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Use Cases: How Restaurants Use Diner Insights
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How a QSR used Azira’s mobile location data to select a new location

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Key Insights:

Challenge:

  • The QSR wanted to assess the viability of a prospective site for a new location based on customer demographics and behavior.

  • The QSR needed to to define the trade area and understand where customers were coming from, including commute patterns.

  • The QSR required insights into the demographic and affinity profile of customers visiting the site.

Results at Glance:

  • Broad Reach: Found that the location attracted a wide customer base from southwest Connecticut, particularly from affluent families.

  • Demographic Fit: Identified that the customer profile was predominantly educated and wealthy, which aligned with the QSR’s target market.

  • Informed Decision-Making: Enabled the client to confidently determine that the site’s demographic was a strong fit for their business.

Solution:

The QSR leveraged Azira for: 

  • Comprehensive Reports: Used reports like the Optimal GeoSpace, Pathing X, and Common Daytime Location to understand the site’s trade area, customer locations, and travel patterns.

  • Demographic and Affinity Data: Leveraged data on customer demographics and affinities to other upscale brands to build a clear picture of the target audience.

  • Mobile Location Data: Mobile location data helped identify the geographic spread and behaviors of customers, offering actionable insights for location planning.

Table of Contents

Background

A QSR with multiple locations across the US wanted to analyze customers at a prospective site to determine if that would be a good location for their business.
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Reports used:

Where do Customers to this Study Location Live and Work?

The home locations (as derived from our Common Evening Common Daytime Report) for visitors to this location lie along the two major highways in the area. There is a high density of visitors from the city of Bridgeport.
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The smaller median CDL (common daytime, ie likely work location) distance indicates customers work closer than they live to the study site.

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Where are Customers Coming From and What is the Study Location’s Trade Area?

The pathing data shows that customers are predominantly using State Route 15 and U.S. Route 1 in traveling to and from the study site.  This, coupled with the CEL/CDL data suggests that customers travel a good distance to reach this location.
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The Optimal GeoSpace for this location sits on the same roads, but gives a more definition to the range from which this location draws most of its customers.

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What are the Customers’ Demographics?

Customers are probably white families with children.  They come from areas of high income and hold advanced degrees.
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What are the Customers’ Affinities to Other Locations?

The customers generally have high affinities to upscale brands like Whole Foods, Soul Cycle and Bow Tie Cinemas.
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The client learned from this study that this prospective location reaches a broad swath of southwest Connecticut.  This location in particular sees customers with families that are white, educated and wealthy.  The client may find that this is a demographic suited to their business.  Mobile location data is key for the client to be able to gather these insights and this is just one example in a variety of use cases.

What Actionable Insights Did the Client Gather from this Case study?

The client learned from this study that this prospective location reaches a broad swath of southwest Connecticut.  This location in particular sees customers with families that are white, educated and wealthy.  The client may find that this is a demographic suited to their business.  Mobile location data is key for the client to be able to gather these insights and this is just one example in a variety of use cases.
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