The Art and Science of Retail Location Strategy


Most of us have heard the common tenet of real estate: location, location, location. More appropriately, for Retail Real Estate it should be revised to location, location, strategy. A good retail location strategy can help achieve long-term success in sales performance, branding, and cost optimization.

In light of the imbalance in retail real estate supply and demand, especially in growing markets, it is vital that retailers secure the best location to serve their customers and boost their bottom lines. According to the 2015 ReisReports’s report on retail market trends, the vacancy rates for neighborhood and community shopping centers is down 80 basis points from its peak in 3rd Quarter 2011. Moreover, strong investment activity in the retail sector is expected to drive down vacancy rates even further in the coming year.

Analytics is the Foundation of Retail Location Strategy

As competition for prime retail space continues to gain strength, a comprehensive location strategy can guide a retailer in making the best real estate decision for their business and avoid costly long-term mistakes. Retail location strategy must rely on sound analytics to help companies operate more efficiently and effectively. There is an astounding amount of consumer data that retailers collect to understand where their customers live, where they shop, where they work, and what they like.

This complex data not only helps retailers predict what promotions to offer or how to staff for peak times, it is also used extensively in location strategy to predict which locations will do well, why certain locations outperform other locations, and how a location will perform after a major facelift. Retailers that are not fully utilizing the power of location analytics to fuel their real estate decisions risk sub-optimal results in driving traffic and sales.

Behind Every Good Strategy is Good Data

Professionals who specialize in retail location strategy are ecstatic with the wealth of data that can be heavily mined to refine models and predict behavior. With this profusion of available data, it can be difficult to identify which data points matter and, more importantly, what is statistically significant. For most retailers, the following data sets have the most relevancy for location analytics:

Business Strategy and Performance: The purpose of developing a real estate strategy is to support the overall Retail strategy. Deciding what data is relevant and how to synthesize it must be assessed in context of this strategy. Is the retailer trying to penetrate new markets, double-down with its existing customer base, or triple the store count in the next five years? Existing business performance and financials provide tremendous insight into existing locations and can help answer why certain locations perform the way they do and if are there similarities in the business performance across store groupings that can be explained by their real estate location.

Demographics: Population size, income, education, age, and household size are still the preeminent data sets when deciding on a new location, investment, or disposition. Psychographics, the study of personality, values, opinions, attitudes, interests, and lifestyles, are also a powerful complement to demographic data. A retailer needs strong analytics to pinpoint the income level, education level, or household size that represents their core customer. Once a retailer understands whom they serve best, they can then determine the proximity of their current real estate to that core customer and identify the gaps in their retail footprint that could be met with a new location.

Competition: The location of the competition is also a data point that must be considered. For some retailers, being close to their competitors is an advantage – which is why you will often see Baby Gap, Carter’s, Gymboree, Children’s Place, and Pottery Barn Kids all right next to one another in a shopping center. They know busy moms don’t want to spend their time racing all over town, so those retailers often use a cluster location retail strategy because being close to their competition is an advantage. For other retailers, they see a positive correlation between their bottom line performance and the further away their competition is located.   Understanding the optimal distance to key competitors and the impact that has on performance is a critical piece of the puzzle when developing a location strategy.

Retail Node: The quality and density of the retail node where a new location is proposed, or an existing location is currently being operated, is a key factor to understanding current or future performance.

Point-of-Sale and Online Sale Data: Point-of-sale data can tell retailers where existing customers live, how often the shop, when they shop and what they buy. Also, online sales data can complement in-store sale information. The data derived from online sales (e.g. addresses, order history, returns) are an input to location strategy because this information can help determine if there are holes in the brick and mortar strategy. If a retailer sees particularly strong online sales coming from a geographic area where they don’t have any stores, it would definitely warrant further investigation.

Key Analytical Techniques

Collecting, mapping, analyzing, and synthesizing the above data points are used to complete analytical models that serve as the foundation for a sound location strategy.  A powerful complement to the common analytical techniques listed below is a Geographic Information System (GIS) which can visually display the data being analyzed.

  • Ring radius study: Using concentric rings around an existing location or a location under consideration is a basic analytical technique to understand the potential customer base that lives or works in a certain radius near the location. Depending on the type of retailer, the ring radius distances used for the analysis may vary anywhere from 0.5 miles to 10 miles. The downside of a radius study is that is difficult to account for geographic barriers (e.g. rivers), road networks, or traffic patterns.
  • Drive-time analysis: Compared to a ring radius study, a drive time analysis can more accurately forecast if a customer will visit a certain location because it does take into account geographic barriers, road networks, and traffic patterns. Also, existing point-of-sale data can give retailers an idea of the average distance their current customer base already travels which can be used to predict the drive-time behavior of future customers.
  • Gravity models: A more refined analysis will employ a gravity model which determines the pull of a certain retail area based not only on nearby population, distance, drive time, but also the presence of competition. The output of the gravity model should provide a retailer with the percentage of trips expected from each geographic area (e.g. zip code, census tract, block group).
Marrying the Art + Science Will Create a Truly Comprehensive Location Strategy

The finest data, spreadsheets, mapping, or decision models will give you only the best preliminary decision on where to put that next store or which store would benefit the greatest from a fresh coat of paint or a new sign.

Ultimately a model cannot tell you the “feel” of the location. It cannot tell you if the visibility is good or how frustrating it is to make a right turn into the location between 4-7 pm. On-site visits to the locations are the most reliable ways to validate and corroborate the data from the model. In order to truly feel confident, the best retailers with the best location strategies employ a “boots-on-the-ground” approach. By marrying the data and models with a site visit, a retailer can develop a comprehensive and accurate location strategy.

The bottom line is with nearly 90% of all retail transactions occurring in a physical store, it is imperative that retailers employ analytics to synthesize the data at their fingertips to inform their real estate strategy and ultimately their business strategy.