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Home Blog App Marketing What Are the Indicators for Measuring the Effectiveness of App’s Promotion

What Are the Indicators for Measuring the Effectiveness of App’s Promotion

Jun 21 2021

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For developers to face a promotion problem after the app is launched, it needs a lot of data to support the analysis.

Through the analysis of these data can grasp some important aspects of the operation, so that their own operations become targeted.

For this reason, if you are an app software promoter, it is necessary to understand what indicators are available to measure the effectiveness of app software promotion.


What are the measurement indicators


The most important concern when doing app promotion and ASO optimization is the effect, but this is also one of the most difficult things to do well.

In the process of statistics, often we need to pay attention to many data indicators, how do we use these data to determine the operation of the APP? Generally speaking this indicator is mainly these aspects, that is, the number of downloads, the number of users and conversion rate.

The measurement indicators of app software promotion are mainly the number of downloads, users and conversion rate. As an assessment indicator, it is different in the form of data performance at each stage.

In the beginning, operators should focus on the number of software downloads and user registrations to come up, which is an important indicator of how to measure the promotion skills of the operations team.

If the expected results are not achieved, you need to adjust your way of working in time and take some special promotion methods if necessary.

When the number of people reached a certain base, then you can focus on the number of users and conversion rate up.

The operation team should focus on how to retain these active users, activate those users who are not active, and adjust their operation plan according to the user's concerns in a timely manner.

Common app channel tracking methods


The monitoring of regular data indicators, such as user volume, new user volume, UGC volume, sales volume, paid volume, various data during the promotion period, etc.. These are the most basic and fundamental indicators that we are most concerned about.

For a rising app, you will spend resources to attract traffic and pull users to other channels. 

This time you need to monitor the good and bad of each channel, which one works well, which one is cheaper per unit, this is all need to channel data monitoring to complete.

Of course, you also need to track and monitor the subsequent performance of users in different channels, and give each channel users a score. You can also monitor the quality difference between iPhone and Android users, generally speaking, the quality of iphone users is slightly higher than that of android users.

Of course, if you have extra energy, you can also monitor the difference of user performance between different models. In short, it is to monitor the performance of different users in different dimensions.



The most direct indicators of app promotion


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The most direct indicator in the app promotion is the app stores ranking, when the basic engineering of your app is completed, the rest is the app promotion, and the ordinary promotion cycle is three months to six months, after such a cycle your app can rise in the app stores ranking, and get promoted and get traffic.

With ASO solution, it only takes about a week to get your app to the top of the app stores, so you can get the traffic quickly without waiting for a long time.


Major ideas of app channel data analysis


Generally, operators will judge the user's viscosity from the user's operation behavior, the higher the viscosity means the higher the quality of the user. Usually we will observe and compare the following indicators.

Launch times: It refers to the number of times users open APP in a certain statistical period, generally there are daily launch times, weekly launch times, monthly launch times, and the number of launches per capita in the corresponding period.

Online usage time: It refers to the time from opening APP to closing APP in the statistical period.

From the usage time, we can also extend the average usage time and single usage time, average usage time = total usage time in the statistical period / number of active users in the statistical period; single usage time = total usage time in the statistical period / number of launches.

This index is an important indicator of user viscosity and product quality, and the length of use is usually analyzed together with the number of starts.

Number of pages visited: This refers to the number of pages visited by users in a certain statistical period. For example, the number of active users visiting 1~2 pages, the number of active users visiting 3~5 pages, etc. The level of the number of pages visited is judged according to different statistical periods. The difference in the number of pages visited is used to judge the page quality and user experience.

Quantity: find the channel with the lowest cost of customer acquisition


It is possible to determine which channel has the highest quality users by looking at two dimensions: user behavior metrics and revenue metrics.

Quality: find the channel with the highest customer acquisition value


To screen a quality channel, the number of its users reaching a certain size is an essential prerequisite. Usually we observe and compare the following indicators.

Download volume: refers to the number of users who have downloaded and installed through the channel.

Registration volume: refers to the number of users who have registered through the download and installation.

Active users: The number of active users refers to the number of users who have launched the APP in a certain statistical period. Active users is an important data used to measure the scale of APP users and the status of the product.

According to different statistical cycles can be generally divided into: daily active users, weekly active users, monthly active users. Active users is an important data to measure the quality of channel users.

User retention rate: It refers to the retention rate of new users after a certain period of time. It is roughly divided into next-day retention rate, weekly retention rate, and monthly retention rate.

The number of new users (activated users): New users are users who start the application for the first time after installing APP, that is, activated users, it is meaningless for users to download and not use APP, so when evaluating the quality of users in the channel, we should not only focus on the amount of users' downloads, but also on the actual amount of users' activation. This is an important indicator to measure the effect of channel promotion.

Use the data provided by the platform


Industry data is crucial for understanding your APP, because with the comparison of industry data, you can know the level of your APP in the whole industry.

From it, we can analyze the advantages and disadvantages of our APP in the industry, find out the problems and make targeted adjustments in the future operation and promotion of APP.

The core conversion rate of users


Think about what the core function of your app is, and then go monitor the conversion rate of that core function. In a game app it might be called the pay rate, in an e-commerce app it might be called the purchase rate.

Different industries have correspondingly different conversion rates, and you can compare your product with the industry average to see where your product stands in the industry. At the same time, through long-term monitoring, you can also more this data to judge the APP different versions of good or bad.


The monitoring of user usage time


On the one hand, it is a very good indicator to monitor user activity. A long user time means a high activity level and vice versa.

On the other hand, think about how much time a normal user is expected to spend every day when your app is designed, and whether the time the user really spends is the same as your expectation after launch? If there is a big deviation, it means that the user's perception of the APP is different from what you thought at that time. This time you need to think how to adjust your product to meet the user's perception.

User losses


Losing users is a concept relative to active users, and refers to those users who have downloaded the app, launched it and registered, but gradually lost interest in the app and then completely left the product.

If active users are used to measure the current status of APP operation, then lost users are used to analyze whether the APP is at risk of being eliminated and whether your APP is capable of retaining new users.

Active user dynamics


Pay close attention to the dynamics of active APP users and listen to their voices. Once you find the abnormalities, immediately organize the staff to discuss countermeasures.

Active users (or core users) are the most valuable resource of the app, and we need to keep an eye on their every move.

User characterization


Describe the characteristics of each indicator of the user, the more detailed the better. Such as gender, age, geography, cell phone model, network model, occupation and income, interests, etc..

These data are not usually useful, but they can sometimes be very inspiring for product people.

If possible, it can also be divided into the following dimensions: what are the characteristics of active users, what are the characteristics of the more silent users, what are the characteristics of the lost users.

User lifecycle monitoring


The user life cycle is derived from marketing theory and is formerly known as the customer life cycle.

It has two meanings, one is the marketing survival window for the individual/group of users. Users change over time, and this change presents numerous marketing opportunities for the market and the company.

Another type of lifecycle is the user relationship management level, which is more important to operations staff. The business relationship between the product and the user changes over time. In traditional marketing, it is divided into potential users, interested users, new customers, old/cooked customers, and lost customers. These cascading stages are very similar to user activation.

The most critical aspect of marketing data analysis is the new customer - lost customer stage, how long a user can interact with the product will determine the product's viability.



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