Walmart’s Closing Stores
Walmart has come under fire of late for announcing a series of store closures around the United States. Many of these closings have come from the Neighborhood Market brand focused on groceries, but several closures have come from Walmart’s flagship Supercenters.
While closures have become a frequent story in a changing retail environment, there is something uniquely interesting about a brand as strong as Walmart closing its signature stores.
To understand the situation better, we dove into location analytics to unpack the decision making and what it could potentially indicate moving forward.
Limited Reduction in Traffic
One of the first stores to have their closure announced was a Walmart Supercenter in Dallas, Texas. In a normal situation, one would expect to see underperforming stores suffer from a significant drop in foot traffic. Yet, the Dallas location was actually operating at fairly normal levels. The graph below shows weekly foot traffic to this location beginning in the first week of June 2017 until May 2019 when the store shut down.
Wider numbers also don’t align with a weak performer as the store enjoyed over 450,000 visits in Q1 2019 alone from over 150,000 visitors. So, not only are people coming to the store, they are coming frequently. Furthermore, comparing this branch against all Walmart Supercenters in the US, it is ranked at the 86th percentile in terms of foot traffic in Q1 2019, with traffic that’s almost 50% higher than the ‘average’ Walmart Supercenter:
Combining this activity with the rise of Buy Online Pick-up In Store, and it is surprising to see these locations shut.
One possible answer comes from a risk that every major chain faces – cannibalization. Walmart’s strength has led to a rapid expansion of stores throughout the country, that in some cases, ended up putting stores into direct competition. For example, looking at the True Trade Area for the same Dallas location compared to two other nearby Walmart Supercenters highlights the challenge that the store faced. It was competing directly with two other centers with an almost complete overlap of customers. This means customers weren’t choosing whether or not to go to Walmart, but which Walmart to go to.
This factor was also substantial for two locations in Louisiana where a closed store – a Walmart Supercenter in Lafayette – saw their audience overlapping almost completely with a Walmart Supercenter in nearby Carenco.
In short, Walmart is not undergoing some shocking reduction in brand value or customer loyalty, but customers are being served by multiple locations. The result is that a specific Walmart Supercenter’s biggest competitor turns out to be another Walmart Supercenter.
Why Does This Happen?
It is easy to look at cannibalization as a simple error to overlook, but in reality, the data available to retailers has been severely limited. Most are forced to make do with trade area data that focuses on the simple breakdown of a 3,5 or 7-mile radius. Yet, the reality is that customers are not limited to an arbitrary circle and are more likely to follow specific travel patterns based on traffic flow, proximity to workplaces and a myriad of other factors. As a result, major chains are often forced to make decisions in a deep fog leading to costly mistakes.
Can It Be Done Better?
Another question is whether cannibalization is simply guaranteed and unavoidable. But analyzing other top brands shows that market planning optimization can be achieved.
Take IKEA as an example. In the Greater Los Angeles area, there are four IKEA locations that surround the city. Each sells the same products, each offers the same level of service and each has a near identical rating on Google – three with 4.4 stars and the remaining with 4.3. But IKEA’s brilliance doesn’t lie in a fundamental difference within the store, but in the absolute perfection of the distance each property has from the other stores.
Even when accounting for 80% of overall visits over a 6 month period from late 2018 to mid-2019, the cannibalization between stores is almost non-existent. For the IKEA customer, it is obvious where they should go, because there is an ideal location and then there is everything else.
The result is an optimization of a region that helps each store maximize its area while enabling IKEA to grow at a sustainable rate.
Walmart’s Supercenter Path
To analyze Walmart’s situation further, we dove into several other locations where multiple Walmart Supercenters sit in relative proximity. The map below shows the True Trade Area of several locations around Fort Meyers, Florida. The locations marked with Blue and Red symbols see almost perfect delineation based on natural borders. Interestingly, while one section of the Red property’s trade area is untouched, it does have significant overlap with the property marked in green further East.
Could this indicate a value in relocating the Green site further to the East? Potentially. However, the separation of the sites marked in blue and red show the impact of optimizing location. Even though the sites are close in terms of distance, the natural barrier keeps cannibalization to a minimum.
The problem of cannibalization comes into greater focus as one moves north to another grouping of three Supercenters in the area of Bradenton, Florida. The three properties all have strong overlap, with the primary culprit coming from the location marked in Red. If you were leading Walmart’s market planning division and looking to maximize the value of each store, wouldn’t this be the ideal site to relocate? Audience patterns indicate that the other stores would effectively serve this market and costs could be limited.
Conclusions – The Rise of Offline Optimization
Not all store closures are equal.
Many brands are only now beginning to utilize tools that enhance their visibility and understanding of customer engagement. As a result, many actions – like store closings – need to be seen more as a reaction to new and accurate data than a decrease in brand value.
The impact of data-driven approaches that leverage location analytics cannot be overstated. It adds a powerful level of capabilities to large and small retailers alike looking to maximize their impact. Where digital companies have leveraged data to optimize their activities, the rise in solutions like Placer.ai has given companies the ability to deploy similarly data-driven approaches to the offline world.
Especially in the scope of the wider debate around the demise of brick and mortar retail, data like this can empower offline retailers to thrive in a changing retail landscape.