Location Analytics puts zing in Business Intelligence
Location Analytics puts a zing in Business Intelligence (BI) applications. BI applications became part of main stream IT more than a decade ago. Large IT users like Telecommunication companies, Banks and other financial institutions, Manufacturing organizations, Retail companies, have been using BI tools to discover insights about their customers’ buying behavior and preferences from the business data that resides in their IT applications like ERP and CRM. The data that BI applications throw out in tabular forms can be visualized on maps by using Location Analytics and further inferences can be drawn to assist in better decision making for business investments. Adding geographic location to the business data and putting it on a map can dramatically enhance the value of the data and can provide insights such as patterns, trends and relationships between different parameters in a visual manner. However, this geographic aspect though easy to incorporate has been missing from business analytics solutions to a very large extent. With the easy availability of map and demographic data more and more organization are looking to add geography or geospatial technology to their business analytics applications.
Location is fundamental to all aspects of business. All departments within an enterprise deal with location data; material suppliers have location data, manufacturing facilities and offices have location data, employees have location data and customers too have location data. Analysis of all this data along with geospatial technology can provide new insights that were not possible before - like, heat map showing concentration of customers in different areas; this can be further analyzed based on parameters like age, education and income level; tracking and communicating with employees in case of an incident or emergency situation. Connected mobile devices like smartphones have made it easier to implement location analytics solutions.
Following are a few more illustrative examples (Credit: HBR Blog, How Location Analytics Will Transform Retail by Tony Costa):
Store Design: After analyzing traffic flows in their stores, a big box retailer realized that less than 10% of customers visiting their shoe department engaged with the self-service wall display where merchandise was stacked. The culprit turned out to be a series of benches placed in front of the wall, limiting customer access. By relocating the benches to increase accessibility, sales in the department increased by double digits.
Marketing: A restaurant chain wanted to understand that whether or not sponsoring a local music festival had a measurable impact on customer visits. By capturing data on 15,000 visitors passing through the festival entrances and comparing it to customers who visited their restaurants two months prior to the festival and two weeks after, they concluded the festival resulted in 1,300 net new customer visits.
Operations: A grocery store chain used location analytics to understand customer wait times in various departments and check-out registers. This data not only enabled the company to hold managers accountable for wait times, but it gave additional insight into (and justification for) staffing needs for each department throughout the day and optimal times to perform disruptive tasks such as restocking shelves or resetting displays.
Strategy: A regional clothing chain was concerned that opening an outlet store would cannibalize customers from its main stores. After analyzing the customer base visiting each store, they discovered that less than 2% of their main store customers visited their outlet. The upside: the outlet gave them access to an entirely new customer base with minimal impact to existing store sales.
Location Analytics is maturing fast, not having access to the information similar to the above examples is like flying blind-folded. Can organizations afford to do that any longer?