Category Management: how to identify the biggest business opportunities

Category: Category Management |Solution: DataMa Compare, Analytical Solution| Type : Recurring | Instries: all ie FMCG, Beauty, Toys | Extension: None

Tags: Tags: #CategoryManagement #RetailPerformance #RetailerRelation #BusinessOpportunities#waterfall#Quantification

Data in FMCG businesses has always been especially perishable; its value quickly diminishes with time. DataMa tools can help category managers transform data into actions in a faster and more precise way to impact category results and to get a competitive advantage

Brian Harris

Creator of Category Management Concept

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)

For example, in the case below, a loss of 5 points of WD on product A results in a loss of 5500 units while a 9 point drop in WD causes product B to lose 4800 units, a difference of 15%. of units.




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.

Of course, the knowledge that the cat man has of the company (process, possibilities of action, budget, etc.), of the market and of the retailers allows him to determine whether the major sources of loss identified are actionable…

Even if they are not actionable, it is nevertheless key to have identified them in order to better understand its performance and work on future actions to be taken.

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

These 4 approaches have strengths and weaknesses. Some are more powerful than others, but none is perfect because the human being’s ability to analyze and synthesize information is limited.

Indeed, it is extremely complex and time-consuming to analyze each brand, each range, each sub-range, each format, each product, etc. on each KPI, and to compare them to the competition.

And then to make a synthesis with priority axes.


Therefore, category managers not being able to analyze and synthesize everything due to lack of time, capacity or desire, this inevitably leads to missing opportunities and threats.

DataMa provides a turnkey solution to this complexity

  • Quantification of the impacts of each business driver on each KPI
  • Quick and impactful visualization of all these impacts
  • Highlighting the most interesting and therefore priority impacts from a data point of view (the small green bar next to each business driver)

Here is an example of analysis which allows, in a few seconds, to understand that the drop in sales value is linked to a strong loss of Distribution Points at Retailer D, which must therefore be the focus of the analysis.



DataMa therefore allows you to immediately identify the biggest business opportunities, so that you can focus on solutions and recommendations.


Accurate and rapid diagnosis for greater added value

The added value of category managers should not lie in their ability to perform precise data analysis but in their contribution to the business.

The problem is that they spend so much time on these data analyzes that they have too little time to focus on their real added value:

  • The story (the storytelling) they tell around the data, to highlight the key points (we know how much a bad story can drown out a superb analysis)
  • Recommendations for actions to be taken and category strategy
  • Internal relations but especially with retailers

The story and recommendations, unlike the analysis of data, are by nature subject to subjectivity because they call on creativity, business expertise and knowledge of consumers, the company and customers.

These are elements that must therefore be discussed internally to be optimized and to have the greatest impact on turnover.



Category and performance analysis is key but also particularly complex.

So complex and time-consuming that it does not leave teams enough time to bring strong added value around an impactful story, powerful recommendations and the development of a deep customer relationship.

However, this customer relationship is the basis of any good business because all studies show that human beings make emotional decisions that they post-rationalise.

We will discuss in a future article other critical aspects of the relationship with retailers that DataMa helps to strengthen: trust and transparency in the diagnosis, objective approach and responsiveness.

So if you also want to drastically reduce your team’s analysis time and have an even stronger impact on business, don’t hesitate to contact me!

You can test DataMa for free on your data to validate its power!