APP user operations: attract new users, promote activation, and gain return traffic!

I have shared many times how to develop the mobile Internet after the dividend ends. Among them, in addition to opening up the traffic of giants and omni-channel traffic, refined operations are the top priority. After all, in an era when even the landlords are shouting that they have no surplus food, it is better to do more, faster, better and cheaper than to be ostentatious. So, how to carry out refined operations?

Based on user population segmentation, labels of different granularity such as user attributes, importance, behavior, and life cycle, we build an analysis framework . For example, we distinguish between new, active, and returning users in the user life cycle, and conduct in-depth analysis and intervention. Ultimately, Mr. QM summarized it into three magic weapons: attracting new users, promoting activation, and attracting returning users.

Specifically, how did Taojiji use the "earn earn" model to achieve an MAU of over 10 million in two months, how did Baidu Kuaishou use the 100 million yuan subsidy + agent model to snatch food from the mouths of the top short video players, and what was the quality of the nearly one million users recalled by NetEase Cloud Music... You may wish to read the report.

1. Traffic is drying up, segmentation is exploding, duration is growing, and refined operations are becoming a consensus

1. The scale of monthly active smart devices in China's mobile Internet has peaked, and the year-on-year growth rate has slowed down to below 5%, but the user's online time has increased significantly, and there are still many opportunities in various sub-sectors.

II. Goals and scenarios of refined operations

1. User-oriented operations aim to maximize user value. By combining business scenarios with user grouping operations, we can reduce customer acquisition costs, increase user activity, and improve ARPU of existing users, ultimately increasing overall revenue.

2. User grouping is the most important prerequisite for refined operations. Users can be divided into finer granularity in different ways, and group operations can improve conversion effects.

3. Taking user cycle grouping as an example, build a refined operation analysis framework

3.1 Active User Analysis Framework and Case Analysis

3.1.1 User Analysis Framework

1) Core analysis indicators of active users

2) Active user segmentation analysis indicators

3.1.2 Typical APP Active User Analysis Case - Taojiji

1) Taojiji case study core conclusions and recommendations

2) Taojiji, which was officially launched in August 2018, has achieved a breakthrough in the fiercely competitive e-commerce field, with its MAU exceeding 10 million in just two months. In addition to the conventional "group buying at a low price" gameplay, Taojiji also launched the "earn money" function to stimulate user growth and improve user retention rate.

"Earn Earn" is a shopping function launched by Taojiji to attract new users and provide commissions as a way to give feedback to users. As long as users place orders, share and invite friends to consume on Taojiji, they can receive cash income.

3) More than a month after its launch, Taojiji iterated its products, added new customer discounts, and launched a new feature called “Earn Money”.

4) From the beginning of 2019, the 30-day retention rate of all user groups has increased. The retention rate of female users and users in lower-tier cities of Taojiji is higher than that of other groups. Among different age groups, the retention rate of users aged 19-24 surpassed that of users aged 41-45 in January 2019, reaching nearly 40%.

5) From the perspective of the 30-day retention rate of Taojiji's overall active users, although there has been a good growth, it is still 7.9 percentage points lower than the average of the top comprehensive e-commerce companies; combined with the retention rates of users with different attributes, Taojiji can focus on improving the retention rate of male users and users in second-tier and above cities in its subsequent user refinement operations.

3.2. New active user analysis framework and case analysis

3.2.1 User Analysis Framework

1) Added core analysis indicators of active users

2) Added new active user segmentation analysis indicators

3.2.2 Typical APP New User Analysis Case - Kuaishou

1) Conclusions and suggestions for the core research of the Kuaishou case

2) In the fiercely competitive short video market, Kuaishou has achieved significant growth in the past six months, with MAU exceeding 50 million in February 2019.

3) Billions of cash subsidies and the pioneering "agent model" have become the key factors for the explosive growth of new users of Kuaishou; as a Baidu product, Baidu APP has a significant effect on attracting traffic to Kuaishou: more than 70% of the new active users of Kuaishou are active users of Baidu APP

4) Competition for existing users in the short video industry: Among the new active users of Kuaishou in February, nearly 75% were existing users in the short video industry.

5) The trend of attracting new users is clear: the urban distribution is further sinking, and the users are extending to both the old and the young.

6) In terms of the stickiness of new users, the average performance of new users of the top short video apps is better than that of new users of Kuaishou

7) In addition, Kuaishou had a high new install-to-uninstall conversion rate in February, which was 7.6 percentage points higher than the average new install-to-uninstall conversion rate of the top short video apps. As short video content tends to be homogenized, Kuaishou must not only have a unique way to attract new users, but also continuously improve its content library construction, provide more innovative short video content, and optimize content distribution based on user preferences, thereby increasing user stickiness and retention.

3.3 Analysis framework and case analysis of lost and returned users

3.3.1 User Analysis Framework

1) Core analysis indicators of lost users

2) Analysis indicators of lost user segments

3) Analysis indicators of core returning users and segmented groups

3.3.2 Typical APP uninstall return user analysis case - NetEase Cloud Music

1) Core research questions and conclusions of the NetEase Cloud Music case study

2) Among online music industry apps in January 2019, NetEase Cloud Music ranked third in the rate of uninstalled users recalled, with nearly one million uninstalled users recalled.

3) The number of times and duration of use of NetEase Cloud Music’s uninstalled and returned users in January were higher than those of existing active users

4) Users who uninstalled NetEase Cloud Music but did not return were more active on Kugou Music, QQ Music and Kuwo Music in January

5) Among users who uninstalled NetEase Cloud Music and did not return, more than 60% were male users; and as the main force of NetEase Cloud Music, users aged 19-30 also had a non-return rate of more than 50%.

Author: Mr.QM

Source: QuestMobile (QuestMobile)