Conforama successfully transitions to a more streamlined and efficient cross-channel strategy by integrating an AI solution for personalized product recommendations

Conforama is the second largest home furnishings retailer in France and is present in seven countries, with 300 stores, including 200 in France. The company sells furniture and decorative items in kit form and posted sales of 1.7 billion euros in 2022.

As a gateway brand, Conforama’s goal is to “Make what people want most accessible at the best price.” It’s an ambition backed by a transformation plan to deliver an omnichannel experience through data and AI. An initial audit and data marketing vision with Artefact identified and prioritized 12 use cases and 25 technical and organizational enablers. The first use case was to integrate a personalized product recommendation into the company’s weekly emails.

Several challenges needed to be addressed through this use case:

  • How to understand the needs of three million customers and recommend the most relevant products from a catalog with 42,000 references?

  • How to propose only products currently in stock, on promotion, and not already suggested to customers?

  • How to easily operate and maintain the technical solution?

Saving consumers time, improving business productivity

By using machine learning algorithms to analyze user data, such as preferences, purchase history and online behavior, artificial intelligence-based product recommendation suggests products relevant to consumers in a personalized way. This allows companies to better understand their customers’ needs and recommend products that match their interests, resulting in increased sales and customer retention.

One of the main benefits of this solution is that it saves customers time. Rather than scrolling through countless product pages to find what they’re looking for, customers can quickly access a selection of recommended products that specifically meet their needs. AI-based product recommendation can enhance the online shopping experience and encourage customers to return for more purchases. A strategic advantage, given that 72% of consumers only interact with marketing messages that are personalized and tailored to their interests.

In addition, AI-based product recommendation can boost business productivity: machine learning algorithms can analyze large amounts of data in real time, allowing companies to continuously monitor customer trends and buying behavior. This can help organizations better understand customer desires and quickly adapt their product offerings accordingly. It can also enable companies to optimize their inventory by offering products that are more likely to sell, which can lower costs and maximize profits.

Lastly, AI-based product recommendation can offer significant business benefits. By suggesting relevant and personalized products to customers, companies can improve their conversion rate, increase sales and strengthen their brand image. From a market perspective, AI-based product recommendation has been shown to deliver +2.5% incremental growth.

A first use case focused on email campaign personalization

Prior to this project, all Conforama customers received emails featuring the same eight products selected each week by the marketing teams. This was a labor-intensive task, as it required identifying the eight products most likely to interest three million customers, each of whom had unique interests. All this time spent analyzing data could have been spent on more strategic activities, such as creating editorial content for those emails.

Today, an email is sent to every Conforama customer each Tuesday containing eight product recommendations. But these recommendations are personalized according to purchase history, and filtered exclusively for products that are on sale, are available in stores, and that haven’t been featured in previous activations.

The implemented AI solution includes 4 main data processing steps:

  • Collection of transaction histories, customer and product references, then data preparation;

  • Building the “Collaborative Filtering” model to calculate customer appetite for the product catalog;

  • Product filtering based on available inventory, commercial news (sales, promotions, etc.), past activations and purchases;

  • Product data enrichment (photos, prices, descriptions, etc.) for activation.

This solution is based on 16 data tables, 25 transformation and modeling steps, and 40 automated quality tests. Dozens of iterations of the model made it possible to choose the most efficient approach based on transaction history. Thanks to this solution, Conforama now generates several million recommendations each week in 45 minutes at a cost of 50 euros per week.

In other words, if you count development and operation costs, as well as incremental sales, the project break-even point is reached in one week, with an automated and reliable solution.

“Time savings, yes, but above all a business benefit for our CRM teams. Because thanks to this personalization, customers click more and therefore buy more. We’ve gained 15% of the click rate following the personalization of these emails, which represents several million in incremental sales.”
Mélodie Charles, Marketing Director Conforama

A smooth transition to AI: lessons from Conforama’s success story

For many players, there are three challenges linked to their level of maturity:

  • Level 1: Personalizing a currently rule-based touchpoint using an AI algorithmic approach;

  • Level 2: Extending AI-based personalized recommendation across the entire customer journey (similar products / complementary products / suggestion based on purchase history);

  • Level 3: Optimizing the orchestration of recommendations across channels to ensure an omnichannel experience.

Level 1 is often the most difficult, as it requires laying the foundations for four separate dimensions: target vision, user experience and priorities; data sources; technological tools; project team and work method.

The Conforama example offers valuable lessons about these four dimensions:

  • Select a first use case and functionalities that can be quickly implemented and measured to put the organization on the road to success. For example, this initial victory means Conforama can now plan the deployment of product recommendations in stores or the improvement of their algorithm thanks to browsing data.

  • Ensure the data is reliable. Good data modeling relies first and foremost on good quality data. For Conforama, exploratory analyses were performed on more than 50 tables to select data sources in areas such as customer knowledge, product repositories and transactions.

  • Use technologies that allow teams to deploy a technical solution quickly and collaboratively. Conforama selected the most appropriate tools for this type of workflow: DBT, BigQuery ML and Vertex AI for their performance, modularity and portability.

  • Build a dedicated team capable of dealing with all potential problems, and adopt a test and learn approach. To do this, a multidisciplinary IT / Conforama business team was formed, and a 2-week sprint approach was adopted.