Case Study
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Client
FMCG Snacking Brand
Completed
August 2019
Timeframe
12 Weeks
Helping an FMCG brand to demonstrate the financial benefits of shelf space data to optimise product range at SKU and store level
Challenge
A well known FMCG brand wanted to optimise their in-store shelf space allocation, by combining existing sales data with new digitised shelf space data at SKU/store level. Given this has never been trialled before, a POC was required to demonstrate the possibilities of combining the two data sets along with how machine learning and optimisation algorithms could be used to identify incremental revenue opportunities
Solution
Machine learning techniques were used to understand which store layout and shelf space was likely to deliver the greatest rate of sale. By understanding the ‘space elasticities’ by SKU, an optimisation algorithm was then used to calculate the proportion of shelf space required for each SKU to maximise sales
Outcome
The POC was able to demonstrate huge potential sales increases even for one store and a handful of SKUs. By leveraging machine learning to provide targeted recommendations for each store, there was no longer a need for a one size fits all rule. Next phase of the plan is now to understand how this approach can be implemented and up-scaled across more brands and more countries
Address
info@bnmanalytics.com
4th Floor, Radius House,
51 Clarendon Road, Watford, WD17 1HP
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