APP user growth: How to use data analysis to improve user growth?

How can we make our APP stand out among a large number of competitors? How to maintain the growth and activity of APP users? In particular, how does the company's promotion department operate the correct promotion strategy? This is a test for our companies and practitioners who have independent APPs.

Now we are said to be in the era of big data, so how can we achieve user growth by leveraging the value of data? Four key steps.

The first step is to establish a data indicator system

As business managers, we will establish data indicators suitable for the company's operations and development at the company level. The principle is actually the same for APP operators . The first-level indicators are at the APP operation strategy level. This type of indicator represents the most basic and critical indicator of the APP. The second-level indicators are at the APP operation business level, and the third-level indicators are at the APP operation execution level.

Step 2: The focus of life cycle data for different products is also different

1. APP in the initial stage of construction

The focus during the initial stage of an APP launch is to verify the core model of the product. At this stage, the MVP concept can be adopted to verify the feasibility of the business model through small version testing, and to quickly iterate and adjust the solution based on the feedback from initial seed users.

MVP means: a cycle of hypothesis, execution, verification, and iteration

Core data dimension - target population portrait At this time, we can access a third-party application detection SDK to understand the portrait of the initial user group, and indirectly confirm the consistency of the user group with the characteristics of the assumed target user group, such as age group, education, gender, region, income and other dimensions.

Core data indicators - APP user retention rate When APP users meet the characteristics of the target user group, the core key indicators include the retention rate, usage time/frequency of these users, which mainly reflect user stickiness. There are many ways to divide the retention rate dimension, which can be based on week, biweekly, month, or quarter. According to the characteristics of the product, a retention rate below 20% is usually not ideal.

2. APP growth stage

During the growth stage, APP still needs to pay attention to data changes in user retention, user time, and user portraits. It can begin to focus on the management of the user's entire life cycle, focusing on data dimensions such as APP user growth and activation, and begin to identify user behavior loopholes in APP applications.

In the APP new user growth strategy, you can refer to the following methods

Word-of-mouth communication relies on the core value of the APP to meet the core pain points of users, and then everyone recommends it to each other. Activity planning is to guide users to download the APP and complete registration through sharing, activities, rewards and other means. The group buying mechanism is a problem with the APP mode settings, such as Pinduoduo’s group buying mode.

3. APP City Maturity Stage

In the later stage of APP promotion, with the rapid growth of users and the continuous improvement of products and services, the APP enters the mature stage. As data operation workers, the focus begins to shift from the first half of the user life cycle (growth, activation, retention) to the second half (recall, monetization) and other key points.

Recall and loss The specific analysis of the reasons for loss can be compared with the following process:

To solve the problem of loss in different links, we will optimize this link.

4. APP decline stage

There are two commonly used strategies during a recession. 1. Scaling. For example, in the recently popular new retail, if you open a barber shop and gain a good reputation within a certain range, you can adopt a franchise model when the technology and personnel are stable. By rapidly expanding the market, you can create a word-of-mouth effect, form barriers, and reduce risks during a recession.

② Ecosystem This is also a relatively ideal product state. A single product and service is difficult to survive and cannot meet the needs of different users. At this time, users are prone to churn and divert their attention. Therefore, products and services must directly form a chain of dependence and form an ecosystem.

Step 3: Common data analysis methods for APP

There are many data analysis methods, such as multi-dimensional time analysis, funnel analysis, return visit analysis, cross analysis, etc.

Step 4: Select commonly used data analysis tools

There are many unused data statistics tools and APP detection data on the market, which have been shared in the previous article. I would like to share with you here that the more common statistical analysis tools include Umeng Data Statistics, Youshu, LeanCloud Statistics, Flurry Analytics, iFlytek Open Statistics, etc. Each statistical tool has its own characteristics. I recommend that you choose a data tool that suits your own habits and data statistical methods.

Step 5. Summary

APP data analysis is a dynamic and complex task. As an operator, you must be familiar with the ability of data statistics and analysis, drive user growth through data, adjust promotion strategies in a timely manner, and perform targeted and refined operations, ultimately achieving growth in the target user group and improving conversion rates.

Attached APP operation core indicator chart

Author: Qiavi Technology

Source: Qiavi Technology