Every go-to-market plan contains assumptions. The useful question is whether you can state them clearly enough to test. In this lesson, Carlos Sanabria applies hypothesis-driven growth to pricing, audience selection, channel performance and the sales funnel.
The process creates a way to decide what to change. Instead of responding to a disappointing result by trying more tactics at once, find the assumption that failed and run an experiment around it.
What you'll learn.
- Write an assumption as a measurable prediction.
- Use small experiments and product behavior to gather evidence.
- Diagnose the funnel from audience fit through customer conversion.
State the assumption before the experiment.
Choosing a customer segment, setting a price and selecting a channel are all bets about how a market will respond. Put those bets into words before measuring the result. Otherwise, it becomes easy to reinterpret any outcome as success.
Carlos gives a hypothetical example connecting a price with an expected conversion rate. The numbers illustrate the structure of a test, not a benchmark other businesses should adopt. A useful hypothesis identifies the change, the people affected and the response you expect to observe.
Collect evidence from behavior.
Run a small experiment with a defined group and measure what happens. Campaign results can show whether a message attracts interest, while product analytics can reveal what happens after someone signs up.
The recording uses a complicated onboarding process as an example. A campaign may bring in new users successfully, but those users can still struggle to reach value. Watching the product journey helps distinguish a marketing problem from a problem inside the experience.
Work through the funnel in order.
The decision tree begins with a clearly defined ICP and evidence of interest. If that foundation is weak, refine the audience. If the audience is understood but active channels do not generate leads consistently, investigate distribution.
If leads arrive but few become opportunities, review whether the offer speaks to the buyer’s problem. If prospects attend demonstrations but rarely become customers, investigate the offer and the sales conversation. Each question points to a different intervention.
Use the result to decide the next move.
After collecting evidence, decide whether to continue, change direction or stop the experiment. Record what you learned so the next test starts from a better understanding of the market.
A practical review links each result back to the original prediction: what happened, where the expected behavior broke down and which explanation deserves the next test. Changing several layers at once makes that explanation harder to establish.
Growth experiments are useful when they improve decisions. Keep the audience, prediction and observation connected, then work on the first weak point the evidence reveals.
