AARRR User Operation Analysis

Concept

This document uses the AARRR model for user operation analysis. The AARRR model (also known as the Pirate Metrics), commonly used in the user operation process, explains the five metrics for achieving user growth: Acquisition, Activation, Retention, Revenue, and Referral. From customer acquisition to spreading and recommendation, the AARRR model forms a closed-loop pattern of a user's entire lifecycle, explaining the process of continuously expanded user base and sustained growth.

Preview

Each product (with its own situations) includes the development process of these five aspects. This document analyzes the five development processes of a vegetable-shopping app through FineBI.

Data Acquisition

Sample data: AARRR Sample Data.xlsx

Acquisition (obtaining new customers) is to make customers aware of, familiar with, and interested in the app, and encourage them to be on trial. Usually there are multiple channels to increase product exposure. But to select the optimal channel and achieve the best user acquisition effect with the least budget, you need to analyze channels.

Channel analysis usually has two dimensions: the quantity of customer acquisition and the quality of customer acquisition. In this document, the average time of opening the app is used as the evaluation standard for the quality of customer acquisition.

Conclusion: It can be seen that the offline event promotion can attract most new customers and achieve the best acquisition effect. 

Solution: Increase investment in offline activities (like conducting offline activities near supermarkets or vegetable markets)..

Activation does not mean successful registration. Activation is to engage customers. The users' usage of the product's core features should be considered more.

For example, to finish activation, short video apps require new users to watch for a certain duration, while chat apps require new users to complete a conversation. So in the vegetable-shopping app, activated users are those who have made a shopping for one time.

Analyze the user growth situation for each month and the corresponding line chart is as follows.

Conclusion: The activation rate decreased in October. 

Solution: Analyze the specific reasons. Then conduct new customer activities and carry out refined operations (for example, to attract users with personalized recommended products on the homepage).

After users are activated, they should be retained (very important) timely to avoid user churn.

Calculate the first week/second week/30 days retention rate of active users based on Retention Analysis.

Conclusion: User retention rate still can be improved.
Solution: Analyze the reasons for churn to further enhance product experience and retain existing users.

When a user is activated, you need to consider how to generate revenue and achieve profitability. The profitability of a vegetable-shopping app is related to many indicators. Here take improving user purchasing activity as a main way to increase revenue.

Divide users into three main categories: Low Active Users, Regular Users, and Premium Users. The funnel chart displays the user distribution clearly.

Conclusion: There are many low active users who are the potential regular users and premium users. 

Solution: Animate low active users and sustain premium users.

When a product has a certain scale of users, it is necessary to stimulate self-propagation among users. The data metric for self-propagating is the K-factor (recommendation coefficient):

K = (the average number of invitations sent by each user to their friends) * (the conversion rate of people receiving invitations to new users)

K value directly reflects the level of self-propagation results. Self-propagation can take effect with the K value greater than 1. Larger K value means stronger power of self-propagation. Self-propagation will gradually weaken and disappear with the K value less than 1.

Calculate the K value of the app.

Conclusion: The K value of the app is greater than 1,showing the apps possesses the power of self-propagation. Solution: Further improve the K value and accelerate the self-propagation speed through operational activities. For example, people inviting new users successfully can get a small gift.