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Demand and price projection
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Retail
Price optimizer and simulator
Pricing suggestion system created to maximize margins and demand for one of the largest retailers in the country.
Challenge
A leading retail company in Colombia, with a portfolio of products generating over 4 billion dollars in revenue, sought to maintain its market share in a highly competitive industry with players constantly expanding. The company aimed to improve its competitiveness through the automation of its pricing strategy, generating suggestions without losing sight of the business perspective. Thus, they could carry out a constant monitoring of their portfolio in a faster, more efficient, and accurate manner, defending their position in the market.
Solution
At Sumz, we have developed a price optimizer based on rules defined by the client, using models of artificial intelligence and advanced analytics. With this system, the client can visualize how demand varies according to the suggested prices from the artificial intelligence model. This allows the client to make data-driven decisions that contribute to maximizing profit margins and sales, while also maintaining their position in the market.
To process and analyze more than 10 billion data points, we use Databricks, along with advanced analytics, optimization, and machine learning models. This robust technological ecosystem allows us to manage large volumes of data in an efficient manner and apply advanced techniques in the process.