In retail, especially in the fast fashion space, pricing is a key differentiator. A major clothing and accessories multinational wanted to maximise their overall margins through modernising and automating their retail pricing. They had three major goals for this solution:
The retailer was calculating prices and discounts manually, based on a series of variables such as the average sales of an item in the past weeks, stocks, business rules, etc. For a global retail giant, this system was not very scalable or efficient. It also led to inconsistencies in strategic approach amongst the different business units.
We worked closely with the company to understand their processes and requirements. Our team then worked with the client’s internal Data Science and Engineering team to develop an automated pricing engine that enabled their pricing department to predict and apply optimal pricing strategies for products during different sales periods. In addition to the simplification of processes and reduction of time spent on manual processes, this pricing optimisation model was scalable to multiple markets, and enabled local departments to be more autonomous.
As a result of implementing this retail pricing optimisation engine, the retailer experienced a significant positive impact on their overall margins during sales periods, across multiple markets. The manual and lengthy process to define pricing for each item on sale was also massively simplified and shortened. Individual errors in determining the pricing for thousands of items in different geographies and currencies were significantly reduced in number.
Most importantly, Management was convinced of the value of Artificial Intelligence and Data Science projects; leading to more initiatives within the company to optimise and improve other business processes.
