Users are expensive, channels are expensive, promotion is expensive...How should we operate?

In the era of super users , if I had to sum it up in one word, it would be "expensive". Users are expensive, channels are expensive, product research and development is expensive, and promotion is also expensive. Therefore, we should pay more attention to the retention of new users and the LTV user lifetime value.

First of all, I have three thoughts about the “era of super users”. The first point is to shift from “focusing on traffic ” to “focusing on LTV”. There are two main dimensions to increase LTV value: one is the horizontal dimension, which is to extend the user life cycle. For example, a website for girls can expand its business to marriage, home furnishing, maternal and child care , early education, etc. as the users grow, thereby horizontally expanding the user life cycle value; the other is the vertical dimension, which is to dig deep into the needs of specific groups of people. For example, like Yitiao Xu Husheng, in-depth exploration is conducted on the middle-class user group.

The second point is the change of thinking. In the past, everyone relied on providing various free products and services to attract and acquire users. However, in the era of super users, we need to focus on whether the product itself can bring real value to users. Because in the mobile Internet era, there are many user entrances and time is fragmented, rather than search engines as in the PC era. User concentration and usage time are very expensive and scarce, and valuable products must be charged. The third point is that in the past we tried every possible means to attract users, but now we try to find ways to keep them from leaving . Fourthly, in the past, users were just visitors, and each connection only added one UV/PV to the data. But now , we must make friends with users and invest energy and time to achieve user conversion and find super users.

The second keyword, “data-driven operations”, also has four points of understanding.

The first is data planning, which is to set an operational goal that is executable, achievable, decomposable, and can be indexed. This is also a necessary ability for operators at any level.

The second is data collection , which collects user basic data, user attributes, user sources, user behaviors and other data through code embedding or no embedding.

The third is data modeling , which involves modeling the collected data based on data planning.

The fourth is data analysis . As the expert from Umeng + mentioned just now, data-based analysis must be conducted through multi-dimensional cross-analysis, including comprehensive analysis through behavioral events, funnels, A/B testing, etc.

The first step is to understand who the user is.

Many people think this question is very simple, and I used to think so too, but after I put it into practice, I found this question really difficult to answer. Because any product has its own life cycle, such as from the introduction stage, growth stage, maturity stage to the decline stage, the following figure is a differentiated operation method formulated for different products in their life cycle.

Our earliest seed users came from educational institutions and offline marketing . It was a closed ecosystem and all users were acquired offline. In 2015, we were eager to achieve success, so we iterated our product and added an open registration function. From the statistics of [Umeng+] on the right, we can see that the number of users has not increased but has decreased. Because the product iteration was logically wrong, we thought that since we already had seed users, we could open the entrance, but we ignored the fact that the product did not have the functions to reach the growth stage, which resulted in users coming and leaving. This was caused by a lack of clarity about the target users .

We need to identify the product life cycle stage through data insights. In the startup phase, it is important to understand what users need and what kind of products they want to see. Through third-party data statistics such as [Umeng+] or its own BI system, it monitors user behavior and meets these user needs through product iteration.

Step 2: Who is my super user?

Combining the user life cycle and the product life cycle, we can see that super users in different product cycles will also be different. Super users are not static. They also have their own life cycle and will change as a group. We must use data operations to extend the user life cycle as much as possible while converting users.

The third step is to develop differentiated operation strategies

From the introduction period to the growth period , we utilized differentiated operation strategies and built a user growth and incentive system, including level icons and points exchange, which significantly increased user activity. At this stage, super users are active users because we don’t have many ways to get users to convert or consume.

During the transition from the growth stage to the mature stage, the definition of super user changes. We conducted explicit and implicit analysis around the iceberg model and launched a kindergarten access control system and online video. Parents need to swipe their cards to pick up their children, and can use the App to watch their children’s situation in the kindergarten online. At this stage, super users are paying users . Through such demand exploration and corresponding operational means, the monthly user retention rate reached 50% at that time, which is a very high figure in the industry. Generally, a monthly retention rate of 15% is already very good.

Super user thinking reminds us that at a time when traffic dividends are disappearing, we should focus on using data to understand user needs, study user behavior, and do a good job in retention and conversion to increase user LTV. However, everyone must be clear about their own positioning and current situation. There must be users before there can be paying users, and there will be super users only after there are paying users. Luo Pang said that he is facing super users and turning his back on ordinary users because he already has a fan base of tens of millions. Based on this premise, we can truly understand the operational logic in the era of super users.

Finally, I would like to end with a sentence: If the purpose of learning is to continuously expand one's cognitive boundaries, then "data" is the key to continuously expanding the boundaries of cognitive super users.

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

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