How to make your APP stand out from competitors and attract more users?

With the rapid development of mobile Internet , a large number of APPs have emerged. Especially in the past year or so, the booming development of mini programs has posed a great challenge to APPs.

How to make your APP stand out from competitors? How to get more users? How to comprehensively manage and operate existing users? How to evaluate channel effectiveness and user quality and formulate correct operation and promotion strategies? All of this undoubtedly puts more demands on the data analysis and operational capabilities of APP promoters !

What we are going to talk about today is how to use statistical analysis tools to analyze and operate APP data.

1. Commonly used statistical analysis tools

Commonly used statistical analysis tools include LeanCloud Statistics, Flurry Analytics, iFlytek Open Statistics, DataEye, Tencent Cloud Analysis, Umeng Game Statistics Analysis, Youshu, ad-brix, and ASO 114.

You can choose statistical tools according to your needs.

II. Focus indicators of different product cycle data

1. Start-up

The focus of the start-up phase is to verify the core value of the product, or to verify the product's hypothesis: a certain product or service can solve a problem for a specific group of people. This stage should follow the MVP (Minimum Variable Product ) idea, verify the entrepreneurial idea at the lowest cost, quickly iterate to adjust the solution based on user feedback, and finally verify it with data.

Key data - target population profile

In the early stage of a startup, you can connect to some third-party application monitoring SDKs to understand the portrait of the initial user group, and verify from the side whether the characteristics of the user group are consistent with the assumed target user group. The most common ones are demographic attributes (gender, age, education, and region).

Key data - retention rate

When the current users meet the characteristics of the target audience, the core focus is on indicators such as the retention rate, usage time/frequency, and user stickiness of these users. Here we will discuss the retention rate in detail.

There are many dimensions of retention rate (7 days, two weeks, 30 days, etc.). Choose according to the characteristics of the product. If the product itself meets niche and low-frequency needs, the retention rate should be two weeks or even 30 days. A high retention rate means that users recognize the value of the product and become dependent on it. Generally speaking, the hypothesis can be verified. Usually a retention rate below 20% is a more dangerous signal.

2. Growth stage

During the growth stage, it is still necessary to pay attention to data such as user retention , user time, and changes in user portraits , but the focus can be on the management of the user's entire life cycle, focusing on the entire user behavior funnel analysis from the growth and activation of new users, triggering "aha moments" to stable and active users of the product.

Analysis of user behavior within the application ultimately determines the value that the product can bring. Developers can set up custom events and funnels to focus on product popularity, conversion rates at each step within the app, and the impact of conversion rates on revenue levels.

By analyzing event and funnel data, you can optimize the conversion steps in a targeted manner to improve the overall conversion level.

New user growth and activation:

Here I will focus on building the virality coefficient of the product, that is, letting the product grow spontaneously. The book "Lean Operations Data Analysis" mentions several interesting categories of user virality:

• Native virality, which is the way to attract new users through the app’s own invite-friend function;
• Word-of-mouth virality, that is, through word-of-mouth communication, users actively become new users through search engines ;
• Artificial virality means encouraging users to invite others through artificial intervention, such as incentives such as rewarded invitations.

The indicator we focus on here is called the "viral coefficient". Students who are interested can learn more about it on their own.

New user download -> activation -> 'Aha Moments' -> product stable and active

After the product begins to enter the spontaneous growth period, it is necessary to pay attention to the user life cycle from new users to active users (after retention) to core users, and refine and detail the key indicators of each process.

3. Maturity

With the rapid growth of users and continuous improvement of products, the focus of data operations begins to shift from the first half of the user life cycle (attraction, activation, retention) to the second half (churn, return) around the time the product enters the mature stage.

Loss and return:

In the process of paying attention to churn and return, the data will reveal a change in the current user base. For a specific analysis of the reasons for churn, please refer to the following process:

The core idea is to determine the reasons for user loss through qualitative follow-up and data verification as the main means, change the product operation strategy to prevent user loss or bring back users, and promote return.

In addition, for some stable delivery channels, ordinary improvement methods may have limited effect on improving conversions. At this time, more refined channel analysis can be performed to optimize and improve ROI.

4. Decline

Eventually, the product enters the decline stage. Generally, there are two ways to do this before entering the decline stage:

(1) Scale

This often occurs in the retail industry. For example, if a massage and health care store is opened and receives positive reviews within a certain range, a chain franchise model can be started when the product is mature. By rapidly and widely expanding the market, a brand effect can be formed to form a barrier, and the risk of recession can be resisted.

(2) Ecologicalization

When a product is growing or close to perfection, a single product may easily have problems with overly vertical demand and users being unable to form dependence. New products with synergistic capabilities can be developed to build a complete product ecosystem, so that users who cannot be satisfied or have lost interest in the current product can be directed to the new product and become new users of the new product. At the same time, users of the new product can also be directed back to the old product on the new product, forming a mutually dependent chain between products, and ultimately users can circulate effectively to form an ecosystem.

3. Data Analysis Methods

There are many data analysis methods, such as multi-dimensional time analysis, funnel analysis, follow-up analysis, cross-analysis, etc. Let’s take a cross-analysis example to help you understand.

Cross-analysis method: usually combines vertical comparison and horizontal comparison to conduct multi-angle combined analysis of data.

For example:

1. Cross-analysis perspective: client + time

The data in this table shows that the number of users on the iOS side is increasing each month, while the number on the Android side is decreasing. The main reason for the lack of obvious growth in overall data is the decline in data on the Android side.

Next, we analyze the reasons for the decline in Android data. At this time, we add the channel dimension.

2. Cross-analysis angles: client + time + channel

From this data table, we can see that the proportion of pre-installed channel A on the Android side is relatively high and is showing a downward trend, while the changes in other channels are not obvious. This is the main reason for the reduction in data on the Android side.

The main function of cross-analysis is to segment data from multiple perspectives and discover the root causes of data changes.

4. Channel Promotion Effect Evaluation

There are many channels to acquire users, such as Weibo, WeChat , operator stores, operating system stores, app stores , pre-installed by mobile phone manufacturers, CPA advertising, cross- promotion , limited-time free services, etc.

To evaluate the promotion effect of a channel, statistical analysis tools can be used. App operators or PR can compare the promotion effects of different channels based on data from multiple dimensions, such as the number of new users, active users, next-day retention rate, single-time usage duration, etc., and determine future promotion channels based on the data to achieve the best promotion effect.

V. Conclusion

Data analysis is a dynamic and complex job. As a qualified product operator, you must be highly sensitive to data. Analyze every user behavior through data, adjust promotion strategies , and carry out targeted and refined operations, ultimately achieving the goal of finding target user groups and increasing conversions.

Regarding APP operation optimization strategies, everyone is welcome to discuss with everyone in the comment area!

The author of this article @51coo compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

Product promotion services: APP promotion services, advertising platform, Longyou Games