New User Activation from the Aha Moment

In 2008, when you were staying in your dormitory playing games and found it was already very late, you opened the Ele.me app and ordered some egg fried rice. Half an hour later, someone delivered the meal to your dormitory. You can't help but say, "Aha, it turns out that with this app, you can enjoy the delicious food around you without leaving home!"

In 2015, when you were walking in school and saw many shared bikes, you could download an app and ride them away. You couldn’t help but say, “Aha, shared bikes are so cheap and you can ride them anywhere!”

Do you remember how you discovered the value of these products when you first used them as a new user?

1. Why is the activation of new users so important?

The life cycle of a single user in a product includes four stages: new user acquisition -> activation -> retention -> churn. Many product managers and operations only focus on the first step, which is how to attract new users. They organize various activities and spend millions of dollars to attract users to the product through different channels, hoping that this will increase the number of users of the product.

Little do people know that if the product experience and value are not well polished, and user activation and retention are not done well, more than 90% of users will be lost after entering the product. Therefore, we also need to pay attention to why users stay after entering your product for the first time? Why are so many users leaving again?

From the relationship between the number of days users use the App and the proportion of active users in the above figure, it can be seen that, except for the top-level products in the Internet field, most APPs lost more than 70% of their users on the second day. The main reasons for the loss of new users are as follows:

1) Product function issues

The product functions cannot meet user needs well, or user needs are not strong. At this time, product managers are needed to polish the product and enhance its value;

2) There is a problem with the new customer acquisition channel

New users are not the target users of the product. In this case, we need to reflect on whether there is a problem with the channel for attracting new customers.

3) Product value issues

The product is valuable to users, but if the traffic is not properly attracted, users will be lost without discovering the value of the product. In this case, it is necessary to activate and retain new users, guide new users to enter the product, stay, and discover the value of the product.

So why is new user activation so important? On the one hand, the activation of new users is in the early stage of the entire user life cycle and is at the top of product traffic. User retention at this stage has a greater impact on later user retention and profitability. On the other hand, new users are most excited and expectant about the product at this stage, and a small change can bring significant results.

2. What is the Aha moment?

New user activation is the first step after acquiring new users, which is to let users experience the value of your product for the first time and complete key conversions so that they can be retained. The Aha moment is the specific goal of activating new users. The Aha moment is the moment when a user first recognizes that a product is valuable to them. The essence of the Aha moment is to use simplified behavioral data to simulate the moment when a user first obtains value.

Here are some representative APP user Aha moments:

• Alipay, stably use more than 3 functions of Alipay.
• Faceu is super cute, use filters to beautify the first photo.
• Airbnb, complete first order in 6 months, 4-star rating or above.

It is important to note that user activation is not the same as the Aha moment. The Aha moment is when the user truly experiences the value of the product, while activation is the behavior generated after experiencing the value of the product. The reason why Aha moment can be used as a measure of new user activation is based on three basic assumptions:

1. This user is the target user of the product, that is, the product can generate long-term value for the user;
2. This long-term value of the product can be quickly felt by users through certain key behaviors;
3. New users who perceive this value are more likely to stay.

3. How to find the Aha moment for your product?

From the above explanation, we can see that the Aha moment includes three key factors: the user’s activation behavior; the time window of the activation behavior; and the number of times this behavior occurs. The following will introduce in detail how to confirm these key factors of the product.

1. User activation behavior

Activation behavior is strongly related to the product's attributes. For gaming products, the Aha moment may be the user returning to the game the next day. For social products, the Aha moment may be adding 10 friends to the product. For information flow products, it may take more than 10 seconds to open and read a piece of news.

There are two steps to finding the Aha moment behavior:

1. Conduct research and analysis on users
2. Determine core user behaviors based on product value

Conduct user surveys through questionnaires and other means, collect user feedback on the product, and analyze what users think are the most valuable features of the product? What key actions did you take to realize that the product has this feature? After collecting and summarizing some key functional points of the product, analyze them and think about what the biggest pain point the product wants to solve for users? How does the product solve this pain point? How do competitors solve this pain point? How is our solution different from theirs? How to make users realize the value of the product, etc.

2. Time window for activation behavior

The time window for new user activation behavior refers to how quickly new users can complete activation. To determine this time period, the following three principles must be followed:

1) The higher the frequency of use, the faster the activation needs to be

The higher the frequency of use, the sooner new users expect to obtain value from the product. The activation period of new users can be roughly determined based on the frequency of use.

2) The shorter the life cycle, the faster the activation needs to be

The following is a ranking of the life cycles of products with different attributes. From left to right, the longer the user's life cycle is.

Games < Social < Content < E-commerce < Tools < Platform < SaaS

3) Refer to actual data

Analyze the actual retention data of new users of the product and look at the time window when most early activation behaviors occur.

3. Number of times the behavior occurs

After confirming the Aha moment behavior based on product attributes, we need to determine how many times this behavior must occur to reach the user activation state?

First, think about the following questions:

• Why does Alipay require stable use of three functions to be considered user activation, instead of just one function?
• Why is it that Twitter considers a user activated only when he follows 30 other users, instead of 10?

In fact, as far as the product is concerned, the retention rate of Twitter users who follow 50 other users will definitely be higher than that of those who only follow 30 other users. So why are there only 10 activated Twitter users?

First of all, the number of times a user behavior occurs 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 to activate it. However, for tool products, they may have 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 not realistic to ask users to repeat too many times. Therefore, we hope to find the optimal number of activation behaviors to ensure that users obtain value without bringing burden to users.

The following two methods are introduced using a camera product to explain how to find the number of times a behavior occurs:

The first method is: the maximum marginal utility point

Collect user data and draw a distribution chart of the number of activation behaviors on the first day:

First, we collect the relationship between the number of times users use filters and the next-day retention rate. For example, a total of 1,800 users used the product filter twice on the first day, and 250 users used the filter four times on the first day. It can also be seen from the figure that the users who used the filter the most times on the first day had a higher next-day retention rate.

Relationship between the number of activation behaviors on the first day and the retention rate on the next day:

From the relationship graph between the next-day retention rate and the number of times the filter is used on the first day, find the inflection point of the retention rate, which is the number of times the marginal utility is the largest. As can be seen from the figure, although the next-day retention rate gradually increases with the number of times the filter is used, the next-day retention rate increases the most when the user uses the filter 0 times and once on the first day. Therefore, it can be concluded that the user's use of the filter once has the greatest impact on the next-day retention rate, that is, the number of Aha moment behaviors occurring is 1 time.

The second method: Venn diagram method

This method uses the set relationship of the Venn diagram to find the largest intersection between the number of users who perform a certain behavior and the retained users, thereby finding the factor that has the greatest impact on user retention rate.

Specifically, three behaviors need to be analyzed:

1. The number/total number of people who have both the behavior and remain;
2. The number/total number of people who took the action but did not retain the data the next day;
3. The number/total number of people who remained on the next day but did not take the action;

These three sets of data can be used to comprehensively analyze the impact of a certain behavior on product user retention. For example, the data between the number of times the filter is used and the user retention of the above-mentioned camera product is expanded according to the above three dimensions. From the data in the figure, it can be seen that among the number of people/total number who have both this behavior and retention, the number of users who use the filter at least once is the highest, that is, the retention of users who use the filter at least once is the highest. In other words, a user's first use has the greatest impact on user retention.

By converting the data in the table into a Venn diagram, we can see more clearly that users who use the app once on the first day have the highest second-day retention rate. Therefore, using the Venn diagram method, we can intuitively find the Aha moment of this camera product.

The above steps can help you find the Aha moment of a product. However, it should be noted that the number of times the Aha moment occurs is not absolute. It only represents the statistical situation of users and is the turning point for most users. After finding the Aha moment, you need to verify its accuracy through testing. For example, design an A/B Test experiment to verify the causal relationship between the Aha moment and user retention, that is, whether this behavior can definitely improve user retention.

Finding the Aha moment is only the first step in analyzing the activation of new users of a product. We also need to design an activation funnel to analyze which step has the highest churn rate and analyze and track the user’s actual path. The purpose of finding this moment is to analyze and optimize the path for activating new users, improve user retention, and bring greater value to the product.

Author: Kebi sauce

Source: Kebi sauce