a complete history of their actions on the site;

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Here are some signs of an aha moment that is more likely to lead users to success in the use case:

Users who complete this target action are highly likely to become successful;
Users who do not perform the target action are highly unlikely to become successful;
There is a cause-and-effect relationship between the target action and the user's success.
Below we will analyze step by step the process of determining the aha moment using the example of Carrot quest.

3 steps to determine aha-moment
At Carrot quest, we used the aha moment to improve onboarding for new users and get more activations for the product.

Segmentation and description of use cases
The team started by studying the sessions of new users. In Carrot list of afghanistan cell phone number quest, all user data is stored on the site. Here's what they studied:

history of communication, questions for support.
We also used the results of a JTBD interview with Carrot quest clients. As part of the JTBD study, we asked 20 users why they bought the service and received four groups of answers.

4 tasks that clients solve with Carrot quest:

Increase website conversion into applications.
Increase website conversion into purchase.
Communicate with users on the site or in the application.
Provide quality user support.
In addition to the tasks, the different segments had different:

the tools they use;
a set of scenarios that apply.
For example, to increase the conversion of a website into an application, users often set up a chatbot - it is designed to collect contacts, qualify leads, etc. This segment also appreciates pop-ups for lead generation in our service.

Clients who connect Carrot quest for support primarily use the chat and dialogue analytics. They set up pop-ups to inform the site audience about technical work and important changes.

Onboarding to the service before defining aha-moment
Carrot quest starter guide until 2022
The old onboarding helped users who wanted to interact with customers on the site. For the other three segments, it was more of a tax than a real benefit.

To personalize onboarding, we added qualifications during registration. To the standard set of user data (email, phone, name, and website address), we added questions about:

what team the user works in;
what problem does he want to solve with the help of Carrot quest.
Despite the fact that there were more registration screens, conversion increased from 28% to 43%.

This is how we approached the ICP (ideal client definition). For this, we used the analysis of client retention curves. Using these curves, we found the “eternity point” — the moment when the client is less likely to abandon the product and stay with us as long as possible or forever. This point can be interpreted as follows: our ideal client is the one who remains active in the sixth month after the first transaction. Most likely, he will stay with us in the future.

The stage result is a benchmark for activity on the 25th week after the first transaction. We focused on this value when analyzing user metrics.