How Places Insights in BigQuery Improves Location Intelligence

Opening a new business location is a major commitment, yet many people still rely on old-school research methods to guide their decisions. 

To beat your competitors to prime real estate, you need a modern approach. Enter Places Insights in BigQuery. 

With this powerful tool, your team can choose winning business locations that drive sales and accelerate growth. Let’s take a look at how it works.

Why Your Business Should Ditch the Manual Research

 

If you’re using static maps and outdated census data, your research is based on old reports that miss how neighbourhoods change in real time. This reduces your chances of picking a location that actually works for your business.

On the other hand, automated, hyper-local data transforms how you analyse the market. 

For example, instead of waiting months for a demographic report, you can instantly see live market trends inside BigQuery. This allows your team to make confident site selection decisions faster.

How Places Insights Can Address Common Location Issues 

Stop crowding your existing market 

Opening a new branch should grow your revenue, not steal it from other locations.

When you map your current sales against Google’s hyper-local business insights, you can see exactly where your current customers travel from.

This helps you position new stores far enough apart to break into a fresh customer base without hurting your existing storefronts.

Spot hidden competitors and market gaps

Manual desktop research often misses the smaller, informal, or newer businesses in an area. But Places Insights is constantly updated with fresh Google Maps data.

This allows you to see the exact density of competitors in a specific neighbourhood while highlighting underserved areas.

These gaps help you see where customer demand is high, but business supply is low, making it the perfect location for your business.

Understand local foot traffic patterns 

A location that looks busy on a Saturday morning might be a ghost town during the workweek. 

Instead of sending people to manually count cars, you can use aggregated data to understand the rhythm of a neighbourhood. 

You will know exactly when people visit nearby points of interest, ensuring your business hours and staffing match the real-world flow of the streets. 

Building Your Own Suitability Model 

The real power of BigQuery comes when you combine your own data with Google’s external insights. 

You can easily upload your CRM information, loyalty programme data, or current store sales. From there, combine it with Google’s Places data to see exactly where your most profitable customers are. 

Once your data is connected, you can create a simple location-scoring model. For example, you can divide a city map into a grid and score each square from 1 to 10. A high score means the area has your perfect target market and few competitors. 

To ensure success, you also need to avoid these common pitfalls: 

• Ignoring internal context: Never look at location data in isolation. Always weigh it against your own sales history. 

• Overspending on cloud costs: Running unoptimised queries can quickly drive up your bills. You need to structure your data correctly from day one.

Secure Your Next Winning Location with 28East

Don’t leave your business expansion to guesswork or outdated reports. By combining the analytical power of BigQuery with the real-time depths of Places Insights, you can confidently choose high-traffic locations. 

If you want a little extra help, 28East has got you covered. As an authorised Google Cloud and Maps partner, we help you connect your data, build custom location-scoring dashboards, and optimise your cloud costs. 

Reach out to us today to get started!