Site Selection, Relocation, and Consolidation are often referenced as the defacto use cases of Location Intelligence, particularly in verticals such as Retail & Real Estate - where brick and mortar assets play a key role in business operations.
With the current worldwide human and economic crisis caused by COVID-19, identifying potentially high performing areas in a “new normal” environment is even more critical as businesses large and small adapt their models and strategies in order to survive. The pandemic is also likely to accelerate the amount of redundant retail space which can be potentially transformed into living, social and commercial areas.
Savills Research are predicting that more than a third of malls in the USA are expected to have to close and that the UK may be ‘over spaced’ for retail by as much as 40%. The biggest shopping center chain in the UK, Intu, is warning that entire sites may have to close as it enters administration.
With this in mind many are moving to consolidate their locations, such as Starbucks who recently announced 400 store closures soon to take place. A while back we looked at two site planning strategies by comparing Starbucks and Dunkin’ Donuts and taking advantage of new location data streams, like Mastercard spend scores.
As reflected in the image above, many stores, including Starbucks, are now focusing on curbside pickup. This allows customers to buy online and then drive to the store (or “curb”) to pick up, in many cases without having to leave their car.
In the coming months determining which stores are best suited for curbside pick up & effective comparative market analysis real estate is going to be pivotal in informing consolidation strategy, which will be closely tied to location. For example, in New York City, a successful curbside location will look very different to a successful curbside location in DC where consumer profiles are different (e.g. higher car ownership, more suburban home and work locations).
There are many different approaches to find or keep the right locations and avoid million dollar mistakes in consolidation strategies such as these. What is true with all of them is they must funnel to a common path: finding a place where a combination of attributes (demographics, socioeconomic profiles, and proximity to certain points of interest, etc) is optimal.
In most cases optimal can be defined as the values of those selected attributes in the neighborhood of the most successful store or location. Once we have this set of properties, we can look for a place where these attributes have the same value (though since it is quite improbable that an area with the same combination of KPI values actually exists, we can always look for the closest match). This target area we are looking for is commonly known as a twin area.
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Twin area analysis can be performed within a site selection solution to identify similar locations to top performing stores. Alongside this they can allow you to:
The video below shows twin area analysis in action.
This functionality can also be included within bespoke Data Science projects or when developing customized apps as can be seen in the example below.
To learn more on how to prepare your curbside conversion analysis and dashboards to share with decision-makers at your firm check out our recent webinar.
Want to learn more about using Predictive Analytics with your consolidation strategy?Check out out recent webinar
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