Create a product that will be used regularly

A product earns repeat use when it continues to help someone accomplish something that matters. For a language application, it might mean practising a skill. The starting point should be the user’s need, not a target for how often the company wants someone to open a screen. Frequent activity and genuine usefulness are related questions, but they are not identical.

Nir Eyal, author of Hooked: How to Build Habit-Forming Products, describes a process called Habit Testing. Its three stages are identify, codify and modify. The approach encourages teams to study patterns among existing users and use what they learn to improve the product. It is most valuable when combined with careful interpretation and respect for people’s ability to choose how they spend their time.

Identify what meaningful use looks like

Begin by defining the activity that indicates someone received value. Signing in may be necessary, but it is rarely the outcome a person came for. A fictional meal-planning tool could consider a completed weekly plan more meaningful than the number of screens viewed. A repair-booking service might be successful when someone arranges an appointment quickly and then leaves.

The expected frequency should follow the need. A weekly planning activity does not require daily visits, and an occasional service can be useful without becoming a habit. Write down both the intended outcome and the period over which it makes sense to observe it. This makes the analysis easier to interpret and prevents an arbitrary activity target from becoming the definition of success.

Look for patterns among people who benefit

In Eyal’s terminology, codifying means examining the paths taken by people who already use the product regularly. Which steps appear before sustained use? Which features do they return to? The aim is to identify a plausible explanation that can be investigated, not to assume every shared behaviour causes loyalty.

For the fictional meal planner, returning users might often save a few favourite recipes during their first session. That observation creates a hypothesis: perhaps saving familiar meals makes the next plan easier. It does not yet prove the feature causes repeat use. People who already enjoy planning may simply be more likely both to save recipes and to return.

Eyal mentioned five percent as a rule of thumb for beginning this kind of investigation. It is not a universal threshold for commercial success. A business still needs to understand its costs, audience and the value delivered. A percentage without a clear definition of the users and observation period can be misleading.

Modify one part of the experience

Once a hypothesis is clear, choose a focused change. The meal planner might make the option to save a recipe easier to understand. It could also explain when that saved information will be useful. Avoid redesigning registration, navigation, reminders and pricing at the same time if the purpose is to learn which change helped.

Before testing, decide what would count as improvement and what problems to watch for. More saved recipes would be interesting, but completed plans and user feedback would provide a fuller picture. If people save items accidentally or cannot find them later, a higher activity count could conceal a worse experience. Examine the path as well as the final number.

Include people who leave

Regular users can reveal strengths, but they do not represent everyone. Ask people who stopped what they were trying to achieve and where the product fell short. Some may have solved a temporary problem; others may have encountered confusing instructions or an unsuitable feature. These explanations point towards different responses.

Do not assume every departure requires another notification. A reminder can be helpful when someone wants it, while repeated prompts can become a nuisance. Make controls understandable and allow people to change their preferences. A product should remain useful without relying on guilt, hidden choices or unnecessary obstacles to leaving.

Treat repeat use as evidence to interpret

Review changes over a period appropriate to the product. Compare similar groups where possible and note other events that might affect behaviour. A seasonal change or a different source of new users could influence results independently of the feature being tested. Keep uncertainty visible when reporting findings to the team.

Habit Testing offers a structure for learning from real use, but the objective should remain clear: help people achieve an outcome worth returning for. Define value, investigate patterns and make deliberate improvements. A product that respects the user’s purpose has a stronger foundation than one that merely succeeds in keeping a session open for longer.

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