Dynamic pricing has emerged as a game-changer in the retail sector, marking a significant shift from traditional pricing strategies. This innovative approach leverages real-time data analysis to adjust prices on the fly, taking into account factors such as demand fluctuations, competitor pricing, and inventory levels.
As consumers increasingly turn to digital platforms for their shopping needs, the ability to dynamically set prices has become not just advantageous but essential for retailers aiming to stay competitive in a fast-paced market.
A retailer in the UK asked help from Tesseract Advisory & Consulting to implement a dynamic pricing solution. In this case study we review how we solved this problem.
Background
The Tesseract team started off with an AI roadmap. The roadmap is an extremely useful exercise that enables us to understand some key points such as:
- What data assets does the client posses.
- The quality of the data.
- The different ways forward.
- Associated benefits, risks and costs for each path.
After the roadmap finished, we moved on to the next part of the project which was the implementation.
The Tesseract team worked over a period of 6 months to build and train a model, based on existing data, which it was then put into production.
The model is currently making automated pricing decisions for the client, leading to improved margins, and reduced inventory costs.
AI in the Retail Sector
The transformative journey of implementing dynamic pricing in the retail industry showcases the pivotal role of AI and data science in modern business strategies. At Tesseract Advisory & Consulting, our case study with a UK retailer highlights not only the technical proficiency required to design and deploy such solutions but also the strategic foresight to navigate the complexities of retail markets.
If you want to know more what we can do for your business, then make sure to get in touch. We specialise in delivering solutions in AI and data science, but also educating decision makers on topics such as data strategy and data maturity.
