Margin Recovery at a Food Ingredient Manufacturer has been saved
Margin Recovery at a Food Ingredient Manufacturer
Revenue Uplift with PriceCypher & Value Recovery Campaigns
By using our powerful AI-driven PriceCypher Predictive Pricing tool, our client was able to regain commercial control and manage their customer portfolio. By implementing data-driven decision making within the company, a margin leakage of €12 million was discovered of which €1 million was recovered within 3 weeks.
A multi-national food ingredient manufacturer was struggling from margin leakage due to lack of commercial control and excessive discounting.
A business with a large portfolio of diverse customers, the ingredient manufacturer finding it difficult to gain commercial control. Issues such as excessive discounting and price variance, neglect of legacy deals and overlooked small customers were abundant. Lack of a solid foundation for strategic pricing and discount guidance was making it difficult to initiative a value recovery in the short term.
An interventionist approach, driven by AI-based pricing recommendation as well as a campaign- based value recovery approach was used to gain quick wins and clean up the pricing estate.
Powered by PriceCypher, AI-based algorithms provided pricing insights to sales to correct low-price outliers. These insights and guidance were given at a deal level, for each and every customer -product combination. Besides building an enabling tool which provided sales team with insights on pockets of recovery, a Price Recovery Campaign was also initiated. Hereby, poor performing sales accounts were reviewed and addressed in their respective client portfolio as they were low-price outliers and earmarked for recovery.
Client recovery €1 million within a period of 3 weeks.
AI-based pricing technique of Predictive Pricing was used to identify a price leakage of €12 million on account of low price outliers. Using differentiated customer tactics for value recovery, a sum of over €1 million was recovered within a period of 3 weeks.
Real Time Dynamic Optimization through Predictive Modelling
Improving Conversion on Commodity Business