From the above explanation, we can see that the Aha moment contains three key factors, the user's activation behavior; the time window of the activation behavior; the number of times this behavior occurs. These key factors in how to identify a product are detailed below.
1. User Activation Behavior
The activation behavior is strongly related to the attributes of the product. For game products, the Aha moment may be the behavior of the user returning to the game the next day. For social products, the Aha moment may become adding 10 friends to the product. For information flow products, it may be to open and read a news for more than 10s.
There are two steps to finding Aha moment behavior:
Conduct user research and analysis
Determine user core behavior based on product value
Conduct user research through questionnaires and other methods, collect user feedback on the product, and analyze what users think are the most valuable functional points in this product? What key actions did you take to realize that the product has this function point? After collecting and summarizing some key function points of the product, analyze it, and think about what is the biggest pain point that the product wants to solve for users? How does the product solve this pain point? How do competitors solve this pain point? How are our solutions different from theirs? How to make country email list users realize the value of the product, etc.
2. Time window to activate behavior
The time window for the activation behavior of new users refers to how quickly the new users will complete the activation. To confirm this time period, the following three principles need to be followed:
1) The higher the frequency of use, the faster the activation needs
The higher the frequency of use, the sooner new users expect to obtain value from the product, and the activation period for new users can be roughly judged based on the frequency of use.
2) The shorter the life cycle, the faster the activation needs
The following lists the life cycle ranking of products with different attributes. From left to right, the user has a longer life cycle.
Gaming < Social < Content < E-commerce < Tools < Platform < SaaS
3) Refer to the actual data situation
Analyze the actual retention data of new users of the product to see the time window in which the vast majority of early activation behavior occurs.
3. Number of times the behavior occurred
After confirming the behavior of the Aha moment according to the product attributes, it is necessary to confirm how many times this behavior has to occur before it reaches the state of user activation?
First consider the following questions:
Why does Alipay need to use three functions stably to be considered as user activation instead of one function?
Why do Twitter users follow 30 other users to count as user activation, not 10?
In fact, as far as products are concerned, the retention rate of Twitter users who follow 50 other users will definitely be higher than that of only 30 other users, so why are only 10 Twitter users activated?
First of all, the number of user behaviors is directly related to the natural attributes of the product. For some products, it is enough to do it once. For example, for e-commerce products, users only need to place an order once, even if it is activated, but for tool products, it may be He has to use certain functions multiple times to achieve activation. In addition, although the more repetitions, the greater the improvement in retention, the activation time for new users is limited, and it is unrealistic for users to repeat too many times. Therefore, it is hoped to find the optimal number of activation behaviors to ensure that users obtain value without giving Users are burdened.
Here are two methods for a camera product to explain how to find the number of occurrences of a behavior:
The first method is: the point of maximum marginal utility
Collect user data and draw a distribution map of the number of activations on the first day:
First, collect the relationship between the number of users using the filter and the retention rate on the next day. For example, a total of 1800 users use the product filter twice on the first day, and 250 users use the filter four times on the first day. It can be seen that the users who use the most filters on the first day have a higher retention rate on the next day.
The relationship between the number of activations on the first day and the retention rate on the next day:
Find the inflection point of the retention rate, that is, the number of times with the largest marginal utility, from the relationship between the retention rate on the next day and the number of times the filter is used on the first day. As can be seen from the figure, although the retention rate on the next day gradually increases with the increase in the number of filters used, the retention rate increases the most on the next day after users use 0 filters and 1 filter on the first day. It is concluded that using a filter once by a user has the greatest impact on the retention rate of the next day, that is, the number of occurrences of Aha moment behavior is 1 time.
The second method: Venn diagram method
This method finds the largest intersection between users of a certain behavior and retained users through the set relationship of Venn diagrams, so as to find the factors that have the greatest impact on the user retention rate.
Specifically, three behaviors need to be analyzed:
Both the behavior and the number/total number of people retained;
The number/total number of people who have the behavior but not retained the next day;
The number/total number retained for the next day without the act;
These three sets of data are used to comprehensively analyze the impact of a certain behavior on product user retention. For example, the data between the number of times of using filters of the above camera products and the user retention are expanded according to the above three dimensions. It can be seen from the data in the figure that for both the behavior and the number of people/total retained, the user has at least Using the filter once is the highest, that is, users who use the filter at least once have the highest retention. That is to say, once a user uses it, it has the greatest impact on user retention.
Obvious that users who use once on the first day have the highest retention rates on the next day. Therefore, using the Venn diagram method can intuitively find the Aha moment of this camera product.