The Einstein Prediction Builder (EPB) allows customers to make predictions using their own data without coding. For example, restaurant managers can use EPB to predict which customers are likely to show up for reservations so they can properly manage staffing.
Customers have mentioned that EPB is not serving their needs. Even though the product allows users to create experiences without coding, AI is a complicated process and customers need more help.
To kick off the project, we worked closely with data scientists to understand the user flow and found that there was significant user drop-off during the prediction building process.
Next, we conducted extensive research to dig deeper into the problem and identify more pain points and opportunities.
EPB uses a data checker to help users assess the quality of data throughout the processes. However, we found in testing that users tend to ignore it due to its limited functionality and unintuitive design.
By recognizing existing issues, we synthesized all our findings into three high-level insights and identified three design strategies.
Given the complexity of the product, we found it challenging to come up with ideas using traditional brainstorming. So instead, we fully immersed ourselves in the product's context, wrote down ideas as we went through it, and grouped them into main concepts.
With the help of the Salesforce Design Team, we evaluated our concepts on the Value/Effort Matrix with a scale of (0-5). We then moved forward with three most valuable and feasible concepts.
The final concepts include two aspects: a streamlined dashboard that simplifies navigation and visualizes progress, and a redesigned data checker that provides engaging data interactions.
We found through our research that users struggle with complex data sets. To solve this problem, the new data checker uses colors to mark different types of data for easier understanding.
Incorporating the new data checker, we explored three design options and chose the "Popover" approach for the best user experience.
We revamped the information architecture of the dashboard by simplifying navigation, visualizing progress, and making the data checker more visible and interactive.
With the new data checker, users can visually see the different types of data as well as the changes during configuration. Interacting with it also helps them better understand the dataset structure.
Moreover, users will be alerted when an error occurs, and they can easily fix it by following the suggestions and shortcuts provided by the data checker.
Without access to users, feedback from EPB design experts was critical. Through weekly meetings, I pinpointed areas for improvement and ensured that the design was better aligned with end users.
The popover allows the user to quickly review the settings and jump to steps when needed.
With these changes, users can efficiently move between different sections, making the overall experience smoother and more intuitive.
A place to show current progress and remaining steps.
Low AI maturity users w/o prior experience with EPB
Remote, unmoderated testing
Participants were asked to complete a set of tasks
Using both the original and the redesigned versions
To measures the speed and accuracy of the user when building predictions.
To measures the user's ability to recover from errors during the process.
The percentage of completed predictions out of all attempts.
The level of satisfaction of the user after using the Einstein prediction builder.
Data and machine learning was an unfamiliar field for me. When I joined the project, I had no idea how the tools worked or even what many of the terms meant. However, by constantly asking questions, learning the ropes quickly, working closely with the technicians, and actively listening to stakeholders, we were able to impress our manager with what we accomplished on this project.
While we initially felt constrained, a mature design system like Salesforce Lighting brought consistency and efficiency to our design. I had to really delve into its principles, components, and guidelines and think hard about striking a balance between following and adapting. I also learned to push the boundaries of design systems when appropriate.
