Pre-Digital And Digital Problem Solving: Predicting Best Retail Location

Traditional Brick and Mortar Businesses have a hard time figuring out a good location to open and run their Businesses. Since the risk for these types of businesses are growing day by day, these businesses invest huge amounts of time and money in searching for good locations.

Presently Brick and Mortar Businesses are facing huge competitions from online businesses like Amazon, eBay, etc. The demand for consumers is a function of attributes such as household income, commodity demand, home ownership status and many other factors. For example, A Furniture store is likely to target people living in cities so they want to be in an attractive location such as inner city neighbourhoods and city malls so that they are accessible to the people living in the city.

In the past, Businesses used to use the Central Place Theory and find places where demand is the highest for their products. But to predict good locations, they had to experiment at various locations and success was very rare. Central Place Theory states that there is a hierarchy of demand for goods both from inexpensive items such as daily groceries to expensive items such as a Mercedes Car. This hierarchy changes when location changes. So the most basic questions to be asked are Where do Customers come from? How far are they willing to come for a product they want to buy? What is the probability of a customer living at location x goes to a store at location?

For example, a Grocery store chain has an upper income target consumer. Their research shows that this target group tends to be loyal customers of fresh foods departments which are highly profitable since the stock needs to be changed rapidly. Therefore, in seeking new locations, these businesses must answer a critical question, have they reached the un-met demand in this target group?

Without Digital Technology, these problems can be solved but less efficiently. Earlier data used to be collected using surveys conducted in Popular Magazines, or areas where shoppers visit the most. But as the data collected is less, it cannot efficiently portray a picture of how the business will run if it is opened in a neighbourhood.

With Digital Technology, this becomes easier. Reilly’s Law of Retail Gravitation states a theoretical means to define a trade area. It is based on the argument that people are attracted to large shopping centres to do their shopping. But are sometimes influenced by the time and distance it takes to travel to a given city. Such data is analysed by the businesses. Based on this a trade area falls under two major categories:

1. Convenience Trade Areas: It is based on purchase of products needed regularly. Ex-Grocery Stores, convenient stores, etc.
2. Destination Trade Areas: It is based on purchase of Major Products such as electronic gadgets, appliances and other products and services. Mostly people travel long distances for these types of products.

In addition to this, a business district has types of customers who shop there

1. Local Residents: People who live within the trade area. They provide the majority of business.
2. Daytime employees: Who may commute there for work or other things. They can bring potential business during workdays.
3. Tourists: These people may or may not bring business in some areas but in areas where tourism is concentrated more, they may bring huge business.

Another Digital Technology which is being used to predict best retail location is Facebook Geofencing. A geofence is a virtual boundary that detects when an object enters or exits an area. In this case, Facebook Geofence detects people with similar interests entering one of its geofence and it compares with its database and analyses so that they know people with what interests mostly enter a geofence.

These are some of the Digital Technologies that are being used currently to Predict Good Retail Locations. Data Science is the key here and the accuracy of the prediction increases as we have more data.