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Customer Story

fonQ embraces data-driven pricing 

The home decor store collaborates with Xebia to develop an automated, data-driven pricing system. The new system significantly increases margins by 25% and allows for more frequent and better price adjustments.
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GCP, Airflow, Github, Data Technology

Industry
Retail

Company
Xebia Data

fonQ is a popular webshop in and around the Netherlands for the home and living space, known for its wide range of furniture and lifestyle products. The company offers a variety of items from well-known suppliers and places a strong emphasis on its own private-brand products. These in-house brands are set to play a key role in fonQ's future, and as such, there is a pressing need to refine the pricing strategy for their products to stay competitive and maximize profitability. 
Why
Private-brand products hold a strategic advantage over competitor-driven products. However, the process of pricing the former was still manual and did not leverage historical data, leading to suboptimal prices.
What
The team automated price recommendations for private-brand products. The new system maximizes absolute margin and reduces manual steps in the pricing process.
How
Xebia consultants partnered with fonQ staff to develop an ML model and deliver actionable price recommendations and insights through a user-friendly web application.

The Journey Towards Data-Driven Pricing 

Before this project, fonQ's pricing of private-brand products involved a manual, rule-based process. While this method provided a good foundation, it was time-consuming and could not utilize patterns in historical sales data, resulting in infrequent and less-than-ideal price adjustments. 

In collaboration, fonQ and Xebia streamlined the transition to automated price recommendations through a structured approach and continuous feedback loops. To make this happen, we assembled a team with all the needed experts – data analysts, scientists, pricing professionals, and reporting specialists – who would manage the entire process from end to end. Due to this setup, we were able to go from a proof of concept to a fully integrated minimum viable product in no more than three months, from local to cloud environments and manual to continuous deployment practices.   
Teaming up with Xebia, we created a smart pricing tool that's simple yet powerful. The Xebia team blended with ours seamlessly, sharing their technical knowledge in the process. Working with them was straightforward and effective; they helped us make sense of the data and significantly boost our margins.
Fonq-logo-new
Sisi You Manager Data & Analytics and Finance

Initial resistance arose over the model's suggestion to lower prices, as it seemed contradictory to increasing profit margins. However, multiple live tests demonstrated that the model's strategic price reductions led to a significant margin boost. This effectively discarded the doubts and underscored the value of data-led pricing decisions.

Fig 1: Absolute margin before and after going live with the pricing model. Slight differences before going live were caused by random variations and small tests we executed to validate the model. A ~25% improvement was observed for the selection of products for which we went live. 

Margins from included products rose by an estimated 25%

The Final Outcome

The machine learning pricing system has significantly enhanced fonQ's pricing agility, enabling weekly adjustments that align closely with market conditions. This advancement has increased fonQ's monthly absolute margins from included products by an estimated 25% and has allowed the pricing team to make informed, data-driven decisions. The price recommendations were generally lower than previously adopted, so customers now benefit from more competitive prices, improving their shopping experience. This successful implementation exemplifies the synergy between fonQ and Xebia, setting a new standard for innovation in retail pricing strategies. 

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