Salesforce Intelligence


Salesforce Intelligence

The problem

Today, marketers are responsible for more than just reaching their customers. They need to be certain they’re driving a return on all their investments. They have over 8,000 marketing tools within the ecosystem, and 64% of analysts’ time (5 hours/day) is spent managing the data. Given that context, we decided to focus on solving two main pain points:

  • Reduce the amount of time and errors related to data manipulation.
  • Add the ability to automate data according to what’s best for the dataset.
Data Model 1

The challenge

When I joined Salesforce in 2020, I jump into a project in which I must rebuild the entire flow of an essential part of a platform that three designers had previously contributed to. After brainstorming about the design possibilities, we learn there are UI constraints and that only parts of the flow can be rebuilt. The beginning was frustrating. However, after analyzing what needed to be accomplished, we realized that focusing on three goals would allow us to overcome the constraints.

  • Streamlined information
  • Introduced a new UI
  • Focused on pain points


UX Work Process

1 – Planing with the PM
2- Research
3- Initial Design
4- User Testing
5- Final Design and Developing
6 – Release
7- Analyse



1. Streamlined information

In the first screen of the flow, the user’s goal is to upload their data or select one of the technical vendors (file host, database, flat file). We made it easier for the user to take action by eliminating or relocating unnecessary steps.

Connection Gallery →

Upload and Technical Vendor Connectors

2. Introduced a new UI

For those screens where we couldn’t change the functionality — due to business concerns, development requirements, or wanting to minimize disruption to our users — we introduced new UI navigation instead. We created a more cohesive relationship between the old and redesigned screens in our platform by adding breadcrumbs, a new header, and a footer to help the user navigate.

Introduced a new UI

3. Focused on pain points

When we’re dealing with complex flows, we always need to focus on the main issues that need solutions. This may seem obvious, but deciding what actually needs solving helps to bring real value for the user experience, business goals, and product impact. With that in mind, we came up with solutions for enabling users to better connect their data (data manipulation), save time and make fewer errors (data validation). We also automated it (automate data refresh).

Data mapping

Mapping connects the source fields in your uploaded file on the column left to the corresponding Marketing Cloud Intelligence (Datorama) fields on the column right. Marketing Cloud Intelligence uses machine learning algorithms to map your data.

mapping 2
New Mapping UI

Mapping Validation →

Mapped Data Preview

Data validation

Three validation messages can appear during validation: mapping errors, mapping warnings, and model relationship errors. Errors are in red and warnings are in yellow. The user can see each of these messages in the pop-up on the left of your mapping under Validations.

To get more information and see how to fix an error, the user can click the error on the left side. Here’s an example of a mapping error indicating that an entity key mapping is missing:

Data automation


4. Analyzed performance

Research Communicate

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