For retailers, there is nothing more important than a clear, data-driven picture of store health. Whether you are a C-level executive looking at the big picture to identify macro-level trends, a corporate analyst investigating the complex identifiers of store success within a particular region, or a store manager making sure that your location is missing no opportunity to boost sales, site monitoring should be a top-to-bottom organizational priority.
The problem is, the methodologies that many retailers are leaning on to monitor and understand site health have a foot stuck firmly in the past. Traditional methodologies leaning predominantly on sales figures and static, often outdated demographics will leave analysts a step behind at best, and at worst, failing without the mechanisms to understand why.
There is a reason that relying on sales data and demographics alone won’t cut it any more. Modern location data streams provide a clearer, more detailed, and up to date view of the consumer. Any site monitoring process that doesn’t keep location data front and center should be considered incomplete, as location is a core component of the micro-level details that determine store success.
The near real-time nature of mobile data makes it a far more powerful tool that traditional demographic sources, like census data which only presents a snapshot that grows promptly out of date.
Mobile data represents real human behavior and allows analysts to see exactly how customers are interacting around a particular store. Analyzing this consumer behavior, monitoring foot traffic, and diving deep into the demographics of mobile data lets store managers stay agile and answer complex business questions.
The clear, up-to-date demographic picture around a site should be integrated into any site-planning workflow. For a store manager, it should inform marketing and inventory decisions. For a corporate analyst it may impact decisions around new product development and inform additional site planning decisions. And for C-Level executives, it can help make tough decisions like whether to convert a store’s format to better match the needs of its customers.
Aggregated and anonymized transaction data can be a powerful tool for understanding store health, especially when used in conjunction with an organization’s own sales data. Credit Card spend insights can show analysts where the majority of consumers shopping in a certain area are coming from (as seen below), can elucidate trends in spending over time in a neighborhood, broken out by industry, and can provide scoring for sites, so that analysts can know what to expect from a store and highlight those moments when a location is performing unexpectedly.
These modern data streams empower store managers, analysts, and executives by untethering themselves from static data sources that grow rapidly out of date, and replaces them with dynamic data streams that paint a more accurate picture of the situation on the ground.
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Using organizational data sources, like online and brick-and-mortar sales data, and customer loyalty program data, in combination with new data streams empowers analysts to understand their stores better and create benchmarks for what success looks like at the local, regional, and national level. But simply examining this data leaves stones unturned. Geospatial analysis completes the site monitoring equation.
Analysis of store catchment areas for example, can allow analysts to understand how a new store location might cannibalize an existing locations profits. Similar geospatial analysis can show how a competitor entering the market can be expected to impact overall sales.
In a field where poor decision making can cost millions and put stores at risk of closure, a Location Intelligence solution like Reveal allows analysts to predict revenues by site, rank new locations by similarity and carry out cannibalization analysis faster and more precisely.
For store managers, corporate analysts, and C-Level executives, using traditional site monitoring tactics and static demographic data is a surefire way to end up falling behind. Store managers can dig deep into the on-the-ground situation at their location, identifying patterns and more effectively serving customer.
Analysts are able to more accurately report on regional trends, find successful and unsuccessful outliers, and make recommendations to improve store health across the whole network. And executives can make sure they are equipped with the most up-to-date intel when making the big decisions around expansion or reduction, and wholesale company realignment.
Integrating modern derivative datasets to work alongside existing customer data, and analyzing it all using geospatial analytics will paint a far clearer picture of site health at all scales.
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