Role of Geospatial Data Analytics in Retail Market Expansion
Role of Geospatial Data Analytics in
Retail Market Expansion
Anoop Nair
Business problem
The retail industry is going
through a turbulent phase with a large number of store closures
simultaneous with a manifold increase in sales revenue. Geographical
expansion helps retailers to sustain their revenue. But the identification of
ideal markets and suitable operation sites has challenged retailers enormously and
they are compelled to explore and utilize new approaches and data to pick their
future sites. Geospatial data analytics is one such avenue that has gained
popularity recently. Retailers select their operational sites in the new market
to reduce their distance from the customers or to enhance
their attractiveness to the customers. When the new market attributes are spatially
and temporally dynamic, site selection becomes very complicated. Consequently, retailers
rely on up-to-date geospatial data analytics to make their market expansion decisions.
So, how do retailers employ geospatial data analytics to determine potential
operation sites during market expansion?
Study method
Evidence-based research (EBR) was conducted
using rapid evidence assessment (REA) of high-quality articles published after
2017. Huff’s gravity model was utilized as the theoretical
lens and a detailed qualitative coding and thematic analysis uncovered crucial
findings.
Findings
Geospatial data analytics has a
significant role in market research during geographical expansion as it
(i)
Helps to determine the attractiveness of retail
delivery locations.
(ii)
Helps to determine potential customers for retail
delivery in a new market.
Geospatial data analytics assist
retailers to identify the size, shape, and convergence of market area, proximity
of stores, and delivery sites to customers. Location data help retailers
identify the commercial feasibility of the retailer site.
Retailers use geospatial data analytics used for planning
retail delivery routes and determining the best delivery
strategy such as direct delivery, en route delivery, and/or centralized
delivery. Geospatial data analytics provide details on whether the retailers
can avail alternate means of delivery, utilize their
spare capacity, and/or logistics using third parties. Spatial data analytics also
provide retailers with suggestions on potential operation models for delivery
sites.
Geospatial data provide insights into
the customer population size, customer location attributes
such as urbanization, customer demographics, psychographics, and customer shopping
pattern. Geospatial data also help retailers identify customer groups' access
to transportation, traffic, and customers' reluctance or inability to shop at
the store. Retailers can formulate their delivery strategy based on the
location of their high-value high-priority customers.
Retailers can utilize geospatial data
analytics to make strategic decisions on whether home delivery or
delivery to customer pickup stations is viable in each market. Delivery
location attributes inform the retailer whether it is economically feasible to
deliver at each location based on the volume of sales and cost of delivering
the order. Spatial data also help the retailers identify the high-value
customers that can serve as delivery route anchors and manage other deliveries
around those delivery hubs. Source and destination location also help retailers
determine the feasibility of decentralized order fulfillment locations and/or
employing third-party resources.
Recommendations
(i)
Retailers must use geospatial data analytics to plan
their delivery tactics. Spatial data must be used to determine the position of
high-value customers and the cost-effective ways of delivering to those
customers such as the frequency of delivery, time of delivery, whether the
delivery can be sourced from the stores or (de)centralized warehouses, whether
the delivery can be bundled with other orders, or whether the retailer can
utilize external delivery vendors to efficiently serve the customers based on
their location.
(ii)
Retailers must use geospatial data analytics to
develop their product promotion strategies. Retailers can customize their
product assortment in physical stores and websites based on the preference of
customers in different markets using spatial data. Spatial data analytics can
be used to formulate the pricing strategy based on the cost of serving the
customers and returns from sales in each market, sub-markets, and/or neighborhood.
(iii)
Retailers can use geospatial data analytics to
formulate strategies that would entice customers to shop frequently by
providing tailored incentives that are appealing to customers in each
sub-market.
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