AI and product classification for pricing in the cosmetics sector

For a consistent and controlled price image 

This document gathers insights from our team of Big Data and Pricing experts experienced with European retail leaders. Our mission is to solve complex data problems that directly damage the profitability and price image of retailers, developing cutting-edge technological solutions based on the latest advances in AI and machine learning. This document gathers insights from our team of Big Data and Pricing experts experienced with European retail leaders. Our mission is to solve complex data problems that directly damage the profitability and price image of retailers, developing cutting-edge technological solutions based on the latest advances in AI and machine learning.

The aim of this article is to address a major issue which pricing and marketing managers come across in cosmetics and parapharmaceutics when segmenting their catalogs. How can we work at the pace of the market and maintain a segmentation - and therefore a pricing - that is consistent with the evolution of the catalog and trends? 

If a brand is focused on consumer perception, then products’ classification and segmentation according to common characteristics (type of product, capacity, etc.) or value attributes (brand perception, innovation, current trend, etc.) is an essential first step for the industrialization of effective pricing. Classifications are largely used by marketing teams. But when pricing teams need to use them they come across a few obstacles. 

Pricing complexities due to market volatility

Aside from the complexity generated from catalogues containing numerous products, the care and the beauty sector face an even more challenging obstacle because of the volatility of the market. This sector of activity is extremely dependent on: 

• trends in fashion & beauty magazines
• a high rate of incoming brands or e-commerce sites
• publications and opinions shared by specialized skin care and beauty influencers

Why are there problems with the coherence of the offer? 

Generally speaking, traditional tools (Excel, BI, etc.) require you to choose between exhaustiveness and speed of work. 

They do not allow you to work simultaneously on classifications for the entire catalog when it holds tens or hundreds of thousands of references. Teams must therefore cut up the product catalog. This is a waste of time and it damages the overall consistency of product pricing. Another solution would be to exclude certain product groups, which would make the strategy less precise. However, limiting the analytics to certain categories due to a lack of technological tools means running the risk of missing out on untapped margin potential or of being inconsistent in the eyes of the consumer in relation to the brand’s overall positioning. 

Given the size of the catalog and the classification data, users are not able to work at market pace. Working in real time implies having automatic classifications on the one hand, and on the other, being able to integrate and modify existing classifications, on-the-fly. For these adjustments to be relevant, they need to be integrated into the strategy right away. 

To strike the right balance between exhaustiveness and speed, the tools most used by retailers today focus on the three main criteria that define a product and are systematically available in the repository: the type of product, the brand and the size (or capacity in the case of health and beauty products). Integrating additional criteria specific to each product family and based on a repository that is not always complete is a real technological challenge in managing large quantities of data. However, from a strategic point of view, going beyond this would enable those in charge of pricing to classify products more accurately by integrating value attributes and not just product characteristics. This approach is very important in cosmetics where the consumer’s choice is not limited to “basic” criteria. For example: “shampoo, Garnier, 250 ml, dehydrated hair, general use” are already much more refined characteristics than “shampoo, Garnier, 250ml”.  We can also go further with value attributes for the customer by adding, for example, “novelty, self-cocooning, sulfate-free formula, natural ingredients, local production, Instagram popularity” to the previous classification.

To what extent is AI relevant for catalog classification for cosmetic retailers?

We keep hearing about AI but we don’t always know how to use it and get the full value out of it. AI will allow teams to automate existing product classifications. By relying on the existing product database, it is possible to train an AI to recognize several types of products and to associate attributes to them. The algorithm will be able to instantly classify a product such as “Lipstick, Dior, red, matte, grape, Christmas edition” from a single photo or product description submitted to it and this product will be directly integrated into a “high sensitivity” pricing strategy during the Christmas period. 

How do you increase performance on classifications and pricing?

In conclusion

AI and business experts form a team, they feed each other. AI enables business experts to make swift classifications. Business experts, in turn, send new information to the AI to adapt to a given context which could include incoming products, a competitive and fluctuating environment or new trends. 

AI eliminates the time-consuming management of classifications over time and teams can focus on optimizing pricing strategies based on different classifications, performance indicators and the retailer’s business strategy (e.g., optimizing private labels, favoring the web, targeting new customers, etc.). A pricing tool that is powerful and flexible enough to integrate these classifications in real time allows us to test the efficiency of a classification, compare several classifications, and change the strategy very quickly if necessary.