Important Factors of Site Selection. How to Augment it with Location Data

Are you looking for the perfect location to open your next store? Selecting the right site can make all the difference in the success of your business. Choosing the right site can make all the difference in attracting the right customers and maximizing your sales potential. However, with so many factors to consider, the site selection process can be challenging.

As a retailer, you understand the importance of choosing the right location for your store. An ideal site not only attracts customers but also bolsters sales and enhances your brand image. However, with the growing competition in the retail industry, you need a reliable method to make informed decisions about site selection.

Before we delve into how harnessing the location data can augment the site selection process, we will discuss  how the legacy site selection process is implemented.

Legacy site selection process:

  1. Define the Trade Area: The first step is to define the trade area, which is the geographic area which the retailer expects to draw customers from. This can be based on factors such as population density, demographics, and competition.
  2. Gather Market Data: The next step is to gather data on the trade area, such as demographic information, traffic patterns, and consumer behavior. This can be done through various sources, including government data, third-party research firms, and internal company data.
  3. Identify Potential Sites: Based on the market data, the retailer can identify potential sites that meet their criteria, such as proximity to key customers, accessibility, and visibility.
  4. Conduct Site Visits: Once potential sites have been identified, the retailer will conduct site visits to evaluate each location’s physical characteristics, including size, layout, and condition. This can also include evaluating the surrounding area, such as nearby businesses and traffic patterns.
  5. Analyze Site Performance: After site visits have been conducted, the retailer will analyze the potential site’s performance based on factors such as projected sales, rent, and operating costs. This analysis may also include a competitive analysis of nearby businesses.
  6. Negotiate Lease Terms: If the potential site meets the retailer’s criteria and is financially viable, the retailer will negotiate lease terms with the property owner.
  7. Open the Store: Once the lease is signed, the retailer will prepare the store for opening, including hiring staff, ordering inventory, and promoting the store to the surrounding community.

The length of time it takes to complete the legacy site selection process for a retail store can vary depending on several factors such as the size and complexity of the retail chain, the availability of suitable sites in the desired location, the amount of data required for analysis, and the negotiation process with property owners.

Generally, the process can take anywhere from several months to a year or more to complete. This timeline includes the time required for market research, site visits, data analysis, negotiations with property owners, and store preparation.

By incorporating location data into your site selection strategy, you can make more informed decisions and gain a competitive edge.

We’ll explore how retailers can leverage location data to augment their site selection process.

  • Identifying the Right Data Sources: Before you can begin using location data in your site selection process, you need to identify the right data sources. Here are a few key data sources to consider:
  • Demographic Data: Demographic data provides information about the characteristics of the population in a particular area, including age, income, education level, and more. Analyzing demographic data can help retailers identify areas where their target customers are most likely to reside. By analyzing this data, you can identify areas with a high concentration of your target audience, ensuring your store is well-positioned to cater to their needs.
  • Foot Traffic and Customer Behavior Patterns: Foot traffic data provides insights into the number of people who visit a particular area, while customer behavior patterns reveal how people interact with different locations. Analyzing foot traffic and customer behavior patterns can help retailers identify areas with high potential for customer traffic.
  • Competitive Landscape: Analyzing the competition can help retailers identify areas where they can gain a competitive advantage. Understanding the number and types of competitors in a particular area can help retailers determine if there is room for additional competition. Location data can reveal:
  1. The number of competing stores in the area
  2. Market share of competitors
  3. Sales performance of nearby retailers
  4. Consumer loyalty to competing brands

Armed with this information, you can evaluate the saturation of the market and choose a site where your store can stand out.

  • Accessibility: Customers are more likely to visit stores that are easy to access. When selecting a site, consider:
  1. Proximity to major roads and highways
  2. Availability of public transportation
  3. Parking facilities
  4. Pedestrian traffic

Location data can help you assess the accessibility of a site by providing traffic patterns, transit routes, and foot traffic metrics, enabling you to choose a store location that is convenient for your customers.

Making data-driven decisions for the greater impact

  • Aligning Location Data Insights with Business Objectives: Aligning location data insights with business objectives is essential for retailers to ensure that their site selection strategy is in line with their overall goals. This involves identifying the key performance indicators (KPIs) that matter most to the business and using location data to track and optimize them. For example, a retailer might track metrics such as foot traffic, sales per square foot, and customer demographics to determine the success of a particular location. By aligning location data insights with business objectives, retailers can make better-informed decisions about where to open new stores or expand their existing ones.
  • Identifying New Opportunities for Growth: Analyzing location data can help retailers identify new opportunities for growth by uncovering emerging neighborhoods or underserved markets. For instance, if a retailer notices a high demand for a particular product or service in a particular area, they may consider opening a new store in that location. Moreover, by using location data, retailers can also identify areas with high foot traffic or low competition, which can help them gain a competitive advantage. By identifying new opportunities for growth, retailers can expand their customer base, increase revenue, and stay ahead of the competition.

GeoIQ’s RetailIQ:

RetailIQ is a retail expansion platform that offers demand prediction algorithms to provide site suggestions and preference scores for fulfillment facility placement, fully backed by the ML model. This innovative approach leverages machine learning to deliver timely, accurate, and actionable insights that give businesses a competitive edge and drive success.

One of the key differentiators of RetailIQ is its ability to provide insightful recommendations for the placement of fulfillment facilities. By leveraging ML-backed demand prediction algorithms, the platform can offer site suggestions and preference scores that help businesses make informed decisions about where to place their facilities.

Data API Flow for Site Selection

Build your Site Report with RetailIQ

Retailers know that the success of their physical stores depends largely on the location they choose. As a result, having a thorough understanding of a potential expansion location is essential for retailers. This is where Retail IQ’s Site Report becomes an invaluable tool for retailers. And, providing them with essential insights and data on a specific location.

The Site Report is a one-page summary that provides concise information about the critical attributes of a location that aligns with your business objectives. It includes information on the revenue score, demographic profile of the surrounding area, and other crucial factors that you need to know to make an informed decision about your next best retail location.

By using robust data analytics capabilities, retailers can gain a deeper understanding of a particular location’s demographic, consumer behavior, and other critical factors. The Site Report can help retailers make informed decisions and choose the best location for their business to expand.

site report

RetailIQ Site Report

Merch-mix rediction/ Ideal product mix prediction:

Retailers often face the challenge of deciding what price, SKUs, or brands to carry in their stores, especially when opening a new retail location. Predicting inventory placement in real-time to maximize sales is crucial, but it depends on understanding the catchment behavior of the location. Unfortunately, many retailers still use legacy methods that are time-consuming and inefficient.

RetailIQ provides valuable insights into the micro catchment behavior of a location, including age group clusters, lifestyle preferences, regional appeal, and other behavior pattern recognition. This information helps retailers integrate their old inventory models with location data, improving decision-making and adding value to the existing inventory model.

Fleet app:

RetailIQ offers a Fleet App that simplifies and structures the retail expansion process. This app enables scouting teams to share their reports directly with decision-makers and management in real-time, allowing for faster and more efficient decision-making. The app also streamlines the scouting process and helps retailers keep track of prime locations based on crucial brand factors such as the high density of commercial establishments, average rental cost, and more.

Case study: Lenskart’s Retail Expansion

One example of how GeoIQ can help retailers with site selection is through its partnership with Lenskart. In this case, Lenskart provided data on its existing stores, including store size, ownership type, opening and closing dates, staff size, and number of ophthalmologists, among other details. This data was then cleaned and preprocessed to account for factors such as new stores with limited sales data, the impact of COVID-19, and the challenge of obtaining accurate information from franchise-owned locations.

Once the data was preprocessed, it was fed into a machine learning model that used a regression analysis to predict monthly revenue for each potential location. The model utilized the store performance data to create custom catchments around existing stores, ranging from 200m to 5km radii, to study the attributes present in these areas. The model then mapped these attributes to both performing and non-performing stores, allowing it to identify the factors that contribute to success or failure at a given location.

Using this information, the model then scores new locations based on the presence of these relevant attributes, as well as input from Lenskart on rental costs and property size. It can also predict footfall, which directly translates into revenue.

The model is able to predict the revenue that would be generated over the coming months by a newly opened store. Now the impact of choosing all the right locations when combined together can be massive.

  • Revenue, when maximized at the store level, would combine to exponentially increase the overall annual revenue
  • The opportunity lost due to faulty decisions would be minimized
  • The effect of cannibalization from new stores could be minimizedVisit our site for more information or contact us at

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