Product

Place matching: A more accurate way to manage POI data

When you’re building customer-facing experiences or working to improve internal operations, accuracy of your location data is paramount. That’s why we are excited to announce Radar’s new place matching feature that makes it easier for brick and mortar and digital-first businesses to ensure that their geofencing data is accurate and up-to-date. Place matching also removes the burden of maintaining data over time.

Out-of-date geofences and POI data is a common culprit for subpar location-based experiences. For brick and mortar businesses, such as QSRs, inaccurate geofences lead to long customer wait times and cold orders. For digital first companies that use geofences to send location-based messages, poor POI data leads to messages sent at the wrong time or location which erodes brand trust and engagement. Place matching makes it easier for businesses to deliver accurate location-based experiences — from curbside pickup to location-triggered messages.

Background on Places

Place matching is powered by Radar’s Places feature, so before we explore specific place matching use cases, here is a reminder about how Places works. Radar’s Places dataset allows you to detect geofence entry and exit events for places, chains, and categories, even if you haven’t created a geofence for that specific place. Places provides near 100% coverage for top chains and categories by ingesting, harmonizing, and curating point-of-interest (POI) data from multiple sources. This means that you’re able to easily create and maintain geofences for thousands of chains and categories by simply tapping into Radar’s Places dataset.

The problem place matching solves

Places is a powerful dataset of chains and categories, but many Radar customers have already created geofences for points-of-interest such as physical storefronts. These internally-managed POI data can quickly become stale or outdated as locations in the physical world can change quickly. New locations open and old locations close down. Frequently, internally-managed POI data can’t keep up with these changes.

Oftentimes, teams face another problem: internally-managed POI geofences are generic circles, even though the physical location is a polygon. In densely populated urban areas, this further reduces the ability to accurately detect location and trigger location-based experiences. Unfortunately, bad data can lead to bad experiences since you can't accurately trigger entry or exit events.

The benefit of using place matching

Place matching helps ensure that your POI data are accurate and up-to-date so that you can deliver precise location-based experiences. By automatically linking together customer-created POI geofences and entries in our Places dataset, place matching updates the imported geofences to reflect the data in Places.

The-benefit-of-Place-matching

Place matching provides customers with more precise polygons for their POI geofences, improving accuracy over large, circle geofences.

One of the benefits of Radar’s location platform is that it supports multiple use cases across business segments. Similarly, since place matching helps ensure the accuracy of POI data, customers can use this data to more reliably build a range of great experiences.

How place matching helps brick and mortar businesses

Retailers and restaurant brands use Radar’s Trips product to create more seamless order ahead and pickup experiences. Trips helps brick and mortar businesses reduce customer wait times by 20-30% and improve internal operations through integrations with on-premise systems, such as Olo.

Accurate trip events are essential for developing these improved pickup experiences. For example, imagine that you’re a large QSR who has a steady stream of orders placed via your mobile app. You’ve invested in location technology, so you know when customers are on their way, but your store geofences were batch uploaded into your location platform and they aren’t as precise as they should be. For instance, some parking lot geofences extend into a nearby road or store geofences bleed into the parking lot.

If the geofence for a brick and mortar location is inaccurate, the trip events – such as approaching signals – are also inaccurate. Customers arrive at the wrong location. Wait times soar. Customer loyalty plummets.

Place matching addresses this problem by ensuring geofences are up-to-date. As a result, developers can build experiences with confidence and operators receive more precise live ETAs and approaching/arrival signals.

How place matching helps digital-first companies

Digital-first companies, such as RetailMeNot, use location infrastructure from Radar to trigger accurate on-premise experiences and contextual messages. To deploy these high-scale campaigns, digital-first companies oftentimes rely on POI data provided by disparate sources, such as retail or affiliate partners. This approach leads to a range of data inaccuracies such as duplicate merchants mapped to one location, stale data from store openings and closings, and mismatched merchants to locations. Not only does this make it challenging for digital-first firms to create a single source-of-truth for their POI data, it also impacts their ability to successfully execute experiences and campaigns.

Frequently, digital-first companies use location data to develop location-aware experiences, such as dynamic app content that changes based on a user’s location. Without place matching, digital-first brands had to rely on outdated geofences, so app content wasn’t necessarily reflective of the user’s location. From the user’s perspective, these inaccurate messages appear to be spam — eroding brand trust and engagement.

By using place matching, brands gain more accurate geofences (and thereby more accurate entry events), so the right experience is displayed to the right user at exactly the right location.

Digital-first companies also use location to trigger messages notifying customers that they can use a payment product, redeem rewards, or take advantage of an offer at a specific location. For instance, companies such as Ibotta send push notifications to deliver offers to customers based on their location. Place matching helps companies confidently launch campaigns, maintain a complete representation of merchants, and have more reliable attribution data. For end users, this results in more timely and relevant marketing messages based on nearby locations.

Get started with place matching

It is easy for current Radar customers to get started with place matching. First, import your geofences and select chains to match your geofences against. By default, Radar will search for places that reflect those chains within 10 kilometers (~6 miles) of each geofence and match to the nearest location. After the geofence import has completed, you can examine the results on the import history page. This will allow you to see a summary as well as logs with details and a link to the places where the geofence was corrected. Dig in deeper to identify geofences that were not place matched, geofences with large corrections, or errors.

Conclusion

Place matching, the newest feature from Radar, makes it easier for retailers, restaurant brands, and digital-first businesses to ensure that their geofencing data is accurate and up-to-date. This saves you time maintaining POI datasets and ensures that the experiences you are building are accurate over time. To see place matching live and learn more about how your business can benefit from more accurate location data, get in touch with a member of our team.