What can be used to determine how many people are in a trade area and where they live?

The concept of a “retail trade area” has been around a long, long time. By broad definition, a retail trade area is the geographic area that a retail store draws customers from. 

The question is, how is it defined? 

What are the boundaries where the probability is high that people living in the area can actually be potential customers and fit your target customer persona?

For example, at a large home-decor department store outside of St. Catharine's, Ontario, most consumers are driving a fair distance to shop there, likely up to an hour. A general industry standard for trade areas is the surrounding area that represents 60% of customers. So for the St. Catharine's department store, that could be 25 minutes.

On the other hand, an espresso bar in downtown Toronto has a highly localized customer base, with the majority of customers coming from nearby office buildings during the day, shops nearby, or the surrounding neighbourhood. In this case, the store might use a trade area of a 5-minute walk time.

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Not All Trade Areas are Created Equal

Traditionally, there are three methods you can use to define a trade area: rings, polygons, or drive/walk times. These methods are purely geographic-based distance measures. Without other forms of data, these methods will get you started.  

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Basic Trade Area Concepts: Rings, Polygons, and Drive/Walk Times

Perhaps you just want to report on a 500 m radius around one of your potential locations. Typically, analysts will choose to show multiple geographic rings at a couple different radii around the address on a map. Real estate brokers might look 1 km, 3 km, or 5 km around a location and use census data to profile the demographics and socioeconomic makeup of those regions.

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To define a more customized trade area, you can use polygons. Any map-based software will allow you to draw a polygon shape on a map that outlines the precise area where you want your store to draw from, excluding those areas you believe you don’t address with your value proposition. This increases precision, allowing you to account for landmarks, waterways, major road segments, or natural geographic dividers that you know will partition customers from coming to one store versus another.

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Say you identified a potential location in a densely populated urban area. You want to know the competitive and demographic stats for an area within a 5- or 10-minute drive or walk time of your potential location. The boundary of the drive and walk times, although more difficult to estimate due to traffic patterns and time of day, would define the boundaries within which your ideal customer profile lives.  

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For all three of these more simplistic methods for drawing a trade area, the idea is to quickly identify the area you believe most customers will come from to get to your location. 

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This provides you with a geographic zone within which you can profile the demographic and socioeconomic makeup of the people who live in the area. The assumption is that these are your potential customers.

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The challenge with using rings, polygons and drive/walk times for calculating a trade area is that they lack the granularity of the actual customer profile and make the assumption that the demographic and socioeconomic profile is good enough to say, “this is where my potential customers live, work and play.” This is ok for starters, but trade areas can be so much more!

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The Rise of Mobile Location Data: Silver Bullet?

Mobile location data is now readily available in the market. This data is based on tracking the GPS signal from individual mobile devices that have applications on in the background that ping device locations every few seconds. There are a plethora of third party vendors that collect this data and aggregate it, anonymize it, and then resell it to private and public sector entities who use the data for understanding foot traffic volumes around geographic locations. Applications for foot traffic data range from public policy discussions on transportation issues, to retail real estate professionals identifying high traffic locations that would be ideal for a new store. 

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This data can be parsed down to identify the number of unique devices and where that device goes during the day. As such, it can be used to identify daytime and nighttime dwell times at any hour of the day, and whether that device entered a “geo-zone” (like a store). 

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Sometimes this data is used as a proxy or supplement to identify and validate the trade area around a store. Why? The idea is that if the device went into your store and we know the nighttime stationary geographic zone of the device (ie. the neighbourhood they live in), then we can get a little more accuracy on the actual trade area and do our profiling from there. 

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Mobile location data is not the silver bullet for trade area construction, however. But, in the absence of actual customer data, it's a good additional dimension to evaluate! 

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The Gold Standard for Trade Area Construction: Customer Data

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Customer data comes in several forms. 

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Some retailers collect the address and postal code of their customers through voluntary surveys or at the checkout cashier simply to know where their customers live. Other retailers get them from online ordering systems which facilitate contactless delivery. This is happening a lot more than it used to. 

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Advanced retailers have established loyalty or rewards programs that integrates with their POS system, giving them a wide range of insights about their customers' buying behaviour in addition to where they live. 

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Having your customers’ postal codes at a minimum gives you the best accuracy about the size and dimension of your trade areas. If you have spend data as well, you have the gold standard!

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Deriving Insights from your Customer Data

You can use your customer loyalty data to learn about the trade area patterns of your network. Customer loyalty data typically includes spending details and patterns over time. This can be used to show your customers' locations relative to the store (e.g. postal code, dissemination area (DA), forward sorting area (FSA), block group, ZIP code) and delineate the organic boundaries of where your customers travel from. Retailers can then measure market penetration, trip frequency, spend amount, basket size and contents, and most importantly, the extent to which these characteristics drop off the further away the customer lives relative to the store. 

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Retailers can also use this data to define trade area quality levels and establish primary, secondary, and tertiary zones. The primary zone might capture 25% of the market share, while further out in the secondary and tertiary zones the share goes down and trip frequency drops off. This has a big impact on where your marketing programs can be most efficient. 

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If you have many years of customer spend data, then you can also see the changes in spend behaviour year over year to identify pockets of softness or contribution growth in your trade area neighbourhoods.

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With this kind of customer intelligence, layered alongside competitive density, demographic and socioeconomic data, as well as foot traffic patterns, retailers can have a massive leg up on the competition about the health of their store network and where they can focus their marketing efforts to drive more trips to their store.

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The most actionable trade area considers every customer as an individual. What are the behaviours of the individual and how far are they willing to travel to get to a certain concept? 

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The real opportunity is to identify more of those customer types that you don't have a relationship with, but which live within the trade area defined by your current customers. With that information, you can go after more market share through your marketing programs! Granular and detailed customer data is the key to successful trade area construction and market share capture.

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PiinPoint Marketmatch identifies new trade areas that increase the probability of high performance locations and de-risk the location choice.

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Check out PiinPoint Marketmatch and our Keto Cafe case study to find out how we have helped other retailers and restaurant brands improve their real estate planning by helping them define their trade areas and producing a data-driven network roadmap. 

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