A complete data growth strategy should be the right time to do the right thing, but just knowing “things” is not enough. We also need the concept of “time”. We also need to know the “rightness” between contact time and things. What it means.
Editor’s note: This article is from the WeChat public account “Dead Leaf Cafe” , author Dead leaves.
Insight into user vitality is the basic condition for developing product data growth strategies. Perhaps you have read books about data growth, but it is still difficult to do good data growth.
It is not that the content of the book is too thin. The real reason is that we lack the ability to gain insight into the vitality of users. This leads to our inability to properly implement these methods. It is not a method error, but we Failed to realize the value of the method.
The complete data growth strategy should be the right time to do the right thing, but just knowing “things” is not enough. We also need the concept of “time”, and we need to know the relationship between the time and things. What does “fit” mean.
When you have insight into the vitality of your users, data growth strategies are no longer mysterious and difficult to use.
If we can induce users to pay for upgrades when they are in a good mood, and induce users to share and repost where they urgently need them, then our payment rate and our sharing rate will be greatly increased.
The difficult part is not how to make users happy, nor how to make them urgently needed, but how do we get insights, when are users happy, and when do they need them.
This is not the intuition of the head. We need more objective data for analysis and evidence.
The core of insight into the vitality of users is to present the chemical reaction inside the product through more granular data. Let us analyze and locate the problem more accurately.
In the case of the previous section, we added several data and information, and you will find that the problem has changed greatly. Your strategy may be judged invalid before it is put on the market.
After a period of data observation, we found that the retention data of new users is very good, and the retention rate of the next day is even much higher than the market average data, reaching an amazing 70%,
The 7-day retention rate is also 60%, and the 30-day retention rate is 50%
That is to say, 10,000 new users are added every day. At the 30th day, at least 150,000 to 200,000 users are counted in the daily activity data . However, the daily data is still 1 million, and there is no improvement.
This means that the retention of new users is notToo big a problem, they do stay in the product. The daily data did not improve due to other reasons.
Because of the problem localization, some of the plans you prepared are meaningless.
This kind of thing is staged in our work every day. Once the problem is located, any of your solutions is meaningless. If it has been invested in the development stage, it will inevitably lead to the loss of resources and time.
More importantly, you will overdraw the trust of your team over and over again, and your pressure will double with the results of each implementation.
Although we can mobilize everyone’s enthusiasm through good communication skills and excellent leadership, this cannot persist for a long time.
Any measure can only maintain the friendly phenomenon on the surface, and no disguise can cover up your trust and face the problem of insufficient balance.
As I describe it: This is a dilemma, and you are deeply trapped. Under the background of great pressure, you are very likely to continue to make wrong decisions. Maybe you will consider the poisonous snake to help you escape. The ropes of the jackpot.
It is unavoidable that you will get deeper and deeper. The final outcome may be the removal of the product, the dissolution of the project team, or even the dissolution of the company.
The culprit of all this, the root cause of your wrong decision is that we lack insight into the vitality of users.
Because of the lack of insight, we are unable to accurately locate the problem, which directly leads us to make the wrong decision.
If you can pinpoint the problem, the outcome will be very different.
It is still the same case. After deeper data analysis, you have found that the reason why daily live data no longer grows is not because new users are retained. To this end, you need a more granular data analysis model to help you find The real problem is.
A detailed analysis of daily live users in the last 7 days shows that the vitality distribution of daily live users is as follows:
Of the 1 million users who log in every day,
0% of users have registered for more than 150 days
5% of users have 120 days of registration,
10% of users have 100 days of registration,
35% of users have 80 days of registration time
25% of users have 60 days of registration
15% of users have 30 days of registration
Data shows that the real problem is not the retention of new users, but the survival of old users.
Of the 1 million users, only 100,000 have more than 100 days of registration.
This means that 10,000 new users today will have only 1,000 users after 100 days, and inWithin 150 days, these new users will be lost.
The user’s life cycle is short, and it can only gather a collection of surviving users for 150 days. Users who exceed 150 days will die silently.
This problem is the reason why 300,000 new users are added every month, but the daily live data is not improved.
The corresponding solution is to extend the life of the old users, so that more users can survive for 80 days, can survive for 100 days, and more users can survive for 120 days or longer.
In the analysis of more granular data, cumulative users are never continuously accumulated from beginning to end, but accumulated over the lifetime of users.
This requires you to accurately define how long the life of most users in the product is, and use this as the interval value to count the number of new users in the interval as the effective cumulative user.
In the case, the effective cumulative users are 150 days of cumulative new users. Based on the background of 10,000 new users per day, that is, 1.5 million effective cumulative users, rather than the 10 million users that have been continuously accumulated Or 20 million users,
For users who have completely died, they have no analytical value except to make the data look better.
Not only that, in fact, this part of the data is often an obstacle in our analysis process, which often makes us miscalculate the form, locate the problem incorrectly, and finally propose the wrong solution.
The granularity of data analysis is too large, which makes it difficult for us to accurately locate the problem, and it also affects our insight into the vitality of users.
In the case, if we can increase the vitality of users and allow more users to go from 80 days to 100 days, that is, increasing the proportion of old users among daily users, we can effectively improve daily activities. data.
It does n’t make much sense to increase the proportion of new users.
In the data model of the case, the registration time is a continuous accumulation process before 80 days, and the proportion of daily activities is increasing.
High, and after 80 days of registration, users begin to churn out on a large scale, and the proportion of daily activities is getting lower and lower.
Improving the retention of users before 80 days is a strategy of doing more with less. Even if we allow more users to survive for 80 days, large-scale churn will still occur after 80 days.
Improving user retention after 80 days is a way to do more with less, and a strategy to extend the vitality of users.
By extending more users from an 80-day life cycle to 100 days, the ultimate manifestation is to increase the proportion of users who last more than 100 days.
Even if we do n’t have additional new users, because of the cumulative effect of the old users, it can greatly improve the daily data, even some usersThe vitality can be extended to more than 150 days.
I think you have found that the higher the proportion of old users in the daily living data, the more daily active users of the product, the more it can show a positive development trend. On the contrary, if the daily living users, the higher the proportion of new users , It means that the product has entered a bottleneck period.
At this time, we are more effective at improving the retention of new users. You need to spend more energy and cost, but the effect is not satisfactory. And extending the life cycle of old users is a clever force that can bring fundamental improvements.
Insight into the vitality of users is the basic condition for a product to implement a data growth strategy. Its core lies in a deeper look at the chemical reactions inside the product. It requires the use of more granular data analysis methods.
When you master this method, you will no longer rely solely on ideas or practices, you will no longer rely on illusory luck, but you will expand your data more scientifically.
You will know what’s going on inside the product. Our positioning of the problem is no longer a broad retention, but a more granular user retention, a new user retention, or an old user retention. Retention.
You will know that the value of user vitality (days of survival) is extremely important to the product’s iterative strategy.
You will also know that a successful product, a great product, will not leave the two propositions, namely to continue the vitality of the user and prevent the death of the user.
In terms of products, we are like doctors, and will do everything we can to extend the life of users, and we will use every possibility to avoid user death.
The premise of this is that you can gain insight into the vitality of users, you can realize the problem, otherwise, you will be helpless.
It’s like a disease with no known symptoms, a disease that cannot be diagnosed, and it is difficult for even a good doctor to treat it.
If we ca n’t accurately find out the lifetime of users, you ca n’t know the starting point, you ca n’t even know what kind of services need to be provided for users, after all, the strategies needed to extend the life of 10 days to 30 days, A 100-day life span to 150 days is quite different.
This is not an operation-specific landing method, but a method that can assist us in product decision-making and assist product managers in in-depth problem localization.
His role is to enable us to locate problems more accurately, so as to formulate the most suitable product solutions and product strategies, and ultimately rely on product design methods to improve one or more data indicators.
Even if you do not change your operating strategy and do not rely on external resources, you can achieve data growth of the product itself.
Thinking
It will happen if we increase the proportion of users who registered for 100 days to 20%What changes?
Can you calculate the number of daily users at this time?