The business intelligence team at Pret is split between technical staff who run the servers, applications and the data warehouse, and the reporting staff.
The reporting team doesn't act as a BI service provider though: "We're not waiting for someone to fill out a report request. They know what they want in terms of business performance. We as BI professionals know how to build that report, know how to do performance management, we know how to visualise it, so it's a dialogue rather than a blind service."
Pret has 350 shops in four regions (UK, Europe, USA and China) with a pipeline that will see more than 400 shops in the portfolio by the end of this year. Each store would traditionally have an existing manager come over to help with the launch. However, with an increasingly international footprint Pimm-Smith sees the need for analytics to help share knowledge remotely. "This is relevant for our analytics in terms of how to manage the quality around those ever expanding Prets around the world," says Pimm-Smith.
By serving staff historic information on how factors like weather and special events affect buying patterns, managers can make smarter decisions when it comes to stock and labour, decisions that have a direct impact on profits. For example, the average Pret store makes around 60 percent of its daily sandwich and salad output in the morning and then makes stock decisions on the fly. Good, accurate analytics are critical to this way of working.
Predicting successful stores
Pret doesn't do all of in BI in-house though. Pimm-Smith looked to bring a consultancy in for a data science project that would help the company decide on new shop locations. As Pimm-Smith put it: "There is no low-hanging fruit left for Pret locations in London. In America we are eating that up. It is harder to find a Pret that is just going to be successful."
By using a consultancy Pimm-Smith is able to blend Pret's internal data with more augmented data which is harder to come by, such as store visibility, competition data and cannibalisation impact, census, and mobile phone footfall maps. "We look at 100 data points, they're looking at over 10,000," says Pimm-Smith. The metric he wanted was a weekly average sales revenue for new locations so that Pret can make a buying decision that would be more accurate than his team could have provided.
Pret a Manger is no longer a small business, and with a growing international footprint the way it delivers real time data to its store managers is key to keeping not just Londoners, but busy people all over the world well fed.
Source: Computerworld UK
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