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Key takeaways

• Collect the addresses, sales, and sizes of existing and potential stores

• Sources include mapping software, public databases, web analytics

• Convert location data into geographic coordinates

• Apply spatial statistics and spatial modeling

• Perform white space analysis

Demand, recruitment, suppliers, and safety

An optimal store location is in an area where the company’s product will be in high demand and with low competition. Expanding to a space where surrounding businesses and shops complement one’s own is a great way to ensure the location fits into the local market. Retail industry volatility increased by 250% between 2023 and 2010, with competitors trading $200 billion in sales every year.

Choosing a location where store employees feel protected and safe is paramount, especially for stores with inventory that may face a higher risk of theft and burglary or if you or your staff will be in the store alone at night.

Finding high-quality employees is essential to any business and its success, so it is crucial to plan your location around where employees want to work or can easily access.

In 2022, 85% of retailers in the UK were impacted by supply chain issues. The percentage was 80% for US retailers. 78% of merchants in the gardening and DIY sectors and 67% in the toddler and baby sectors faced major issues. Goods shortage affected 67% of US merchants and 68% of UK merchants.

Supply chain disruption remains an issue in 2024. The best retail location is one where it is easy for you to connect with your vendors and suppliers. If not, you might face delays or frequent issues with inventory levels.

What data do you need for your retail store locations?

You need to gather your existing and potential stores’ addresses, sales, and sizes. You can use various data sources, such as public databases, surveys, social media, web analytics, and mobile apps. You can also use GIS software to capture and store spatial data. Thankfully, many customers still prefer in-person shopping. After the pandemic, 59% of US customers said they would continue curbside pickup. 30% of shoppers prefer curbside or buying online and picking up the goods in the store over having them delivered.

54% of retailers are focused on customer experience, ahead of data-driven (14%) and cross-channel marketing (16%), mobile (11%), and programmatic purchasing or optimization (4%).

Processing data via geocoding

Geocoding is the process of converting location data into geographic coordinates. These coordinates are then converted into a flat map representation. Various methods of spatial analysis are available, such as spatial statistics and spatial modeling. Spatial statistics measure and test spatial data relationships, patterns, and trends, such as the density, distribution, and correlation of stores and customers. Spatial modeling builds and simulates scenarios and results, such as product demand and supply or the impact of opening a new store.

Maps display your data’s location, spatial distribution, and proximity. These include the locations of your customers, stores, and competitors.

Researchers collected indoor location data of hundreds of thousands of customers in two Chinese shopping malls (DYC in Beijing and LH in Chongqing) over one week. The temporal customer flow patterns revealed that flow was highest on weekends and lowest in the middle of the week. DYC saw hourly flow peak on weekdays, while this was mealtimes for LH.

According to the analysis of the results, which was published in the International Journal of Geo-information, the temporal pattern difference between the two malls was due to location differences. The store location, reputation, and visibility strongly impacted flow density. Clothing stores were affected by the distance to the floor center (only LH) and elevators. Graphs and charts show non-spatial data values, such as store sales and customer traffic to stores.

White space analysis

White space analysis identifies sales figures, market competitors, customer details, risk assessments, etc. It helps answer the question of where to open your next store. The best locations are those from which competitors are absent and where the expected revenue is higher. Revenue prediction models within specialized software are applied to find whitespace areas. You can use a spatial filter to reduce the search area and set other criteria, such as a minimum revenue amount.

Recap

Consider demand, recruitment, suppliers, and safety

The data needed for your new store locations

Processing data via geocoding

Conducting white space analysis