Case Study
REACH.ai
Booking Dashboard
Bringing an outdated experience back to life again
- Client
- REACH.ai
- Role
- Lead product designer
- Duration
- 2020-2022
- Tools
- Figma · Principle
Summary
At a glance
REACH.ai scans a salon’s live appointment book for last-minute openings and cancellations, then contacts the customers most likely to fill them, using machine learning.
The merchant dashboard was noisy and hard to trust during peak hours. As lead product designer, I redesigned it, from discovery to a design system.
The new dashboard doubled weekly engagement and cut support requests by 40%. Most clients on the old UI chose to upgrade.
What I owned
- Planned and led a 3-day onsite workshop in Phoenix, Arizona
- Ran a heuristic evaluation of the existing dashboard
- Gathered mixed-methods research from 52 customers through customer success calls
- Designed the wireframes, the UI, and a design system
- weekly engagement with the dashboard
- 2x
- support requests
- -40%
- customers gave feedback
- 52
Context
The problem
Owners needed a command center that made demand, staffing, and client conversations obvious at a glance. Instead, they juggled several tools in the middle of a packed Saturday.
“I need an easy-to-use interface to gain quick, high-level insights into our client’s booking habits and how it affects our stylist’s schedule.”
Frustrations
- Several complex communication tools
- A noisy dashboard at peak hours
- Appointment details spread across systems
Goals
- Surface information to the owner on request
- Fewer inbound calls and emails about bookings
- Fewer front-desk interruptions
Discovery
What we learned
We couldn’t afford formal user testing, so customer success ran a mixed-methods study with 52 customers, asking the same three questions during sales and support calls. I then reviewed the existing designs against usability heuristics.
- “There is a lot of information on the dashboard.”
- “I can’t find what I’m looking for.”
- “It’s difficult to change locations.”
Heuristic findings
- Simplify the color palette
- Collapse redundant navigation into a calm primary rail
- Optimize for mobile
- Remove unnecessary and legacy features
- Reduce the amount of content on the page
- Establish consistent status color, type, and confirmation patterns
Discovery
Workshop and wireframes
I planned and led a 3-day onsite workshop in Phoenix, Arizona to review the experience and find where to improve it. Activities included stakeholder alignment, research synthesis, dot voting, sketching, crazy-eights, critique, and a retro.
Design
Polishing the pixels
High-fidelity UI brought breathing room, confident typography, and a design system that made charts, status, and AI moments feel like one product.
Design challenges
- Research without a budget: no formal user testing, so customer success asked 52 customers three questions and I added a heuristic evaluation.
- Too much information: I simplified the palette, collapsed navigation, and removed legacy features.
- Existing customers: the new dashboard had to be worth the switch, and most chose to upgrade.
Retrospective
Results
The redesigned dashboard doubled weekly engagement and cut support requests by 40%.
2x
Engagement surge
Customers spent twice as much time in the dashboard over a week, compared with the previous version.
-40%
Support decline
Fewer “where do I…?” tickets as hierarchy, labels, and empty states did the teaching.
Other results
- Positive feedback from current customers and from prospects during sales demos
- Most clients on the previous UI chose to upgrade to the new dashboard
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