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?
To be the simple solution to a
big business problem
This article was originally published at GenerationCFO.
One of the largest retailers in the UK had a problem with the way its stores used their floor space. Displays were not well presented and positioned, and the space available was not used efficiently. This retailer worked with us to come up with a solution to streamline processes and implement fixes across its 1,400 stores.
By creating an automated data pipeline for POS data from all stores, split across 480,000 product lines and 1,500 sub-categories, sales could be benchmarked against a control group of similar stores. This information is now being shared with the chain’s general managers and section heads each week. They then pinpoint issues, take targeted action and continuously optimize their stores’ performance.
The regional management could immediately see the value in the tool. Action plans were created based on the findings from the data – managers record each action they are taking to drive sales, from keeping racks of jeans tidy, to replenishing stocks more quickly. Each participating store takes at least 15 actions every week; the effective ones are added to the company playbook and rapidly scaled across the business.
As a result, revenue has increased by 1.9% on average – the equivalent to approximately £190m in extra sales across the company.
This multi-national transport group needed to increase fleet efficiency and productivity across the US and UK. It worked with us to analyze financial data and take effective action to help with the following:
The data were analyzed and used to develop tailored weekly plans for depot managers to action. For example, a depot manager used Quorso to save £9,000 by canceling unnecessary phone contracts. By scaling this up across 22 depots, the business saved more than £100,000.
This major restaurant group, which operates 650 restaurants under various brands, wanted to improve energy usage across one of its chains. It also wanted to encourage people to buy more starters, side dishes, drinks and desserts with their meals.
The Quorso data team built a pipeline to monitor weekly sales and energy usage at 80 of the restaurants. It’s analytics layer then compared sales of each menu item against a control group and measured energy and gas use by time of day.
This gave restaurant managers a view of their sales by time of day and location-specific guidance on how to improve them. Marketing teams could see just how successful their promotions were in real-time. Managers started trying new promotions and ideas to boost sales and Quorso automatically shared their most successful efforts with other managers and locations across the chain.
After six months, the business had tried more than 400 different ideas, each one digitally documented, tracked and measured in Quorso. This drove a 2% increase in sales, and energy costs fell by 8%.
These three examples show just how powerful data analytics, clever planning and measurable execution can be. With precise objectives, automated processing, trackable actions and most crucially, better data and recommendations in the hands of the managers that make things happen, you could find the hidden revenue in your business, too.
These three examples show just how powerful data analytics, clever planning and measurable execution can be. With precise objectives, automated processing, trackable actions and most crucially, better data and recommendations in the hands of the managers that make things happen, you could find the hidden revenue in your business, too.
