Technology
The product works. The business around it is held together by hand.
Technology businesses build sophisticated products and then run acquisition, onboarding, billing and support on a patchwork nobody owns. The leak is rarely in the code.
How the business actually runs
Before any software is discussed.
Engineering ships. Marketing drives sign-ups into a funnel instrumented by whoever set it up last. Onboarding is a welcome email and hope. Billing lives in a payment provider with the edge cases handled manually. Support answers the same questions repeatedly, and nobody connects that load to the parts of the product causing it.
- Software companies
- platforms
- SaaS and technical product teams
Where the money leaks
Four leaks we see in almost every one of these businesses.
Not generic inefficiency — the specific places where revenue, margin or time escapes a technology business.
- 01
Sign-ups that never activated
The gap between registering and doing the thing the product is for is where most churn is actually created — before anyone has decided to leave.
- 02
Usage invisible to the people who sell
If sales and success cannot see what an account does, expansion and churn both arrive as surprises.
- 03
Billing edge cases handled by humans
Upgrades, proration, failed payments and refunds done by hand do not scale, and each one is a chance to lose money quietly.
- 04
Support load nobody attributes
Ticket volume is a product signal. Treated as a cost centre instead, it funds a team to keep answering the same question.
The system
One connected flow, end to end.
Each stage feeds the next, and every stage is measurable. This is the shape of what we would build — the detail is decided with you.
- 01 Acquisition
- 02 Sign-up
- 03 Activation
- 04 Billing
- 05 Support
- 06 Expansion
- Marketing site and conversion instrumentation
- Onboarding and activation flows
- Product analytics joined to the customer record
- Billing and subscription automation
- Support tooling with product feedback loops
- Internal dashboards for the whole lifecycle
- Sign-up to activation rate
- Trial-to-paid conversion
- Net revenue retention
- Support contacts per active account
A worked example
Illustrative, with the assumptions stated.
Assume
- Say a platform takes 900 sign-ups a month
- A minority reach the activation milestone in week one
- Billing exceptions are handled manually by one person
What changes
- Activation is defined, instrumented and then designed for, rather than assumed
- Usage data sits on the customer record, so expansion and risk are visible to the people who act on them
- Billing handles its own edge cases, with exceptions surfaced rather than worked
- Support themes feed a product queue instead of a bigger support rota
Assumptions only. For a product business the first exercise is usually defining activation honestly — most teams have never agreed on it.
Is this you?
Worth a conversation if any of these are true.
And worth saying plainly when they are not.
- You cannot say what share of sign-ups activate
- Sales and success cannot see product usage
- Billing edge cases are manual
- Support answers the same question every week
Pre-product-market-fit, instrumentation is cheaper than infrastructure. Measure first; build the commercial machinery when there is something repeatable to scale.
Further reading and other industries
Tell us how the business runs today.
We will map where the revenue leaks, show you the system that closes it, and say plainly if the numbers do not justify building anything.
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