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A significant number of our clients have their loyalty card data integrated within the TAS platform.  However, we’ve found that when it comes to leveraging that data to its fullest potential, many are just scratching the surface of what’s possible. Given that, we wanted to take the opportunity to tackle the topic here in-depth, so that anyone can understand how fully embracing loyalty card data can lead to better real estate decisions.

Data Preparation

Before preparing the data, it is vital that you decide the level of geography that you want to view the loyalty card. Typical geography levels include:

Block Group

Pros Cons
Standard census geography. Geographic boundaries change every 10 years.
Relatively small geographic areas that work for most retail concepts. May be too large for convenience based retailers.
All data providers produce demographic and consumer demand data at this level of geography. Thus, it is very easy to add additional data or to perform calculations such as share of total potential.

Zip Codes

Pros Cons
Everyone knows their zip code.  Thus, it makes a great level of geography to work with if performing exit surveys or POS questions. Zip codes are not a standard geography. The boundaries are different depending on the source of the zip code data.
Zip code boundaries are constantly changing.
Zip codes are very large and may be too large for many retail concepts.


Pros Cons
Can be sized to work best for representing your trade areas. You need to build the grids.
You need to roll any 3rd party data such as demographics or consumer potential to the grid if you want to perform calculations across the loyalty data and the 3rd party dataset.

Individual customer latitudes and longitudes

Pros Cons
You can’t get any more accurate than the actual latitude longitude of the customer! You are working with individually identifiable information. This is typically frowned upon by IT.
You still need to assign a standard geography or grid ID to the data so you can perform market share calculations

Below is a typical schema for how the final data set will look.

Store ID Geography ID Total Volume Department A Volume Department B Volume
1 410239601001 1,000 500 500
1 410239602001 2,000 750 1,250
2 410239601001 500 400 100
2 410239602001 250 125 125

Understanding Store Trade Areas

One of the primary purposes of customer loyalty data, as it relates to real estate, is that it allows you to visualize the trade areas of your existing stores. Below is a sample of two maps which show the geographic distribution of customers for a few stores.


TAS refers to the above visualization as a customer PIN map in which each store’s customer base is shown using a distinct color. PIN maps allow you to see the geographic reach of each store and the amount of overlap between stores. However, the map can become very busy and depending on your settings, can sometimes become hard to analyze.

Another common way to visualize customer data is by using a visualization commonly referred to as Desire Lines. They show the flow of sales (or market share) from different geographies to each store. Personally, I like this visualization better than PIN maps as I think it is a bit cleaner and shows the capture areas of stores in a more distinct fashion. I also think it allows for an easier understanding of how stores are splitting volume from geographies.  A sample desire line map is shown below.


Creating a Custom Demand Layer

Another primary use of customer loyalty data is that it allows you to create a custom demand layer based on the spending patterns of your customers. The basic premise is that from analyzing the geo-demographic characteristics of your customer base, you can identify areas that contain concentrations of people that have the same characteristics of your best customers. How this is accomplished will be the focus of an upcoming blog post, but it typically includes calculating an expenditure per market household by lifestyle segment. The result of this analysis is a map like the one below which shows you the geographic distribution of demand for your specific retail concept.


Once you have a custom demand layer, it becomes possible to overlay your customer data to see areas that have high demand but low customer penetration. These are areas that you want to analyze for future store openings. In the map below, both Summerlin and Henderson have high demand but not many existing customers.


Understanding Cannibalization

Having loyalty card data allows you to quickly analyze the potential amount of sales cannibalization that a new proposed store may have on your existing store fleet.  This can be accomplished by creating a trade area for the proposed new store and overlaying the trade area on your loyalty card data.  The report below shows that the proposed location will have a huge overlap with the existing customer base of store 2864.  Given the amount of overlap, this site should be killed extremely early in the process, allowing your real estate team to focus on other deals that have a higher likelihood of passing real estate committee.



Having loyalty card data opens the door for all kinds of analysis. PIN Maps, desire lines, and cannibalization analysis are great end-user tools that can be prepared extremely quickly and easily allowing your Real Estate Team to focus on opportunities that have the highest likelihood of success. More advanced analysis such as customer profiling, custom demand layers, and unmet demand calculations take a bit more work but can really pay off. Customer profiling and custom demand layers will be the subject of future posts.

Bill Dakai