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Two month ago we at CTAC put our heads together on what could be the best business case for the SAC Analytics Designer Hackathon. The solutions we present is a shelf profit analyser – under the tag: “How to optimise Coke Selling”.  

In our application we embedded the science behind product placement in our solution. The science of how to optimise the shelf layout is extremely complicated and depends on many different factors. While there are many professional solutions on how to optimise the shelf layout, there are not many solutions which evaluate the efficiency of a shelf and analyse the optimum shelf margin. This is exactly the solution we provide.  The business case behind our solution can be found here.

The strength of the solution is that it shows store specific data combined with data from likewise stores in the company, also it shows the lay-out of the shelves in different stores. It brings the power of analytics to the decision makers close to the field. Together, the store advisor and store manager can analyse shelves and plan their target sales volume for the future. The Analytics Designer enabled us to create a full process flow with a logical drill path and extended filter possibilities to create a unified and meaningful solution.


In our story we tell you about Tom the store advisor and his mission to increase the ‘Coke’ margin in the store in Tilburg.

In order to maximise his Coke profit, Tom needs to evaluate his target store based on the relevant store cluster (location, demographics, size, revenue).


Then Tom goes a level deeper. On the Analysis page he is interested to compare the subcategory of Coke with other subcategories within the same category (Water, Energy drinks, Lemonades) in order to understand the evolution of coke sales over the last month.

He immediately sees the sales trend per category, and can set it off against different KPI’s such Actual Sales vs Target, Average Revenue, Store m2.

We have also enabled Tom to compare his Sales with the best performing store in the category.

On the Shelf Analyser page Tom wants to compare the different products on the shelf based on the product mix, the sold quantity and the revenue it generates. The shelf with the supporting charts allow Tom to analyse the revenue of his shelf, based on key KPI’s such as quantity sold, revenue and margin.


His insight he can then use in the Margin Simulation, where he can adjust his sales target version for the coming month. This simulation input can later be used on the Shelf Analyser page in order to identify his potential shelf profit and optimise his margin.


We embedded a process flow, which allows to evaluate the product subcategory sales based on the store cluster, competing categories and product mix of the shelf. Combined, this analysis  enables the store advisor and store manager to understand current market and sales trend, and adjust their subcategory sales targets which will maximise their profit.

Check it out and make maximum profit.

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