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Pricing Optimization for FMCG Retailer

Revolutionize pricing for increased revenue and sales.


The FMCG retailer needed AI consulting services to implement a pricing optimization mechanism that could scale for more than 100K items based on the seller's user input for the product catalog. The goal was to ensure that differential pricing remained within set thresholds and to increase customer buying on the platform.


Developing an ML-driven pricing optimization mechanism posed several challenges. The solution needed to incorporate user feedback, RFM analysis, and historical pricing data to accurately predict prices.

The algorithm considered various factors about the item. These factors included the category, quality, description, usage, availability, shipping cost, and type. The algorithm used these factors to determine the appropriate price.


To address these challenges, our Accelerance partner served as their AI consultant developed a price prediction model that leveraged user feedback, RFM analysis, and historical pricing data. The algorithm used advanced monitoring techniques to analyze item characteristics and predict the most suitable price.

To ensure the accuracy and optimization of the price, they took into account various factors. These factors included the type, quality, description, usage, availability, shipping cost, and category of the item.


Better pricing strategy led to more revenue and increased sales for the business. This indicates that the new pricing approach effectively attracted more customers and encouraged them to make purchases.

Customer loyalty and satisfaction increased, leading to a significant 40% rise in customer retention for the FMCG retailer. This suggests that their efforts to deliver an exceptional customer experience have been successful.

The pricing optimization system improved fair pricing. It resulted in a 20% better control over fair prices. This ensured that customers paid reasonable prices for their purchases.

By implementing AI, the FMCG retailer used machine learning (ML) to optimize pricing. This helped them improve revenue, retain more customers, and gain better control over pricing. Ultimately, it had a positive impact on their business in the long term.