Sales x Product x Marketing

Running a matrix organization comes with some complexity. There can be blurred lines of accountability between sales, product, and marketing, and this can lead to conflict or a disjointed customer experience.

Through scaling Revolut as a matrix organization, we have developed learnings on how to optimize collaboration between functions and minimize conflict. We believe that the four enablers below help you run a B2B sales organization smoothly in conjunction with product and marketing.

Shared Single Source of Truth

As in other parts of the organization, we believe an objective single source of truth creates alignment and reduces conflict. It’s central to how our performance management system works, and the same principle applies to how sales, product, and marketing work together.

In practice, this requires a detailed customer data platform (CDP) combined with multi-touch attribution. Retail growth tends to follow a linear process. B2B buying journeys don’t, and they can involve many touchpoints over time: a call with sales, a website visit, a Grand Prix event, marketing material, a follow-up conversation, and an introduction to an integration engineer. To understand which parts of the process are working, each of these needs to sit on one timeline and be measured against the eventual conversion.

We’d recommend setting it up as follows.

• Build a unified customer data platform of all your target customers enriched with data as they go through the full go-to-market funnel. This includes:
  • A database of all businesses in your universe
  • Each interaction with the customer from sales, product, or marketing (e.g., meeting, website click, email, marketing campaign, business event, etc.)
  • Timeline of interactions
  • Inferred or heuristic facts (e.g., estimated revenue or employee count) and a hypothesis on which products might suit the customer
• Run multi-touch attribution
  • Assess ROI or yield per pound spent across the full go-to-market funnel for sales, product, and marketing
  • This shows the contribution of each interaction and each function to the eventual conversion of a customer
  • Multi-touch attribution to identify contribution of different interactions across a buying journey

Data-Driven Product Feedback Loop

Feedback from customers needs to be systematic and based on data. We found this to work effectively with a disciplined approach requiring AEs to log all customer feedback into the CRM. The central Sales Operations team distills this information and shares it with the Product team.

This ensures that product and sales can use objective data to come to agreement on decisions.

Automated Next-Best-Action (NBA)

A next-best-action (NBA) engine uses AI to decide what should happen next with each target customer. It looks across product, marketing and sales together.

This matters most in long, complex sales processes that involve several functions. When people decide the next step by hand, teams disagree over who owns the customer and actions often happen in the wrong order, and the customer experience suffers. The engine reduces both problems.

It works by looking at every possible touchpoint and ranking each action by expected return on investment, so actions happen in the right order. Those actions can include a salesperson reaching out, an AI SDR, a lifecycle marketing campaign or an invitation to an event.

Dual Attribution Incentives

We adopted the dual attribution incentives between product sales experts (PSEs) and generalist AEs. Generalist AEs keep commercial ownership of the account and coordinate the client relationship, while PSEs own the technical deal design, custom pricing models and risk scoping. PSEs and AEs don’t split the commission. Each earns 100% of their individual commission on the deal.

This “double commission” creates better alignment between Product and Sales. The extra cost is built into our ROI calculations, and we’ve seen that it ultimately improves ROI because it helps unlock complex, high-value accounts.