Retail store expansion strategies are one of the most fundamental issues for growing retailers. Opening a new store can be a game changer if you get the location right, or your new store could be doomed to failure if the location doesn’t attract enough customers.
In addition to geographical factors, such as transportation accessibility and real estate prices, demographic factors and mobility patterns in the areas of interest play a key role in decision making. These data on population, purchasing power and consumption habits are what generate an optimal expansion strategy.
And it seems that the end of the pandemic is encouraging the retail sector to return to the upward path it was on before 2020. According to the NRF (Natural Retail Federation), the U.S. retail sector announced that new store openings in 2021 were double the number of stores closed in the same period.
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Organizations are relying on the efficient use of big data and location analytics as the backbone of all their processes and decision making, reducing the risks of new investments associated with expansion plans and new store openings.
The use of big data management techniques simplifies the analysis of expansion managers, since it is possible to reduce hundreds of potential locations to a limited group with real sales potential, defined by the model.
Today, thanks to Big Data, store site selection is much more efficient and less risky.
By incorporating Big Data and foot traffic analytics, you can minimize the risk associated with investing in a new store location and unlock growth potential. This is especially valuable for retailers, delivery services and insurance. Likewise, venture capital firms benefit from location evaluation in assessing the market potential of a target company.
Factors to consider when expanding my outlets to other locations in the country:
Connectivity and mobility: stores should be easily accessible to customers and should be connected to different modes of transit, while traffic flow should be adequate to ensure high visibility.
Ease of parking: the availability of parking in or around the store is one of the crucial factors determining consumer footfall.
Cost-effectiveness: identifying locations strategically helps to reduce operating costs, such as rent, significantly, while maximizing sales potential.
Presence of competitors: it is important to know the market penetration available in a given location so as not to be drowned out by excessive competition, as this can lead to cannibalization.
Culture of the local population: understanding the local environment helps to gauge the needs of the local population and adapt your offerings accordingly.
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Economic power of the local population: analyzing the purchasing power of the local population is important to know whether the products offered can find a stable market in a location.
Foot traffic: visualizing how customers move throughout the shopping area provides information on the impact of promotional campaigns and store displays.
Demand forecasting: predictive models take historical sales data, identify trends and predict when they will recur.
Points of interest: observing changes in cross-shopping behavior, consumer travel patterns and customer affinities for a customized area of interest over time allows:
- Gain insight into footfall for different areas over time.
- Identify precise consumer foot traffic patterns.
- Observe visitor traffic to competitor locations.
- Precise targeting of real estate, stores or digital advertising sites.
Area profiling: comparing trends in shopper behavior between different geographic areas over a period of time.
At PREDIK Data-Driven we find the best potential locations for your business with our predictive modeling, obtain location suggestions that have similar characteristics to your most successful existing locations, create a revenue forecast and risk forecast providing sales forecast and management information to decision makers.