The COVID-19 pandemic undoubtedly transformed the retail industry. The blending of industrial and retail, an emerging trend pre-COVID, was exponentially accelerated by the increase in online shopping during the pandemic. This has prompted retailers to reexamine their distribution networks and big-picture strategies in order to adapt and thrive. Retailers have been compelled to fast-track delivery services, and this unexpected shift has brought an eagerness to apply data science to distribution and e-commerce strategies.

What can data tell us, and how should it be used?


When ordering online, customers want to get their product as quickly as possible. Data-driven planning can help retailers optimize their distribution networks to discover how a new store location will affect e-commerce activity. It can also help them maximize their existing footprint to facilitate last-mile delivery, pick-up and returns.

There are a number of ways a retailer can use data to optimize distribution. For example, a grocery retailer may want to weigh the benefits of expanding its delivery service to more locations. For this type of analysis, CBRE’s Retail Analytics team uses massive mobile data to develop precise trade areas. This information is joined to demographic block groups with a high propensity for online grocery purchases in the previous 30 days, and factors such as delivery miles, costs, and impediments (factors that slow down the movement of goods such as traffic, vehicle capacity, number of orders, loading and unloading times, employee hours and pay, fleet size) are taken into account. Finally, the analysis is run through a routing algorithm to identify how many homes can be served in a single delivery trip—maximizing reach and minimizing costs. This analysis will identify the optimal existing locations within the network for implementing grocery delivery.

Retailers can also apply data science to leverage unit performance metrics and bolster online purchasing options. A network performance analysis can help retailers identify underperforming locations. Those units can then be analyzed as opportunities for improving e-commerce services through possible conversion to buy online, pick up in store (BOPIS) sites. 

Data science can be a powerful tool in growing last-mile delivery services. Using a retailer’s portfolio performance metrics, along with location data to estimate online orders, analysts can apply an infill scoring algorithm to determine how a location would work as a last-mile distribution site. The infill scoring examines distribution centers, delivery companies, trucking, businesses around sites, coverage for a market and urbanicity. These insights can then be applied to a lease-renewal analysis and filtered by lease expiration to prioritize the best opportunities for distribution hubs. Identifying locations that would make successful last-mile distribution centers or BOPIS sites can help with estimating inventory in those locations.
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The balance between e-commerce and bricks-and-mortar


Retailers can also use data in omnichannel analysis to evaluate bricks-and-mortar and e-commerce sales activity to refine a big-picture strategy. The number of options available range from data insight guidance to predictive analytics.

If you’d like to examine your e-commerce business and its relation to site selection, a directional hotspot analysis can assess online orders to provide insights on areas of high online activity where you may want to consider opening a store. While not predictive, the analysis provides information to consider in site selection.

In another scenario, perhaps you opened a new location and want to compare its performance with online sales. With enough data points to study, an impact analysis can be conducted to assess how opening a new store affected online orders. Did you gain customers, or did opening the location take away from online orders? This analysis can identify trends and factors to help determine levels of cannibalized sales that can be applied when considering future locations.
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Analyzing the effects of store closures


With drastic business changes accelerated by the pandemic, retailers are searching for answers as to how store closures affect performance and customer retention. An in-depth predictive analysis can use retailer data to identify the percentage of customers lost from a store closure. The analysis can help retailers determine where to open a store to increase the number of customers who could be serviced.

There are a multitude of ways location intelligence and analytics can be applied to drive growth and evolution in this new world of multi-channel purchasing. Whether using your data to find the right balance between e-commerce and brick-and-mortar, provide faster delivery or develop a more comprehensive growth strategy, Retail Analytics is key to making smart business decisions.