High street retailer Marks & Spencer is claiming major improvements in its ability to keep the shelves of its outlets stocked with the right products following an under-the-radar project to introduce sophisticated predictive analytics technology.
The project will play a key role in helping the supermarket ensure its stores are stocked with enough mince pies and turkeys to avoid disappointed customers over Christmas.
The company, which reported its first increase in profits in four years in November 2014, has begun using predictive analytics to manage its suppliers more effectively and to ensure that smaller outlets receive the widest possible range of goods.
What began as a back-room project to test software that can convert complex sales and customer data into graphical images that business executives can understand is now beginning to drive boardroom decisions.
“We started to see that when we demonstrated live data to people of that level who were digitally engaged, they would want to make decisions on the spot,” said Pete Williams, head of enterprise analytics at M&S.
HOW M&S GREW ITS ANALYTICS OPERATIONS
Pete Williams, head of enterprise analytics at M&S, saw a demo of Spotfire at an analytics conference and was impressed by its capabilities
M&S began trials of Spotfire visualisation and discovery tool
The company trained analysts for four days using 10 Spotfire licences
The team identified proof-of-concept use cases, including a supplier portal
First results demonstrated in four weeks
M&S developed a prototype supplier portal and tested it with two suppliers for eight months
M&S tripled its Spotfire licences to meet growing demand
M&S bought an enterprise agreement for unlimited use of Spotfire
M&S opened up its supplier portal to a wider range of suppliers
M&S began a proof-of-concept trial using Hadoop and Spotfire to analyse customer survey responses
M&S created a business intelligence strategy function, reporting to the CFO
Marks & Spencer began rolling out data visualisation software, which allows business professionals with little IT experience to analyse complex datasets, throughout the business in February 2013.
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