Manufacturer case study
Optimizing sales price and quotation process in real time.
Optimizing sales price and quotation process in real time.
A global top 3 steel producer and manufacturer of steel products submits hundreds of complex bid / quotations per month, where they bid on large projects and each bid can contain hundreds of line items. For each item, the quotation will include a price point somewhere between the minimum acceptable price and the ‘ideal’ price. Determining that price point is a manual and time-consuming process, which tries to take into account a variety of data points (client history, economic variables, and so on). The client wants to be able to adapt and optimise sales prices in real time for each customer / quotation at the item level and at the same time apply A.I. driven intelligence to make smarter price recommendations with visibility into 3 levels:
The recommended Target Price needs to be able to be adjusted or evolve, based on shifts in the business strategy.
Predictive Layer gathers the following data points:
With this data, Predictive Layer’s Genius Seller forms an understanding of the business logic and provides the Target price recommendations.
Once trained, the solution was ready to be tested by the client in parallel with benchmarking against sales representatives that were not following price recommendations from Genius Seller (in other words: validate the KPI from sales representatives that were using the price recommendations from Genius seller vs sales representatives that were not using the price recommendations). Having successfully passed these tests, the Predictive Layer solution was deployed into production across several countries in Europe (France, Germany, Spain, Luxemburg, Belgium, Netherlands, UK, Italy, Poland). It currently manages more than 80 000 quotations per day with a gain of productivity close to 30%.
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