Complete algorithm for determining the aha moment

[sakibkhan22197]

number of users who reach the aha moment;
the speed at which they reach it.
Carrot quest has a free trial period that lasts 7 days. To increase the likelihood of payment, we must bring a person to the aha-moment as quickly as possible.

Confirming the causal relationship between the aha moment and user success
The actions and methods above help identify potential aha moments, but the presence of a relationship between metrics does not mean a cause-and-effect relationship.

To prove a cause-and-effect relationship between the action found list of afghanistan cell phone number and user success, you need to conduct an experiment. In it, different segments of new users should experience an aha moment in the test and control versions of the product. If the share of successful users changes, this will prove a cause-and-effect relationship.

Experiments are often used to test cause and effect. For example, running A/B tests on different sections of the CJM and checking whether conversion has increased in the section for the group where the changes were implemented. The closer to 100% the metrics predicting user success and failure in the previous step of the analysis, the higher the probability of a cause and effect relationship.

The Aha moment depends on the task that the user wants to solve with the product. Therefore, when defining this moment, it is necessary to accurately indicate the use case and stages on the path to product adoption .

The search for an aha moment should begin with qualitative methods of analysis. To identify potential aha moments, answer the following questions:

1. For what task did your product achieve product/market fit?

2. What are the alternatives to solve this problem?

3. How does your product create added value compared to them?

The answers will help identify user actions within the product that can lead to the product's value being realized.

The next step is to use quantitative methods of analysis.

To describe the occurrence of an aha moment, use the structure: “The user performed X actions in Y days from the moment of registration.”

Next, select the optimal values ​​for the number of actions using correlation analysis.

Select the aha moment criterion that gives the maximum values ​​for these two indicators:

1. The share of users closed in success after completing the target action.

2. The share of users who were not closed as successful if they did not complete the target action.

The final step is to validate the causal relationship between the aha moment and user success. Run an experiment where you create a difference in the proportion of users who experience value. If the proportion of successful users changes, this proves the causal relationship.

Resume
Aha-moment is the moment when a new client realizes the value of the product and becomes its regular user. This is a simple action that is of key importance to the consumer. It is usually expressed in the number of messages received or sent in the application, loading reports, integrating data with other client services.