Visually guided AI prediction building
with Einstein Prediction Builder
PRODUCT DESIGN • enterprise application • university collab
Aug - Dec 2022
4 UX designers, 1 Data scientist
🤗 Special thanks to Salesforce Einstein's Research, Design and Development team for their mentorship throughout the project!
Improved the adoption rate(~30%), reduced overall churn and enhanced user experience of a no-code AI prediction tool, Einstein Prediction Builder by Salesforce.
Let's say you are an analyst tasked at creating a report about what happens in your business org. In an ideal world, you would create a data visualization based on predictive data. You would write code, and quickly create a machine learning model. What if you don't know how to code? Or have low AI maturity? This is where Einstein Prediction Builder comes in.
It is a low-code tool to create custom predictions and deploy models. Since it’s low code, users don't have to worry about data ELTing(extract, load, transform), wrangling or tuning.
EPB creates custom predictions about what happens in your business without writing any code!
Salesforce team came to us with multiple problem statements. Adoption was low, tasks were left incomplete and models created with Einstein Prediction Builder were often not deployed. We needed to identify how to make it simply serve it's users.
6% drop-off
between the number of orgs who enabled EPB and those who have deployed the prediction.
More than 1/4
of the orgs that have enabled the predictions are not using them.
15% churned users
Started using EPB, and then stopped it.
How might we guide non-technical users to complete workflows so that they successfully build
AI predictions in Einstein Prediction Builder?
Jump past solution to research 🚀
Lillian
Biz Ops Interpreter (aka Business Analyst) in a startup. Acts as a bridge between business needs and technical capabilities.
Key Goals
• Define company's strategic decisions by making trustworthy cross-BU AI insights. Lillian is tasked at creating reports, & dashboards based on these AI predictions.
Pain Points
• Unfamiliarity with Einstein Prediction Builder.
• Low AI & data maturity. Unable to complete all tasks while creating a prediction.
• Finds current predictive model to be time consuming and confusing to use.
Lillian, familiarizes herself with EPB through the introductory interface. She is able to see potential outcomes EPB can bring to her work and further, benefit the business.
Walkthroughs take her through the EPB’s canvas and get her up to speed.
Lillian is able to focus on one task at hand with the help of 5-part canvas with clear segmentation of navigation, progress, working area, assistance, and CTAs.
Phase statuses (icons) on the navigation tree keep Lillian in loop of her prediction progress and also grasp her attention of any errors along the way.
Einstein (Salesforce's mascot) visually guides Lillian throughout, by being ever present on the guidance panel.
It helps her with understanding data, results, and terminology.
✨Bonus: Lillian can leverage Salesforce's trailblazer community to post her questions along the way.
Lillian can easily resolve errors with the help of the data checker. Visual hierarchy and actionable tips help her in real time.
With the focus on understanding AI maturity & machine learning levels of non-technical users, my team and I conducted primary research interviews.
Customers mentioned that Einstein Prediction Builder, as it is today, is not serving their needs.
"I had enabled the prediction, but I am not using it anymore."
"I have atleast one prediction enabled and it is used, but irregularly."
"...Feels like a lot of open ended instructions like 'Name your prediction' and everything depends on what data we have."
"Love Einstein is here to help. Wish they guided me at the start."
EPB has a long & complicated workflow. Heuristic Evaluation & User testing enabled us to focus on 'Building Prediction' part of the whole flow. Key observations:-
We performed a light competitive analysis to gauge which elements our competition was prioritizing in comparison to us.
EPB had the core components as its competitors, but it's discoverability and explainability was sub-par compared to our competition.
Users may lack the knowledge necessary to accurately predict a model. Eg, identifying the characteristics of a dataset.
GUIDANCE
Technical nature of AI predictions warrants written or
visual guidance at each step.
57% users abandon tasks in the event of a confusion or an error.
GUIDANCE, PREVIEW
Leverage Einstein to help users recover from errors in real-time.
Users not aware of expectations of each step. Eg, which part of the dataset is used for train, test and prediction?
FEEDBACK, DRAG & DROP
Progress of each step should be accessible through the interface. Implement drag & drop functionality to boost explainability of AI model predictions.
Even though predictions are enabled, they are either used irregularly(29.2%) or not deployed(27.1%).
EDUCATION, GUIDANCE
Improve model quality by training EPB. Educate non-technical users to accurately create segments and subsets of data.
Visual Learning
Making the experience of EPB more scannable and engaging through visuals.
Clarity
A bunch of critiques from Salesforce's product, UX, and engineering team later....
