Salesforce CDP (Customer Data Platform) is a declarative tool for marketers and analysts to deliver a single, integrated view of the customer. This feature allows users to ingest customer and engagement data into Salesforce CDP and harmonize their schemas into a unified data model.
Lead UX designer responsible for end-to-end user experience from ideation through development for 6 months.
Product Managers, Developers, Architects, UX Researchers, Content Writers, Product Marketing.
Launched in November 2020 as a part of the Salesforce CDP general availability launch.
It’s amazing how much data one person can generate. And how many different sources that data can come from. Even a simple shopping trip can generate customer data related to sales messages, web traffic, purchases, preferences, location, and a multitude of other sources. Businesses need to keep all that information organized and accessible so they can gain a more complete understanding of their customers.
Defining the data model can be complex. Customers need to understand what data is collected (and how), the existing data structure, and how that data relates to other sources. There is a huge unfulfilled business need to bring all of that data together into a single, actionable view of the customer.
The primary objective of this project was to create a foundational user experience where data from any source system can be ingested (by creating data streams) and unified (by mapping into a standardized data model) in Salesforce CDP.
Data is retrieved from the source by way of a Connector. Connectors establish trusted connections to the location where your data sources reside, building communication between servers so that your data can be continually accessed. Data Streams assist the connectors in that they dictate how often and when the connections should be established. And they also assist with actually populating the data into the data source object once the connector gains access.
Now imagine that all your data is ingested and each data set is speaking its own language. How do we get them all to understand one another? These data sets must all conform to the same universal language to begin interacting with one another. That’s where the second phase, data modeling, comes in. Salesforce CDP utilizes an open-source, collaboratively defined data model known as the Cloud Information Model (CIM) to help with this unification.
We kicked off the design process by running a series of design workshops to understand the concepts and technology we planned to build. The goals of these workshops and meetings were to:
Break down product requirements.
Understand the technology and architecture.
Identify design goals.
Document assumptions around personas and user goals.
Create a research plan for requirement and design validation.
After gathering the requirements and design goals, we started the design work by white-boarding and creating low fidelity concept mockups. These quick design wireframes helped me work with the Product Management and Engineering partners to represent the user flows and further question the technical requirements and scope from a user experience point of view.
As mentioned before, the designs were based on standard Salesforce applications heavily lifting standard design system patterns. While doing so, we documented my design assumptions which would be addressed in the design and persona validation phase.
We interviewed 9 participants from qualifying Salesforce companies. The participants were a mix of personas with Admin, Analyst, and Marketer roles. The study included a prototype clickthrough with a set of prompts, questions, and discussion on the feature functionality.
“[I’m] fan-boying over this whole process. I’m totally stoked about it. Thanks for developing these awesome solutions that make life better for people like me.” – Crystal Springs Resort
Product serves real needs.
“So Marketing Cloud is one of those products where… when you're setting things up, admins are very nervous because it can't be wrong, your data has to be right. Even the simplest thing, have to be very careful. Don't want to email someone who opted out. I'm always very very careful with it, very conservative about what I do. So going into it for the very first time, I didn't know the technology (referring to the data modeling) and what I was expecting. But if I'd done it once or I understood tech better, [it] would not has been as overwhelming.” – PayPal
Points of confusion arose, which led to some anxiety around the experience.
How might we build trust with our uses and improve time-to-value for ingesting and unifying data from different source systems.
Mapping is a tedious task and time-consuming.
From the user tests, the most time-consuming task identified was the field mapping step. The reason observed was the lack of familiarity with the CDP data model. The data model has a slight learning curve, and prerequisite knowledge is required even for any user persona using this feature.
To address this issue, we broke the user flow into 2 steps: data ingestion and mapping. With mapping as its own step, we could introduce draft and in progress modes to built user confidence and trust.
Improving time-to-value for Salesforce Sources.
We wanted to further reduce the task completion time by introducing the concept of mapping bundles. A bundle would include standard source objects, attributes, and mappings to the CDP data model. These mappings would be pre-packaged and deployed without user intervention. This functionality would enable users to quickly get started with Salesforce CDP and serve as a learning mechanism for future custom configurations.
In addition to the fore-mentioned enhancements, we also uncovered new technical requirements around the shape of the source data described below.
Provide data transformation capabilities during mapping.
When going through real customer datasets, we observed that the attributes sometimes required lightweight transformations, e.g., changing date formats, concatenating fields, trimming values, etc. Such data transformation capabilities would help customers preserve data quality and improve the accuracy of their customer data.
This new requirement made the mapping experience more complex and demanded more UX explorations beyond our first version designs.
As we continued to iterate and define these visual patterns, we conducted quick internal research with stakeholders and customer reps. The low-fi explorations gained a lot of support and traction. With backing from executives, we decided to spend more time exploring and bringing this concept to life. The explorations included visual and experiential considerations, including iconography, styling, color, and information architecture; while also validating customer use cases and datasets.
We consolidated all of our primary research findings and feedback from the internal studies and reviews to put together the end-to-end prototype for this feature.
We interviewed 19 participants with a mix of personas with data systems architect and business user roles. The study included small goal-based tasks. The primary evaluation metrics were task completion rate and time on task.
“That was... probably one of the best mapping exercises I've done in my life. And I have to do this quite a lot with different packages and products. Very intuitive and user-friendly, very awesome.” – Cadillac Fairview
“There's a fine line between technical and simple; the genius is making super-technical things simple. The simpler you can make things while letting you have control. Good balance.” – ThomasArts
This project was a multi-release development effort with two engineering scrum teams leading the Data Ingestion and Mapper functionality. We had to make several tradeoffs and stack features for supporting incremental development. One of such tradeoffs was full accessibility compliance for the mapper on pilot release. Still, the pilot customer feedback was overwhelmingly positive and helped us plan for future improvements and new functionality.
After the GA (General availability) release in Aug 2020, the design team decided to bring 2 new UX designers to cover each feature individually. I helped with the transition and onboarding, and serve as a consultant on the project.
Since GA, we've implemented mechanisms to measure impact and success of the feature which include:
• Metrics - Daily & monthly active users. CSAT from sales teams, implementation partners, and customers.