Five measurable experiments
Growth Marketing for Startups: Your First Five Experiments
Growth marketing for startups after launch: how to write a testable hypothesis, prioritise with ICE, and run your first five measurable experiments.
Blog · page 4 of 5
Write-ups on validating an AI startup idea before building it: what the evidence says, what founders get wrong at go-to-market, and what we learn running the playbooks with them.
Five measurable experiments
Growth marketing for startups after launch: how to write a testable hypothesis, prioritise with ICE, and run your first five measurable experiments.
One channel, then more
A startup marketing strategy that starts with one channel: list the options, test a few cheaply in parallel, and commit to the one that works before scaling.
Free tools, honest results
A free online survey platform workflow for a student startup idea: interview first, write a short survey, recruit the right people and read results honestly.
Surveys, with limits
Online survey tools for startup validation: what surveys can and cannot tell you, why panel samples mislead, and how to use a survey alongside interviews.
Five kinds, one checklist
Competitive intelligence tools for startups: the five kinds of tool, what each is for, and a checklist to evaluate them before you add another subscription.
Choose the niche with data
Market intelligence for AI startups: what to research before choosing a niche, which free public data sources to use, and how to compare niches side by side.
Competitors, on the record
Competitive intelligence for founders: the public evidence to collect on competitors, how to keep it current, and how to turn it into pricing and positioning.
Start from what you know
How to find a business idea by starting from problems you already know: a five-step method to list, filter and test them, instead of brainstorming from nothing.
Interview, then pilot
How to validate a product idea as a domain expert: interview the buyers you already know, then run a paid pilot with a success metric agreed up front.
Eight things to prove
What a startup validator report should prove before you trust it: eight things, from a real buyer and sourced market size to kill risks and the next test.
Who pays, what it costs
Creating a business model for an AI startup: who pays, what it costs to serve them, and whether revenue covers it, with the nine-block canvas and AI costs.
Problem, prototype, price
Product validation in three stages: confirm the problem, test a prototype, then test willingness to pay, with the evidence each stage needs before the next.
Twelve weeks to a real venture
An entrepreneurship curriculum built on execution: what courses like Lean LaunchPad and I-Corps get right, and a 12-week plan with a weekly deliverable.
The founder sells first
Founder-led sales for B2B AI startups: why the founder sells first, the six-step first-customer playbook, and when to hand selling to a hire.
Talk first, then build
Customer discovery for technical founders: who to talk to, what to ask, how many conversations programs like NSF I-Corps expect, and how to log what you learn.