Location Analytics for Optimal Retail Site Selection
Retail Site Selection can have a huge impact on business success
The site selection problem is one of the most fundamental problems for growing businesses and new entries. Opening up a new business location can either be a game changer if hitting the right spot, or your new business site could be doomed to fail if the location is not attracting enough customers. Hence, there is a high risk of making wrong investment decisions. There are many factors that influence this essential decision that it becomes one of the most challenging topic for every business.
The Problem of traditional location decisions
Besides geo-based factors like transportation accessibility, real estate prices and availability of qualified workers, also socio-demographic factors play a vital role in decision making. The latter group of factors determine the size and distribution of the potential customer base. With data on population, purchasing power and consumption habits demand forecasts can be generated.
Having so many variables that influence the revenue of a specific location, the traditional approach of evaluating a site by manual survey of the land and building a competition landscape becomes costly, time consuming and ultimately too complex.
Gaining the best return on your investment
Luckily, with the rise of Big Data Analytics, there is no burden of incorporating a bunch of different factors at a time anymore. By leveraging available geo-based and socio-demographic data from the internet and integrating business data from existing stores, retail site selection becomes a lot more efficient and less risky.
Simulations can model predicted market shares in different isochrones given existing competitors, substitution products and pricing. Moreover, the cannibalization effects of the new store on existing facilities can be calculated by measuring the effects of overlapping catchment areas.
When incorporating Big Data and Geo-spatial Information Systems (GIS), risk associated with the investment into a new store location can be minimized and new growth potential can be unlocked. This is especially valuable for retailers, delivery services and insurances. Also, Private Equity firms profit from location assessment in the evaluation of a target firm’s market potential.
How we find the perfect location for your business
As a leading data science company in retail, we excel on our experience with the largest food retailers in Germany and localization projects in other industries. In our analysis, we combine new technologies like Big Data and Machine Learning with GIS and risk models. Of course also individual preferences and corporate regulations are communicated beforehand and considered in the optimization.
We can help you in the following ways:
- Find the best location out of a list of potential locations with our scoring model
- Get suggestions for locations that have similar characteristics to your most successful existing sites
- We create a revenue forecast and risk prognosis as an interactive map (see image)
As a result, we deliver a sales forecast for potential sites and visualize the findings on a map to provide management insights for decision-makers.
In our analysis, we use GIS, Big Query, and Machine Learning to combine and analyze data from different sources. Your business data is linked with external data like weather, credit card transactions, and movement data. Of course, individual preferences and corporate regulations are communicated beforehand and considered in the optimization too.
Check out our success stories below or talk to one of our Data Science experts.
New: nioSPOT – Location Intelligence software for retailers
nioSPOT is our software solution for location analyses in retail.
The assortment must also be individually adapted to the location
To get the maximum out of your investment, we are also happy to consult you with category management analytics. We help you to set up the ideal assortment given the consumer class in the new store’s catchment area.
