Revenue engine
A clear market story, one chosen go-to-market motion, a repeatable sales process and a pipeline you can forecast from.
Practical frameworks and worksheets for founders and leaders building the commercial engine to scale a technology business. Browse by the part of the business you're responsible for, the problem you're trying to solve, or the stage your company is at.
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Not sure where to begin? Each path is a short sequence of resources that build on each other, in the order they are most useful.
A clear market story, one chosen go-to-market motion, a repeatable sales process and a pipeline you can forecast from.
A scoped onboarding, a repeatable implementation plan, an account health model and account plans that surface expansion early.
A review cadence the team actually runs, one decider per recurring decision, reporting built around decisions and an early warning on strategic drift.
A readiness baseline, a ranked shortlist of AI ideas, role-based enablement, documented workflows and governance policies in place.
One primary partnership motion, qualified partners, a 90-day onboarding plan, a working referral motion and a tiered review rhythm.
46 resources
If you built a product but have no revenue motion, here's the operating logic behind SaaS growth, reduced to five levers you can pull.
Choosing a first go-to-market motion is a decision with real inputs, not a matter of taste, and here's the logic behind picking the right one.
Turning a project into a business means deciding what gets reviewed weekly, monthly, and quarterly: a simple cadence that fits a small team.
A step-by-step way to define who the product is for, what it solves, and why now, so the market can finally understand what you built.
A repeatable playbook for founder-led content, partner co-marketing, and network email sequences that generate real pipeline on no budget.
A working sales process needs four decisions made in advance, not improvised call by call: what you ask, who qualifies, what the stages are, and what counts as progress.
A pipeline you cannot trust is not a forecasting problem, it is a data hygiene problem, and it is fixable with three habits: clean stages, tracked reasons, and aging rules.
A practical way to segment accounts by value and risk, run lightweight account plans, and set a review cadence you'll actually keep.
A repeatable implementation journey with clear phases, roles, and artefacts, built to work even if you're the only person running it.
How to set scope, timelines, and mutual responsibilities up front so customers know exactly what onboarding includes before it starts.
A simple health scoring method, a minimum touchpoint schedule, and a lightweight QBR substitute you can run without any dedicated CS headcount.
A practical structure for response times, escalation levels, and documentation so a small team can scale support without over-promising or losing track of answers.
A plain-language guide to the six numbers that matter: recurring revenue, gross churn, net churn, CAC, CAC payback and runway, plus where LTV fits.
How to choose tiers, pick a value metric, and set a discount policy so pricing decisions stop happening ad hoc, deal by deal.
A practical baseline for CRM, support, documentation, analytics, and security so the business runs on systems, not memory.
A scoring framework for turning scattered feature requests into a prioritised roadmap tied to business goals, not the loudest voice.
A structured way to route feedback from sales and customer success into product decisions, so nothing useful gets lost in someone's Slack DMs.
Before you hire your next five people, decide how the business is actually structured, not just who reports to whom.
A job description tells someone what they'll be doing; a role scorecard tells them what winning actually looks like.
Consistency in hiring doesn't require bureaucracy. It requires a repeatable process anyone on the team can follow.
Most onboarding plans describe a new hire's first week; almost none define what accountability looks like by day 90.
Good management isn't a personality trait. It's a cadence of 1:1s, team meetings, and feedback loops run consistently enough to become reliable.
Simple, consistent accountability beats an elaborate performance system that nobody has the time or patience to run properly.
You don't need an enterprise security program to be credible. You need a defensible baseline across trust, access, devices, and documentation.
The deals that stall in procurement usually aren't lost on price. They're lost on how long it takes to answer a security review.
Unmanaged permissions and slow offboarding are the most common security failure in small technology companies, and the easiest to fix.
Most technology companies have no shared answer for what counts as sensitive data, where it lives, or when it should be deleted, and that gap is the real risk.
Every tool you connect to customer data or company systems inherits a piece of your risk, whether or not anyone reviewed it before signing up.
Small technology teams don't need an elaborate incident response program. They need a clear, calm first-response sequence they can run under pressure.
Most technology companies don't have an AI strategy problem. They have an AI sequencing problem, and this framework fixes the order.
There's a real difference between individuals using AI tools and an organisation that's genuinely ready to adopt AI at scale. Most companies confuse the two.
Buying tools and hoping people figure it out is not an enablement plan, and it's the single most common reason AI adoption stalls after an enthusiastic start.
Every technology company has a growing list of 'we could use AI for this' ideas: the constraint isn't imagination, it's prioritisation.
The gap between a person using AI well and an organisation using AI well is process, and process is exactly what most companies skip.
Adding an AI feature is easy; deciding whether AI belongs in your product, packaging, or pricing at all is the harder and more important question.
AI is already making decisions and producing content inside your company. The question is whether anyone owns what happens when it goes wrong.
Most leadership teams don't fail from lack of talent. They fail because growth, product, customer, and operations are quietly running four different businesses under one logo.
A board meeting that produces no decisions is a governance cost with no governance value. The fix is cadence and structure, not more slides.
Most leadership friction isn't disagreement about the right answer. It's unresolved ambiguity about who had the authority to answer at all.
More dashboards and more slides have made most management reporting louder, not more useful. The fix is deciding what a leadership team actually needs to act on.
Strategic drift rarely announces itself: it accumulates quietly across a leadership team until priorities that once matched have quietly diverged.
Most technology companies collect partners before they decide what partnerships are actually for. Here's how to reverse that order.
Enthusiasm and a logo on a slide are not qualification. Here's what actually predicts whether a partner will produce results.
The agreement is rarely the reason a partnership fails to produce anything; the first ninety days after signing usually are.
Most partner-sourced growth motions fail from complexity, not from lack of partner interest. Here's how to design one that survives contact with reality.
Partnerships don't fail at signing or onboarding nearly as often as they fail from being left unmanaged for a year. Here's the cadence that prevents that.
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