TL;DR
- Role: Principal Product Designer
- Timeframe: Dec 2015 to Apr 2017
- Business problem: SalesforceIQ had powerful relationship intelligence but users had to do too much work to understand, clean, and act on the data.
- What I changed: Redesigned Contact Gallery around action, trust, and correction, including multi-merge and a master contact model.
- Proof: +40% daily active users; +34% duplicates merged.
The problem was not more intelligence
SalesforceIQ was early to the AI CRM story. The back-end captured relationships traditional CRM could not see. But intelligence in the back-end does not matter if the front-end makes users suspicious, confused, or tired.
Users still had to answer the same questions on every visit: Is this the right contact? Can I trust this record? What should I do next? What happens if I merge? Three long-standing customer issues had been open for months. The product was not translating what the AI knew into anything a sales user could act on without re-learning the app.
I worked weekly with support, product, marketing, and engineering so prioritization stayed honest about customer sentiment, then took the contact intelligence and data-quality surface directly.
What I did
- Researched pain points with AEs, customer success, and customers to anchor the redesign in actual friction, not assumed friction.
- Shipped fixes to three long-standing customer issues in the first month before opening a broader redesign conversation. The credibility bought license for the harder work.
- Reframed Contact Gallery from a display surface into a workflow surface: designed for what users needed to do (understand, trust, resolve), not for how to show more data.
- Introduced multi-merge and a master contact model, which meant rethinking the contact entity, not just adding a button. That model decision is what produced the 34% lift.
- Helped lay early design foundations for Salesforce Einstein after the PredictionIO acquisition, contributing thinking on confidence signals, source transparency, and correction loops.
- Mentored the first AI-focused designer on the team.
Multi-merge as a product model
Three frames of the decision the +34% lift sits on.
- 01 / 0301 · Pick master
The first step exposed the model decision in the surface. Three contacts the system thinks are the same person, three sets of facts, one explicit Master selection. The product was not auto-merging on the user's behalf. - 02 / 0302 · Select photo
A separate explicit step for the default photo. The merge is composed from atomic decisions the user can reason about, not one large irreversible action. - 03 / 0303 · Edit details
The composed contact stays editable before save, with every name, email, phone, address, and handle from the merge visible at once. The user owns the final shape, not the system.
What changed
Daily active users
After Contact Gallery redesign, 2017.
Plus a 34% lift in duplicates merged via the multi-merge model.
- Daily active users increased 40% after the Contact Gallery redesign.
- Duplicates merged increased 34% after multi-merge and master contact model shipped.
- Three long-standing, high-frequency customer issues closed inside the first month.
- Early Einstein thinking on confidence, source transparency, and correction loops fed forward into the company's broader AI direction.
Trust improved because users got clearer control over machine-captured data. The lesson still applies: AI earns trust by making the next action clearer, not by making the data denser.
Reflection
This case is a bridge between earlier CRM design and today's AI product work: make machine intelligence legible, editable, and useful inside the workflow the user already has.