Simplifying Sign-Up with Google Places

Incorporating Google Places to simplify the sign up flow and setting the groundwork for a cleaner database

About the Project

I streamlined the retailer sign-up by integrating Google Places, enabling quick location entry, reducing friction, and overall speeding up the sign-up process.

Role and Team

Lead designer, partnering with product managers and engineering

Duration

3 months

What's Provi?

Provi is a B2B online marketplace that serves the entire three-tier system of alcohol distribution. Restaurants, bars, and retailers can order for their beverage program all in one place, distributors can manage their customers and orders, and suppliers can increase their brand presence.

Summary

I redesigned Provi’s retailer sign-up flow by integrating Google Places, simplifying business entry and dramatically improving account match rates with distributors.

✅  Google Places and business match rate: 30% → 89%

✅  Distributor-connected accounts: 15% → 70%
✅  Fewer duplicates, better data, faster onboarding

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Problems and Goals

Manual input caused drop-offs and data mismatches
Streamline onboarding with Google Places
Only 15% of new accounts were distributor ready
Improve retailer–distributor match accuracy
Manual distributor matching (70% of the time!)
Set retailers up for ordering immediately
Duplicate accounts, fragmented data
Build cleaner, more scalable data
Problem
Goal
Manual input caused drop-offs and data mismatches
Streamline onboarding with Google Places
Only 15% of new accounts were distributor ready
Improve retailer–distributor match accuracy
Manual distributor matching (70% of the time!)
Set retailers up for ordering immediately
Duplicate accounts, fragmented data
Build cleaner, more scalable data

Solution

We simplified the sign up flow by:

1️⃣ Integrating Google Places API for autofill

2️⃣ Auto-matching retailers to distributor data via Google Places ID

3️⃣ Removing redundant fields and manual steps

4️⃣ Adding fallback manual flow for edge cases

Results

Metric
Before
After
Retailer-Distributor Match Rate
30%
89%
Distributor Connected Accounts
15%
70%
Manual Match Interventions
70%
11%

Bonus: Cross-functional teams (Ops, Sales, Marketing) now operate more effectively with cleaner account data.

Learnings and Path Forward

  • Not all drop-offs are bad — sometimes it's quality filtering
  • Technical feasibility checks (like Places ID match %) are crucial for early validation

This project laid the foundation for future enhancements across the platform, from customer support to internal tools.

Want to see the full breakdown with research, flows, and more screens?

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