Revenue Is Growing, but Profit Isn’t: How to Manage B2B Deals

Denis Ermilov Deputy Director for Strategic Clients at Cinimex In B2B, an increasingly common and troubling picture is emerging: sales are growing, revenue reports look strong, but profit is stagnating or even declining. The problem is usually not “bad salespeople,” but the way deals are structured: discounts, special terms, non-standard commitments, inflated customer expectations, and rising delivery costs can quietly eat away at margins. When a company has the discipline to bring deal economics into a single framework and add data-driven recommendations, two areas of lost value become visible: some customers generate revenue but consume the company’s best resources with little profit, while others are underserved — meaning the company misses out on both sales and margin where healthy growth could have been achieved.

This is similar to assortment management in retail: a company may take pride in its turnover, but if that turnover is built on products with minimal margins and expensive logistics, the business is essentially working “for the sake of volume” rather than results. In B2B, the equivalent of logistics is the workload of delivery teams, implementation timelines, SLA commitments, penalty risks, and the cost of supporting exceptions. That is why it is important to look not only at sales volume, but also at the economic profile of each deal: how much the company actually earns after delivering on its promises.

The most common mistake is operating with two separate sets of metrics: sales are managed in a CRM system, while deal economics are tracked in scattered spreadsheets, often only after the fact. Discounts are approved “because we need to close the quarter,” implementation effort is estimated optimistically, SLA risks get lost in email threads, and approvals follow informal processes. As a result, companies may spend years missing the main source of margin leakage: profit is not lost through one major decision, but through hundreds of small concessions and exceptions — while the best specialists are tied up servicing high-maintenance customers and promising prospects fail to receive enough attention and gradually lose interest.

The right approach is to manage deal economics in the same way manufacturing companies manage costs: at the moment decisions are made, rather than after the reporting period has closed. This means establishing common rules and a unified calculation model covering price, discounts, terms, delivery costs, and risks; running “what-if” scenario analysis; introducing clear guardrails that protect margins; and creating transparent approval workflows. Discussions with customers then stop being a blind negotiation and become a manageable process: in some cases, it may be more profitable to restructure a deal; in others, to adjust the service level, revise timelines or scope of work; and sometimes, to simply walk away and preserve resources for more profitable opportunities.

This is where a “deal economics dashboard” becomes essential — a single environment showing prices and discounts, commitments and risks, planned and actual margins, deviations, causes of losses, and key management decisions. In our practice, this approach is implemented through a “deal economics profile” linked to the fulfillment of commitments and exception management. Once these elements are brought together, companies begin to see what was previously hidden: which deals generate revenue without profit, exactly where margins are leaking, and which changes to deal terms can actually drive profitable sales growth. In the new competitive environment, the winners will not be those who sell the most, but those who sell profitably and manage the process through an ecosystem for deal economics management.

In the next column, we will look at the second layer: how to build a real-time, data-driven recommendation layer on top of the “dashboard.” Which signals from sales, communications, SLA execution, and financial data are actually useful? How can companies generate recommendations for the next best action — identifying where there is a risk of losing a customer, where there is potential for expansion, which terms should be improved, and which deals need to be restructured? Most importantly, how can this be made not a matter of “magic,” but a controlled mechanism with transparent data sources, explainable recommendations, and clear accountability for decisions?