For a consistent and controlled price image
IA et pricing : quels exemples d’application ? 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 that pricing and marketing managers come across in the DIY and home improvement retail 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 classifying and segmenting products 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 widely used by marketing teams. But when pricing teams need to use them they come across a few obstacles.
Saisonnalité, marketing d’influence et innovation des pure players : comment comprendre les nouvelles règles du jeu ?
Aside from the complexity generated from catalogues containing numerous products, the specific challenges of the DIY sector are seasonality and reactivity in the evolution of trends. This sector of activity is indeed influenced by several factors:
- First of all, the macroeconomic context. This has a particular impact on purchasing trends in a context of health restrictions. We saw this on the French market in spring 2020: paints and outdoor swimming pools were out of stock. The lockdown has given the French the desire to take charge of small jobs in their homes. Long-term trend or temporary episode? In any case, price professionals must be able to react accordingly when it comes to supply and price competitiveness.
- The influence of decoration blogs and magazines (Vogue presents for example in the following article a selection of “The best Instagram accounts to follow for inspiring interior design”) as well as from decoration & home TV shows.
- Publications and opinions shared by influencers on social networks.. The success of do-it-yourself or home improvement influencers has been driven by the covid crisis during which consumers have been consuming more to improve their home comfort and learn manual skills. In addition, many bloggers who specialize in other fields (such as beauty, fashion, sports, cooking...) indirectly influence trends through the care they put in the decor of their photo shoots. XXL mirrors, rattan lights or "olive green" kitchens are three examples of trends that have exploded on social networks in the last few months, all fields combined.
- E-commerce, with very aggressive competition (Amazon, Mano Mano...), but always more innovative. For example, the Mano Mano website, which primarily targeted individuals, has extended its offer to professionals in 2020 and tested new services linking professional handymen and individuals.
This unpredictable environment is causing the marketing and pricing teams in the DIY and home improvement industry to constantly redefine established classifications. However, retailers in this sector indicate that these classifications are still done manually and often “by using common sense”. The consequence? Outdated and potentially unreliable classifications that could harm product price positioning.
Pourquoi ces problématiques sur la cohérence de l’offre ?
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 completeness 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: product type, brand, and size (or capacity in the case of paint products, for example). Integrating additional criteria, specific to each product family and from a repository that is not always complete, presents a technological challenge in managing massive amounts of data. From a strategic point of view, however, overcoming these difficulties would enable those in charge of pricing to classify products more accurately, by integrating value attributes into the repositories and not just product characteristics. This approach is indeed very important in the field of DIY and home improvement where, for some products in particular, consumers do not limit their choice to "basic" criteria. For example, "sofa, Maisons du Monde, 2 seats, convertible, blue, velvet, Scandinavian style" are already much more refined characteristics than "sofa, Maisons du Monde, 2 seats". We can also go further with value attributes for the customer by adding, for example, "new, made in France, instagram popularity, cocooning" to the previous classification. This example concerns the same product, but the pricer evolving in the decoration and do-it-yourself sector is also confronted with the diversity of buyer profiles that depend on the targeted products. The pricer must be able to segment according to pragmatic criteria (for the customer who wants to buy a drill for example), or according to value criteria (for a sofa, one customer will choose according to trend, another according to comfort).
IA et pricing : quelle pertinence dans la classification du catalogue, pour les enseignes de décoration et bricolage en particulier ?
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 "Tableware, appetizer, red, Christmas edition" from a simple photo or product description submitted to it and this product will be directly concerned by a "high sensitivity" price strategy during the Christmas period.
However, as we have seen before, there is a very large amount of unpredictability in the decoration and do-it-yourself sector due to trend changes driven by magazines and social networks. Pricing, product and category managers need tools that allow them to work more efficiently, but above all to react very quickly. Mercio is committed to providing retailers with this agility. For example, the choice of color for the new kitchen is a key element for the buyer. So if olive green suddenly becomes the trend of the moment, the pricer will be able to instantly update the "color of the moment" classification, and maintain consistent pricing.
A classification tool must therefore provide a clever mix between automation thanks to AI and a manual “corrective” part. This means allowing the person in charge to keep control of the AI by rectifying or sending new information related to an evolution on the market, whether this be an incoming product, a new trend etc.
Comment procéder pour monter en performance sur les classifications et le pricing ?
Data science is a great starting point to creating value insights for a brand that does not have an existing classification. If the retailer already has classifications, data science will help gain accuracy by verifying the validity of the classifications in place and by enriching them.
Receipts constitute a massive volume of data for retailers, and this data is continually being renewed. This is not an obstacle for AI but rather provides it with a great opportunity to learn. By analyzing sales receipts, a machine learning algorithm can identify purchase behaviours and links between products that are very difficult to perceive “with the naked eye”. For example, cases where an increase in the price of an electric sander would lead to an increase in manual sanding equipment of the same brand. Once the classification is identified and automation is in place, teams can "feed" the AI algorithm with new information. This article shows a first example of classification. All the data available to the retailer will allow him to gain finesse: store receipts crossed with web shopping carts, web visits, and insights into loyalty programs, for example. Thus, new classifications can be identified: "routine" product, impulse product, etc.
In conclusion
AI and business experts form a team, they feed each other. The AI enables business experts to make swift classifications. The business experts, in turn, send new information to the AI so that it can 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.
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