Increasing bottom line growth with B2B Price Optimization

B2B telco contracts can be much more bespoke than consumer packages. And the bigger the contract, the greater the scope for customized services and pricing. But designing contracts is an imprecise science, with arbitrary pricing dynamics which can leave sales and/or revenue on the table. Pricing too high? You lose the customer. Too low and you might win the deal, but margins suffer. A data-led approach can optimize the trade-off for each individual prospect and enable fact-based decision making in the sales process.

We devised a way to introduce transparency into pricing by calculating how a proposed price matrix compares to similar deals, correcting for all deal-specific parameters. We developed an AI pricing algorithm to dynamically benchmark deal parameters against historic pricing and profitability data to create an optimal reference price for each individual B2B deal. Parameters included product configuration, volumes, customer firmographics, usage behavior etc. This new data-driven way of working has allowed for increased automation in the deal approval process, with each deal automatically scored on a 5-point scale (A-E) for normalized profitability.

The new pricing solution has delivered three key benefits.

First, by establishing a single, accurate version of the truth, it has built transparency and objectivity into the internal dialogue around pricing, improving both the quality and efficiency of the process.

Second, overall profits have increased by approximately €25m per year, equaling a ~10% growth in bottom-line results for the addressable segment.

And third, it has allowed for increased automation in the internal deal approval process. Deals signaling on- or above-par margins can be approved automatically based on the algorithm. Only deals with sub-par profitability are sent for manual management approval, vastly increasing overall efficiency.