Shopping For The Next Retail Site

By Paul Sill
From the November/December 2024 Issue

In retail, choosing the right location can be everything. With more than 40 million potential predictors available—and billions of data pings from apps and connected cars—how do you make sense of it all? More importantly, how do you filter out any misleading signals to focus on what truly matters?

Thanks to advanced predictive models, turning data into actionable insights has never been easier, allowing retailers to assess a site’s revenue potential and develop a scalable roadmap for national growth.

Whether a business is a small startup considering a handful of locations or an established enterprise expanding across hundreds of locations, data can illuminate the path to strategic success. Here’s how to build a model that not only highlights what’s happening, but points to the next best investment.

Harnessing The Power Of Mobile Data

Effective site selection starts in the real world: with resources on the ground identifying promising locations. Mobile data now supplements this process, helping narrow down options and mitigate risk in a fraction of the time by offering retailers comprehensive, real-world insights.

Mobile data has been one of the biggest game-changers in site selection. By creating a snapshot of consumer behavior, mobile data enables retailers to understand who is visiting certain areas, how often, and for how long—which are essential elements in forecasting a site’s performance based on actual activity. Here’s where mobile data delivers the most value:

1. Developing customer profiles: Mobile data not only reveals demographics, but also the lifestyle and behavioral patterns of those visiting a location to ensure a building is being built to target. This includes information like age, income, shopping preferences, and even frequency of return visits. This enables retailers to refine and align site selection to match target customer profiles.
2. Tapping into traffic patterns: Foot traffic data provides a precise view of potential customer flow at any given site. By tracking peak hours, repeat visits, and customer density, mobile data provides a window into the foot traffic volume that a location may generate. These patterns are valuable in retail and in other sectors such as healthcare, where understanding patient flow is critical; and in industrial settings, where consistent traffic patterns may indicate a strong labor pool availability.
3. Understanding the competitive landscape: Mobile data can also map out competitor locations and even track the frequency of consumer visits to these places, allowing for an in-depth analysis of market saturation and loyalty patterns. For brands evaluating market potential, understanding where competitors are—and how often their locations are visited—can be a decisive factor in choosing sites that minimize overlap and maximize customer reach.

Building A Balanced Model For Reliable Forecasting

While data offers potential, its true value depends on a well-structured model that separates meaningful insights from “phantom signals.” Building a balanced model isn’t about collecting more data, but about understanding which data truly impacts performance and filtering out the noise. This structure transforms data into a clear picture of each site’s unique value proposition.

A robust site selection model integrates four key components:

1. Consumer attributes: Demographic insights such as age, income, lifestyle factors, and even tourism trends provide a foundational understanding of the potential customer base in any given area. These details create a backdrop for a location’s commercial potential, helping align site selection decisions with customer demographics that match the brand’s identity and value.
2. Market attributes: A strong model goes beyond direct competitors by analyzing the broader market landscape. This includes evaluating complementary businesses, nearby attractions, and popular hubs that may boost the attractiveness of a location. For example, a retail location close to a gym and a café might drive additional traffic through cross-shopping behavior. Understanding these connections enables a more strategic evaluation of each site.
3. Site attributes: Physical characteristics such as visibility, accessibility, surrounding infrastructure, and logistical details can all significantly impact a site’s ability to draw and retain customers. End-cap spaces, for instance, can offer greater visibility, while drive-thru availability may appeal to convenience-seeking consumers. Integrating these site-specific factors into the model enables a comprehensive analysis that captures both the visible and logistical appeal of a location.
4. Operational attributes: Often overlooked, operational factors—such as business hours, staffing, and service capacity—can have a substantial impact on profitability. The ability to scale operations efficiently, manage high traffic volumes, and meet customer service expectations are critical to a site’s success. Models that account for these elements provide a more realistic projection of a location’s success.

Developing A Scoring Engine To Strategically Scale

With a balanced model in place, the next step is to evolve it into a “scoring engine” that ranks potential sites based on sales potential, risk of cannibalization, and geographic fit. This tool provides a systematic, scalable approach for expansion, aligning site selection with long-term business goals. Whether evaluating a few sites or hundreds, this engine streamlines the selection process by creating a roadmap that maximizes growth and profitability.

A strong scoring engine allows businesses to evaluate thousands of locations, identifying sites with the highest revenue potential and minimal overlap based on the brand’s unique criteria. Whether evaluating a few sites or hundreds, this engine streamlines the selection process by creating a roadmap that maximizes growth and profitability, continuously learning and refining as more data is added to enable even more precise forecasting over time for business expansion.

Leveraging Data For Long-Term Success

In an industry defined by rapid shifts and fierce competition, data-driven site selection isn’t just smart; it’s essential for long-term success. Retailers who leverage these advanced models to guide site selection will set the standard for strategic growth, transforming high-risk decisions into clear, data-informed strategies for enduring success.

Sill is Head of Visionary Insights Group at JLL, where he is responsible for driving growth through predictive analytics, strengthening relationships with existing JLL retail clients, and managing the design and implementation of the company’s proprietary, cloud-based A/I enabled sales modeling platform VuePoint. He leads a team of software engineers, data scientists, and project managers ready to support client planning and strategic initiatives related to real estate and development.

Putnam County, Florida Recognizes Retail

In northeast Florida, the Putnam County Chamber of Commerce recognizes the value that a thriving retail sector provides. Supported by its Elevate Putnam Campaign Investors and the Elevate Putnam Strategic Plan, the Chamber has engaged with Retail Strategies, to tap the county’s opportunity in that sector. Retail Strategies, which specializes in recruiting national retail, restaurant, and service businesses, visited the county and spent time in its communities to analyze the existing retail landscape and opportunities. As part of a three-year contract, the Retail Strategies team will match the real estate assets of Putnam County to the preferences of national commercial tenants.

Says Mark Litten, Vice President for Economic Development for the Putnam County Chamber of Commerce, “We are excited to be working in partnership with Retail Strategies to address retail and downtown development in our cities to help strengthen our overall economy and sense of place. This addresses a vital component of our new 5-year Strategic Action Plan.”