Making informed location decisions is crucial for business growth, whether you’re planning a new store, expanding into a new market, or targeted marketing campaigns. However, finding the right data—fresh, accurate, and analysis-ready—is often a frustrating and time-consuming process. Using disparate data sources introduces variability in schemas and inconsistent quality, leading to extensive time spent on data preparation and transformation. That’s why we’re introducing Preview of Places Insights, giving businesses the ability to derive insights from Google’s comprehensive Places data directly in BigQuery. This POI data encompasses millions of places around the world, spanning diverse categories like restaurants, retail stores, landmarks, and more.
Unlock real-world insights
Places Insights allows you to derive statistical understanding from Google Maps’ comprehensive Places data within the secure environment of BigQuery data clean rooms. The ready-to-use dataset lets you securely run analysis combining your own data with Google’s rich POI data, all while safeguarding sensitive information. Featuring nearly 300 place types and over 70 diverse attributes, including price level, ratings, store hours, parking, and wheelchair accessibility features, you can tailor your analysis precisely to your needs. With monthly updates reflecting real-world dynamics, this rich dataset can help you identify prime locations for expansion, understand the impact of surrounding areas on your current stores' performance, and strategically evaluate new market opportunities.
Density of restaurants in Manhattan visualized using a heatmap
Securely run your analysis in BigQuery’s data clean rooms
We know that protecting your proprietary information is paramount. That's why BigQuery data clean rooms are essential. They enable you to securely combine your data with Google's Places data. For example, a retail chain can securely combine their sales data with Google’s Places data in BigQuery’s data clean room to analyze aggregated location insights such as the density of POIs near each of their stores—like the number and average rating of restaurants near their top performing store locations. This can be used to run statistical models to understand how concentrations of different geospatial characteristics may correlate to their store’s performance, without ever exposing their proprietary data or analysis.
From finding the ideal location to evaluating store performance
Place Insights provides you aggregated data that you can use to optimize your expansion strategy and evaluate store performance. Restaurants can identify high traffic areas near complementary businesses such as theaters or shopping malls, to pinpoint the optimal location for their next store. Retailers can analyze how proximity to attractions correlates with store performance or sales revenue.
For example, to understand the market landscape near your potential locations, you can use a SQL query to identify nearby high rated coffee shops. This query will return the number of 4-star and above coffee shops that are open at 4pm within a 500-meter radius of your store locations.
SELECT WITH AGGREGATION_THRESHOLD
mylocations.name,
COUNT(*) as count
FROM
`places_insights___gb.GB` places
JOIN
`my_locations.locations` mylocations
ON
ST_DWITHIN(mylocations.location, places.point, 500)
CROSS JOIN UNNEST(regular_opening_hours.wednesday) AS wednesday_hours
WHERE
'16:00:00' BETWEEN wednesday_hours.start_time AND wednesday_hours.end_time
AND primary_type = "coffee_shop"
AND rating >= 4.0
GROUP BY
mylocations.name
This query provides valuable insights into the area, helping you assess the saturation of coffee shops in this location.
Heatmap of coffee shops near existing locations in London with ratings greater than 4 and are open on Wednesdays at 4pm
By bringing this data into BigQuery, you can seamlessly integrate it into your existing analytics workflows, use with your ML tooling, and visualize insights with Looker Studio.
What data is available?
This preview release provides access to a sample of Google's Places dataset, top city per country, for the following regions: Australia, Brazil, Canada, Switzerland, Germany, Spain, France, United Kingdom, Indonesia, India, Italy, Japan, Mexico, and the United States. With over 70 attributes, you can tailor your analysis to your specific business needs. The data is also regularly refreshed on a monthly basis. This dataset will expand in attributes and countries over time.
Ready to get started?
Fill out this form and we will reach out to you to learn more about your use case and how Places Insights can help you. We look forward to seeing how Places Insights helps you make more confident decisions.
