AI in Retail: Beyond The Hype and Into Reality for Store Operations.
The term "AI" is often thrown around, but what does it really mean?
Every week, Quorso measures and analyzes thousands of actions that Store teams are taking in order to drive performance. With an array of data on what store teams are actually actioning we have been building a wealth of data around what really drives performance.
Our Agile Stores Lab team seeks to interpret anonymized samples of this data and what this means for modern retailers.
The Quorso platform granularly analyzes a company’s Management Information (e.g. Sales data straight from a POS system), looking for where there are big store exceptions in performance. An example is shown below, the main exceptions that surfaced for a grocery store were in category sales of wine, specials promotions, and meat waste.
Store Managers use the app to launch an action plan to improve the identified exception. The plan contains details like root cause (stock, planogram, training etc. issue), description and target. We’ll call this action to improve an exception a ‘Mission’ from now on.
Managers then use Quorso to granularly track the impact of each of their Missions, at a level of statistical robustness it would be too boring to go into here.
This allows Area Leaders and central teams to see with a high degree of accuracy what their store teams are focusing on to drive performance, the actions they are taking, and the impact each of their Missions is having on business targets.
Our Agile Stores Lab aims to answer definitively with data the questions everyone has always wanted to know about what really drives performance in stores.
Ross McMillan is heading the lab. Ross is not only literally a nuclear scientist but wrote demand forecasting algorithms for the UK's biggest grocer, Tesco, and was a key part of McKinsey's retail operations network before joining Quorso. So we can say he's pretty comfortable with the numbers.
For our first report we’ve analyzed 22,583 Manager Missions that were solely focused only on improving Sales. Retailers in the selected sample set had large store networks i.e. greater than 100 stores.
The report will go into some insights we’ve generating around:
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