Modular Sales Motions
Build modular sales motions on top of the core engine, designed around the nuances of a specific customer segment
Define every motion on five dimensions: strategic objective, trigger and target, role of the AE, execution discipline and enablement dependency
Tailor the motion to the segment: high velocity for SMB, full-cycle ownership for Mid-Market, executive pods for Enterprise and trigger-based plays for expansion
Introduce Solutions Engineers as deals grow complex, with the AE owning the commercial relationship and the SE owning technical scoping, integrations and security
Don't scale AE headcount until unit economics are stable, which means proving product-market fit on three hard metrics: department ROI, predictable conversion and fewer feature requests driving deal losses
Treat activation as the finish line: track time to first transaction, support integrations and log roadblocks for product teams
You do not need to re-invent the wheel when you launch into new segments or geographies. You can take the same core revenue operations engine and build modular motions on top. The same philosophy, processes and discipline apply and you can leverage the central data to build new tech enablement. This modularity is critical to how we scaled efficiently and effectively.
Sales Motions
Five design choices shape each sales motion
Strategic objective
The commercial outcome the motion must produce
Trigger & target
What starts the motion and who it points at
Role of the AE
How much of the funnel the AE owns
Execution discipline
The few non-negotiable rules that make it repeatable
Enablement dependency
The tech that powers it, e.g. routing and alerts
Let’s Walk Through a Practical Example
You are currently doing SMB sales which is high velocity and highly automated. You are thinking of moving into the mid-market segment. How do you take the core revenue operations and build a mid-market sales optimization?
You need to build a new playbook of sales enablement material, incentives, tools, and integrate the mid-market into the AI sourcing engine. You will also need to figure out the product and marketing interactions required to convert customers. All this can be developed in conjunction with the sales operations team and rolled out globally once it has been tested.
Some examples below based on what we did at Revolut:
Technical Sales
AEs and Solutions Engineers co-sell larger deals
Account Executive
Commercial owner
- Account strategy
- Executive relationships
- Contract
Solutions Engineer
Technical owner
- Technical scoping
- Integrations
- Security reviews
AEs cannot change technical architecture or promise custom work without SE sign-off
Add SEs when:
- Custom integration is required
- Deals stall at technical validation
- Complex, high-margin products launch
As deals expand, relying solely on generalist AEs to handle technical discovery introduces significant risk. You can address this with a formal co-selling model:
- A commercial Account Executive owns account strategy, executive relationships and the contract
- A Solutions Engineer owns technical scoping, integrations and security reviews (and AEs cannot modify technical architecture or promise custom engineering without formal sign-off)
Consider adding technical specialists when:
- A product requires custom integration
- Deal tracking shows generalist pipelines stalling specifically at technical validation stages
- Introducing complex, high-margin products that demand specialist expertise
The Co-Selling Rule
Generalist AEs retain commercial account ownership and coordinate the client relationship, while PSEs own the technical deal design, custom pricing models, and risk scoping. PSEs and AEs do not split the commission but rather they both earn 100% of their individual commission for the deal.
This "double commission" creates better alignment between product and sales. The double cost is reflected in ROI calculations and we have seen that ultimately it drives ROI as it helps unlock complex high value accounts.
Prerequisites to Scaling
Two checks come before scaling AE headcount
Verify product-market fit
Three hard metrics
Sustained ROI
12-month GP ≥ one loaded rep cost, sustained
Predictable conversion
Discovery-to-activation stable at 10%+
Fewer feature gaps
Fewer deals lost to product gaps
Manage adoption & churn
A deal is done at first transaction, not signature
Time to first transaction
Days from signature to first transaction
Integration support
Enterprise gets an SE for the first 30 days
Roadblock log
Integration issues logged for product
Do not scale AE headcount until core unit economics are stable. This requires working through product-market fit and managing adoption barriers and onboarding churn. Practically this is achieved with feedback loops for each segment.
1. Verifying Product-Market Fit (The 3 Hard Metrics)
- Sustained Department ROI: The sales team generates 12 months predicted gross profit of at least 1 fully loaded rep cost over consecutive quarters
- Predictable Lead-to-Sale Conversion: Conversion from qualified discovery opportunity to activated account stabilizes in double-digit percentages
- Stabilization of Feature Requests: Deal losses driven by critical product deficiencies decline, confirming the core product reliably meets customer needs
2. Managing Adoption Barriers & Onboarding Churn
A deal is not completed when the contract is signed. It is completed when the customer activates and generates recurring volume.
- Track Time-to-First-Transaction: Track the exact days from contract signature to first live transaction. Every additional week of delay increases account churn risk
- Integration Assistance: For enterprise deals, pair the customer with a Solutions Engineer during the first 30 days of implementation to ensure integrations launch smoothly
- Document Common Roadblocks: Maintain a shared log capturing integration challenges, routing feedback directly to product teams to prioritize platform fixes