Strategy 3 min read · · Last updated:
By Mark Ashworth · Founder, ChurnTools

I Changed One Word on a Button and Conversions Went Up 20%

From "could you try it" to "would you try it." One word. Conversions up 20%. You cannot brainstorm that result and you cannot one-shot it from an LLM. You can only find it by testing.

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TLDR: I once changed a single word on a button, from "could you try it" to "would you try it," and conversions went up 20%. Twenty percent, from one word. The lesson is not about that word. It is that you cannot find results like this in a brainstorm or a prompt. You find them by testing.

One word, twenty percent

"Could you try it" is a question about ability. "Would you try it" is a question about willingness. That tiny shift reframed the ask, and the number moved hard. If you had put both options in a meeting and asked people to vote, nobody would have predicted the gap. That is the whole point.

Why brainstorms and LLMs cannot give you this

A brainstorm produces opinions. A large language model produces the most probable phrasing based on everything it has read. Both are useful for generating candidates. Neither can tell you what your specific audience will actually do, because that lives in behavior, not in consensus or in training data. The only thing that settles it is putting both versions in front of real users and counting.

This is exactly why retention work has to be run as experiments, not guesses. The tactics that move your churn curve are often small, unglamorous, and impossible to predict in advance. You have to ship the test.

How to actually run these

  1. Pick one thing at a time. One word, one headline, one flow step. If you change five things and the number moves, you learn nothing about why.
  2. Decide the metric before you start. Conversion, activation, retained-at-30-days. Write it down first so you cannot rationalize afterward.
  3. Run it long enough to trust it. A 20% lift on 40 visitors is noise. Give it real volume before you call it.
  4. Keep the winners in a log. Small wins compound. Ten of these a year is a different business.

If you want a structured way to do this for retention specifically, the ship retention experiments in 15 days playbook lays out the cadence, and the full experiments library gives you tested tactics to put through your own A/B pipeline. To sanity-check whether a lift is even worth chasing, the MRR Impact Simulator shows what a few points of improvement is worth at your scale.

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Frequently asked questions

Answers to the questions I get most often about this topic.

Can one word really change conversions by 20%?

Yes, small copy changes can move conversion meaningfully because they change how the ask is framed. Switching a button from "could you try it" (a question about ability) to "would you try it" (a question about willingness) reframes the decision, and that produced a 20% lift in one case. The exact size depends on your audience and volume, which is precisely why you measure it rather than assume it.

Why not just ask an LLM for the best wording?

An LLM gives you the most probable phrasing based on its training data, and a brainstorm gives you opinions. Both are good for generating candidate variations. Neither can tell you what your specific audience will actually do, because that lives in real behavior, not in consensus or training data. Use them to create options, then let an A/B test decide the winner.

How do I run an A/B test properly?

Change one thing at a time so you can attribute the result, decide your success metric before you start so you cannot rationalize afterward, run the test long enough and on enough volume that the result is not noise, and keep a log of winners because small lifts compound over a year. For retention specifically, run these on a fixed cadence rather than ad hoc.

How does A/B testing relate to reducing churn?

The tactics that move a retention curve are usually small, unglamorous, and impossible to predict in advance, which makes them ideal candidates for testing rather than debating. Treating retention changes as experiments (onboarding tweaks, save-flow offers, email triggers) lets you find the handful that actually work instead of shipping guesses. The experiments library gives you tested starting points to run through your own A/B pipeline.
MA

Written by Mark Ashworth

Founder of ChurnTools. I spend my time studying how SaaS companies lose customers and building tools to help them stop. Previously worked in SaaS growth and retention across multiple B2B products. I also write about growth and answer-engine optimization (AEO) at growthpigeon.com.

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