into AB, hence the published DEs won’t reflect the most recent contacts or dimensions.

Can it be more frequent? From what I’ve seen, the refresh used multiple automation (scheduled daily at midnight) with the Data factory Utility activities configured by the professional services team. These activities extract, load and calculate the core data from SFMC to project into the AB application for front-end use and typically take 2 hours of runtime. Obviously, the runtime might be variable for others because of the data model and requirements. Therefore, I assume the AB architecture and runtime probably limit the refresh cycle to run more frequently.

The Dimensions Model

In my opinion, the dimensions are the backbone to AB, allowing the users to build complex and defined segments. Customer_Churn_Score could bring value to the business in retaining customers by allowing to identify and target through the personalised offer in the message. The logic to build this value is locked down and is only accessible to the professional services team.

Can it be redefined? In my experience, each time we needed to add/update/remove AB dimensions, it was both cost and time effort with the professional services team. However, there are up to 30 user-created attributes (announced in the June 2018 release) that can come from data extensions that are registered in Contact Builder. Though this didn’t seem to work for me on the first few attempts during that time and I decided to move on assuming the muddled documentation would be updated at some point. Therefore, I recommend to carefully craft these logic/rules to avoid the cost and the hassle to reach the professional services team.

The Bottom Line

There is a small target group that would fit for the use case with Audience Builder and, to be honest, it is tedious to configure, elements are locked, and refresh is limited, hence AB might not fit the purposes for everyone.

If the business can live with these facts, AB provides versatility to the team and removes the dependency on standard queries and needing to create DEs manually.

I consider AB like a minified version of an ETL tool which pulls data out of the source, makes adjustments according to requirements and then loads the transformed data into a database.