Advanced Geospatial analytics A powerful tool for retailers to create value in Omni channel retail
– Think Tank: McKinsey & Company
Report summarized by : Bummary
Sophisticated retailers are closely analyzing the interplay between offline and online customer decision journeys. Taking an omnichannel view of store performance allows the store to ‘get credit’ for all the sales in which it played a role, whether those sales happened online or offline. This approach is helping retailers get a better view of each store’s true economic performance and making better decisions about their omnichannel presence.
Stores aren’t going away anytime soon. McKinsey estimates show that 75 to 85 percent of all retail sales till 2025 will come from brick and mortar retail stores. However, stores tomorrow won’t be a place just to sell stuff. It will be experience centers to display products, a place for groups of friends to walk in and take selfies or act as fulfilment centers to deliver products. The probability of a store having weak sales in numbers but being a key driver to generate sales elsewhere in the omnichannel route is a big possibility in the future.
Data analytics and related tools are now capable enough to help figure out which stores play a positive effect on product sales through other channels and which ones play the role of cannibalization. McKinsey research shows that a stores Halo effects could lead to 20 to 40 percent total economic value addition for retailers.
For years retailers have been relying on traditional retail datasets for decision-making
To understand about consumers, retailers have used point of sale information, demographics, market trends and other similar data sets for years. However, development in computing and analytics has now made possible churning and analyzing large volumes of unstructured data sets to churn granular analysis that can help understand customer journeys, behavior and performance attributes previously unavailable for analysis.
Sophisticated retailers are using geospatial data and analytics delivering insights to internal stakeholders from various teams like marketing, finance, operations based on their data needs. Combining advanced geospatial techniques and machine learning and applying it to unstructured data is unleashing insights on consumers previously unavailable. Data points like opt-in e-receipt programs and anonymized mobile-phone location data shed light not just on quantity but also quality of consumers.
Geospatial analytics: A case study
This case study talks about the application of these technologies by a global specialty retailer delivering its products through its own brick-and-mortar stores, online website, and wholesale network.
The challenge: Declining retail sales because of intense market competition.
Solution: The Company turned to geospatial machine learning for insights to reverse the trend.
The process: A team of data scientists built a model customized for the brand, leveraging both internal and external data. After testing hundreds of variables, the team used geospatial machine learning to identify factors that have the greatest positive or negative impact on sales in a specific zip code.
The model helped the team figure out potential sales in each zip code and store, and provide a comparison to actual sales figures. The team further used geo-spatial simulation to estimate each store’s impact on wholesale and online sales.
The result: Data analytics team at this specialty retailer identified unique factors that contribute to creating a strong e-commerce halo. The results showed that a store has a strong e-commerce halo if it is a larger store located in an area with a high proportion of young and urban professionals. Factors like being far off from a competitive brand, located in a high traffic retail environment such as a mall with strong footfall or a powerful retail location, and having low tourist spends are all factors working to positively impact or create a strong e-commerce halo.
Conclusion: The retailer used the insights to identify stores, which weren’t living up to the sales or profit potential and which specific micro-markets provided untapped growth opportunities. Further analysis showed opportunity to generate 20 percent higher EBITDA by closing, relocating or reformatting stores. The retailer used these insights to build high performance teams and ground level plans to restructure its store network and plan capex to expand into geographies and markets with higher halo effects.
Getting started with geospatial analytics
- Do an internal analysis of inventory of data availability and advanced analytics capability.
- Retailers with limited access to wholesale accounts, siloed business units, and a handful of data scientists should consider building their data requirements and partner with external providers capable of delivering these insights.
- Companies should also build analytics team with the capability to initiate and execute a geospatial analysis. The team’s key tasks are performing analysis and generating insights to make key strategic decisions like store closures, product innovation, e-commerce halo opportunities etc.
- Build an agile structure with cross-functional teams working together. The success of omnichannel is predicated on having teams who don’t work in silos but with close involvement of all key stakeholders in the room taking decisions based on a common set of data and agenda.
Geospatial analytics is a great way for retailers to understand customers by combining the power of advanced analytics and unstructured big data sets. Making decisions based on traditional KPIs could backfire because the probability of a store to create a halo effect isn’t captured by four-wall EBITDA analysis. Using advanced analytics and big data can help companies identify drivers of value and make on-ground strategic decisions based on granular assessment of multiple variables.
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