Category management: how to identify the biggest business opportunities

The context

At the end of each period, the category managers analyze the performance of their category within their retailer, with a more or less detailed analysis of the competition, and an in-depth analysis of their brands and products.

It takes a lot of time, creates stress, and the quality of the diagnostic varies depending on the level of analysis of the cat man, his business expertise, his available time, the importance of data within the team…

This article focuses on 2 key points that have a strong impact on the quality of the analysis and the confidence that can be had in its performance diagnosis:

• The analyzes are mainly made on the basis of % change
• The human being has a limited capacity for analysis and synthesis

Not quantifying the % of change can lead to wrong conclusions

In 99% of cases, out of habit and lack of an appropriate tool, analyzes are made by only looking at % changes, which can be misleading, for example:

• A small % of evolution on a large mass (volume, value) can have a strong impact on the business
• Conversely, a large % of evolution on a small mass will only have a minimal impact on the business (if this % is strong each period, it is obviously a trend to be followed meticulously)

Hence the importance of quantifying the impact of the evolution of explanatory KPIs (WD, price, linear, etc.) on sales value and/or volume (or margin for key accounts).

And this on all business drivers: brands, categories, brands, sub-brands, ranges, formats, up to the sku.

This quantification (or massification) of the % of evolution makes it possible to identify the biggest absolute gains but especially the biggest absolute losses, and therefore to identify the priorities on which to concentrate for a better performance.

Human analytical limitations lead to missed opportunities and threats

In performance analysis, there are many possible approaches:

• A structured end-to-end analysis, which follows the same pattern each period
• An analysis that starts with a familiar pattern but then diverges based on what the data shows
• An analysis without structure, where we focus on the most important % of changes
• An analysis centered on the hypotheses that we make based on our knowledge of the business