Achieve precise and rapid energy price predictions, eliminate proprietary ML platforms, realize a 2x boost in data science productivity, and save 100% on subscriptions with minimal investment
The client, a top-3 global leader in animal health products, faced stiff challenges from their competitors who were equipped with more advanced pricing strategies to adjust prices based on changes in demand, costs, and competition. The client’s fixed pricing strategies led to inefficient resource allocation, because of which they often needed help to respond effectively to changes in demand or production costs, resulting in overproduction or stock shortages. The inability to adjust prices based on demand or market conditions led the client to miss revenue opportunities, lose market share to competitors, and impact the brand value.
DynamicPricingAi looked at six input data sources—product, demand, wholesale, inventory, promotion, and competitor—to build a dynamic pricing model. The solution was able to ingest data from multiple internal and external data sources and prepare it for Minimum Advertised Price (MAP) determination. MAP could be adjusted by market positions, retailers’ overall acceptance, customers' acceptance, and inventory gap premium. Not just that—to overcome limited data, the solution simulated promotions with reinforcement learning framework to continuously collect data and improve pricing.