Retail, as with other sectors, is going through a digital transformation process. This is partly driven by the availability of technology and partly by the sector attempting to adapt to the expectations of consumers. One way in which the sector is starting to measure, develop targets and to improve the experience of consumers is through the application of big data derived from location analytics. Such data is collected by digital technology.
With consumer expectations, a review of behaviors by Gary Sankary for Retail Drive notes that people are increasingly searching for, comparing, and purchasing merchandise online and at any time. This can range from tracking down the location of a department store for an in-stock item by checking a cell phones to having an item delivered.
The consequence of this means there has been a shift in the balance of power towards the consumer. To stay competitive, businesses must ensure they are doing everything possible to interpret and predict customer behavior. This includes the speed of delivery and there are signs that consumers are beginning to expect same day deliveries.
Reacting to this is not straightforward, since retailers require sophisticated digital technology to manage assets, optimize delivery routes and meet customer service expectations. This requires the use of real-time data analysis and interactive maps. From this things need to be controlled in terms of time and also spatially.
For this, intelligent spatial analytics platforms are appearing on the market. To be effective these systems need to optimize deliveries and to signal to customers about any delays. An example comes from Alteryx, which provides a self-service analytics platform that allows for analysis of geospatial data to allow for a variety of data sets to be connected and geocoded. The system also allows for demographic data to be inputted (this might be used on the basis that millennials have faster expectations about delivery times than, say, baby boomers). Another thing the software can do is to create trade areas based on drive time, and blend data by physical proximity.
A different approach comes from Esri, where location analytics are used to determine the best location for a store in relation to a target group of consumers. With this, the company states, sales revenues can be increased by “understanding precisely which customers want which products and services, and targeting marketing to specific customers more effectively.”
A third platform comes from Data Science Studio which utilizes spatial technology to construct visual maps, generated from datasets. Such maps contain engaging and interactive elements like such slide bars and color-coded filtering. In one example application, the U.S. IRS designed a heatmap to display student loan debt across the U.S. This included an accurate regression model that was used to predict the proportion of student loan debt in a given ZIP code.
Being able to visualize business data against an accurate map is an important step in the digital transformation of retail. For those companies that elect to use such platforms, these types of location analytics help to reveal hidden patterns, relationships and trends; providing important business intelligence for the retail sector.
