Places Insights

Background:

Google Maps Platform has a vast amount of data about Places (businesses, landmarks, parks, etc.) that enterprise customers across industries want to leverage to make business decisions.

Over time, we collected multiple use cases for analyzing Places data at a large scale. We wanted to help these customers answer questions like “Where should I build my next storefront” or “In which cities could I expand my logistics business”?

Challenge: Customers need access to our Places data to find answers to their big questions, BUT we have strict TOS inhibiting customers to cache and use aggregated data.

Hypothesis: If we build a Places Data Analytics Dashboard within Google Cloud console, our customers will be able to play around with, analyze, and make business decisions pertinent, anonymized Places data within the confines of Google Cloud.

Team

I led end-to-end planning and execution of both the survey & concept testing for this foundational project.

For this work, I was embedded in a core working group including:

  • PM

  • Lead Engineer

  • UX Writer

  • UX Designer

Methodology

Approach:

  • Survey with ~1000 Google Cloud customers

    • Purpose: Gather quick, scaled feedback on attributes and potential metrics of Places data most important to users.

    • Participants: 50/50 split of existing Google Maps Platforms customers and solely Google Cloud customers with analytics use cases.

    • Method: Survey with background questions, stack rank questions for proposed metrics with open-ended for use cases.

  • Concept testing

    • Purpose: Gather feedback on initial concept of Places Analytics dashboard with top metrics from survey results

    • Participants: 10 customers whose role was responsible for decision making related to relevant use cases

    • Method: 60 minute remote interviews with concept testing a lo-fi prototype

Insights & Outcome.

  • The biggest finding was that customers needed flexibility with Places data; they needed aggregated combine it with proprietary and 3rd party data, funnel it into their own models, and build their own data visualizations.

    • Based on these findings, I recommended our team shift away from building a full-fledged Analytics dashboard in Google Cloud console, and instead explore creative ways to allow customers the flexibility to leverage data within our TOS. This ultimately saved immense product and eng resources as we built a solution with existing parts of the Google Cloud ecosystem.

    • We landed on Places Insights dataset which gives customers access to aggregated insights from Google's Places data that encompasses over 250M+ businesses and places to make more informed business decisions all within BigQuery's data clean rooms.

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