Background: Whenever you decide to set up a brick and mortar store for your business, one key aspect is the site selection process.Your business maybe exceptionally good and valuable but unless it is approachable and easily accessible to clients and even your employees, it is likely that people will choose to go somewhere else for the same product. Site selection is the process by which you can evaluate the needs of your business against the potential advantages of a new location. For example, a 24x7 retail store, which relies heavily on visibility, needs to be placed in places where it can attract maximum customers, such as a residential complex or fuel stations. Similarly, a strategically placed lifestyle store will attract more customers specially impulse buyers. CEO of Zestl Software Private Limited, Hardik Gandhi explains the science behind a business site selection.

Site selection is an essential component of business expansion and has a big impact on its success or failure. Different verticals like retail, services, and manufacturing have different needs from the sites they select. Traditionally, site selection was known as a “best-guess kind of game” but Artificial Intelligence (AI) and analytics-driven by big data are rapidly turning it into a science. So how can you beat your competition in these exciting and competitive times, when real estate gets lapped up in the wink of an eye?

Which model do you use?

Site selection models started with Newtonian physics – where the attractiveness of the location was related to the distance between consumer’s residence and business was the most important component. Early methods like the Proximal Model and Reilly’s Retail Gravitation model focused primarily on spatial interaction (gravity) determined on proximity of the location in relation to population volume, density, and flow.

Then came the Analog Model that used demographic data and spending habits. The last 10 years have revolutionized both the availability of data and our ability to analyse that data. This has opened the door to customised models that use quantitative factors (population density by age, average spend etc.) and qualitative factors (proximity to a competitor, direction of traffic flow etc.). Satellite data from Geographic Information System (GIS) providers and Regression Analysis that tracks store performance down to specific causal factors have the potential to predict the success of a site with much greater probability. Store chains have an advantage here because their regression analysis models can leverage sales data from existing stores.

Most site selection models today still reside in data analysis. Data silos, quality of data and other challenges associated with spreadsheet tools seriously hamper the value companies can get from their analytical models. Here I evaluate ways in which you can easily join the revolution to build better models one step at a time and improve your hit rate on new sites.

The site selection toolkit

We’ve laid out a set of questions that will give you an idea of what you need to have in place to start building data models that improve your hit rate on new stores. These basic capabilities will empower your analytics geek, guide business development and make life easy for operations.

How much science is there in your art?

Your business development or real estate team might be evaluating tens and hundreds of sites every month. How easily can you access all the data you have aggregated over the last three years?

• Enriching your data: Are you augmenting data your field team collects with data that comes from GIS providers that analyse satellite photographs and other sources that deploy technology or manpower you currently do not have?
  
  
• Linking site survey data to sales data: Have you built the ability to evaluate the attributes of an existing site with respect to its success or failure?
  
  
• Scoring: Can you score your sites objectively based on qualitative and quantitative factors? How much time and effort does it take to go from the deployment of field personnel to get a score?
  
  
• Data flows: Are you able to give your in-house analysts and decision makers an instant access to all the data and reports you have on a site without consulting multiple individuals, sending and receiving a blizzard of emails and spending precious time and effort in making reports?
  
  

Now envision a world where your business development/ real estate team is surveying half the sites they currently do to select one, your success with new outlets is up by 30% or more and Return on Investment (ROI) timelines are shorter by 30% or more. This is something AI has the potential to deliver today and the decreasing cost of relevant technologies makes this goal affordable. But it will take a sustained effort from key stakeholders to get there. Market leaders like McDonald’s have demonstrated the impact GIS data paired with advanced analytics can have and in time this will be the new normal.

Zvolv works with a number of retailers, large and small and helped them shrink store launch timelines by making smarter decisions faster. 

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