Build a product retention analysis system from 0 to 1
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In the capital winter, everyone is talking about growth, and DAU (daily active users) seems to have become the standard indicator for companies and media to describe product growth. However, can DAU growth alone be a talisman for a product? What are we missing in order to achieve long-term and stable user growth? This article will combine two cases to share with you the product iteration strategy of retention , Magic Number and user growth. 1. What is real user growth? Retention, as the name suggests, means that users stay on your website/APP and continue to use it. The concept of retention is simple and easy to understand, but few people can deeply understand the essence of retention. 1. The relationship between growth and retention In the picture above: new users come to our product and gradually leave over time; although the number of users is growing overall, the number of users leaving is also increasing. In the picture above: although there is a certain loss of new users after they come in, there is still a high retention rate; as time goes by, the total number of users increases very quickly, which is the real growth. Many companies spend a lot of money to attract new customers, such as the "first order free" model common in the O2O industry. Although the DAU increased significantly on that day, these users are actually negative assets; because many people left after enjoying the first discount, which is an unscientific way of growth. To achieve real growth, our products must first satisfy the core values of users, and then we must use operations to keep users and keep using our products for a long time. We can't always look at DAU, but should pay more attention to retention; only by improving retention can we achieve real growth. 2. When does a product start to grow? Not all products can grow rapidly at any stage. Before rapid growth, you need to accurately position your product. We need to make sure that there are people who use the product, that the user market has a certain size, and that there is a clear business model . This is the concept of Product Market Fit (PMF). Once we find PMF, we need to consider gradually growing it, and a very important point here is data-driven. 2. What are the methodologies for retention analysis? 1. Introduction to Data Analysis Methods Let me introduce to you a very simple data analysis methodology, which is applicable to most data analysis scenarios, including retention analysis. First, we discover problems through daily data monitoring, then set a solution goal based on the problem and use data to explore. In the process of exploring a problem, there may be many reasons; we will establish various hypotheses, conduct experiments based on the hypotheses, and finally test the hypotheses based on the experimental results. We continue this cycle until we find a satisfactory result, which we then use to optimize our product or operations. 2. Retention Analysis Framework The common AARRR model includes five steps, among which retention analysis is divided into two stages: new user retention analysis and product retention analysis. The first is the new user retention analysis. When users just start using our product, this is what determines whether they stay or leave. We basically only have one chance to showcase our products, and we must show the most valuable aspects of our products, otherwise users will be lost. The second is product retention analysis. After a new user stays and continues to use your product, he will gradually discover and explore the features of the different functions of the product. At this time, we need a function to impress users and come up with new features to make users feel that your product is good. Only in this way will users stay for a long time. Magic Number, which I will share with you later, is such a data analysis tool . 3. New User Retention Analysis: Sidekick Growth Process Sidekick is a SaaS company that provides enhanced email functionality. It can send personalized emails and monitor whether the recipient has opened the email. Through data monitoring, they found that the retention rate of new users continued to decline significantly. 1. Analysis of lost users In response to the serious problem of new user loss, we set a goal: to improve the retention rate in the first week. In order to achieve our goal, we need to conduct data exploration and conduct a portrait analysis of lost users to see what characteristics they have. We analyzed the number of times churned users used our product and found that nearly 60% of churned users churned after using our product only once. This means that when users come into contact with our product, if they have a bad first experience or do not discover the value of the product, they will be lost. Further interviews with lost users revealed that 30% of users did not feel the value of the product, and 30% of users said they did not understand the purpose of the product. The core of these two types of problems lies in how to enable users to quickly discover the value of our products, and they account for 60% and need to be solved as a priority. 2. Product Iteration Exploration There are two main ways to improve retention: one is to change products or technology, and the other is to intervene manually through operations. Since users have not discovered the value of our product in a timely manner, returning to the methodology just mentioned, we can conduct a series of explorations. Attempt 1: Remove infrequently used features Since users have not discovered the value of our product in a timely manner, we will try to cut out complex and difficult-to-understand functions and highlight core functions. The test results showed that the retention rate not only did not increase, but continued to decline. Attempt 2: Prompt customers to discover the core value of the product Users don’t know what the core value of our product is, so how about providing in-product prompts to new users? The results showed that the retention rate continued to decline, and the attempt did not produce any good results. Attempt 3: Product operation guide video Since users didn’t know how to use our product, we tried to make an introductory video; in fact, many companies are doing this, but the data showed that it still didn’t work. Attempt 4: Use the product directly in your mailbox After more than 20 experiments, we finally found a feasible method. After the user downloads and installs the product, we prompt the user: You can go to your mailbox to use our product and track emails. Because users discover products by downloading them from a web page and assume they can be used directly on the website, the retention rate of client-side products is very low. So they gave users a reminder – you can use it directly in your mailbox. After adding this sentence, the retention effect was much better. This is the result of the data. It was the blue line before, and then it gradually increased to the yellow line. Through this case, we can have a clear understanding of the methodology of retention analysis and the process of data analysis; and the process of product iteration is not that simple and requires repeated exploration and cycles. 4. Product Function Retention Analysis: Magic Number Exploration Practice 1. Product Function Retention Analysis Methodology After the retention rate of new users increases, we face the second problem, which is to move the retention curve upward, which is in a stable period. I have summarized three methods to improve the overall user retention and improve the retention curve during the stable period. First, analyze the retention trends of different functional modules to increase product stickiness. Second, analyze the number of visitors and activity levels of different functional modules. Third, analyze the path users take to use the functions, identify reasons for churn, and reduce churn rate. (II) Magic Number Exploration and Practice A user uses certain functions of our website or APP, performs certain actions, and then stays to continue using our products and becomes a loyal user. This shows that there is some correlation between user behavior and retention rate. We need to find this correlation and then see if there is a causal relationship. Magic Number may be a bit abstract, and we need to quantify it. Let me give you a few examples. Facebook found that users who added 7 friends within 10 days had a higher retention rate, and Twitter found that users who followed 30 influencers within 10 days had a higher retention rate. These Magic Numbers are found through data analysis and data mining, and there is a complete methodology. The first step is to determine the product on boarding functions. A social app may have multiple on-boarding functions, including logging in, adding friends, adding followers, sending messages, liking, sharing, uploading files, etc. The second step is to analyze the correlation between user behavior and final retention. The retention rate of users who click to follow users 7 times within a week is 57.5%, the retention rate of users who click to follow blogs 5 times within a week is 54.4%, and the retention rate of users who click to like or comment 6 times within a week is 52.6%, all of which are strongly correlated. The third step is to select the appropriate Magic Number. Select the appropriate Magic Number based on the company's current development strategy, operating costs, feasibility, and A/B testing. If the current development strategy of this APP product is to quickly acquire new users and expand the market, then we can use "adding 7 new users within a week" as the final Magic Number. The fourth step is to find the final Magic Number, and then we need to execute and operate it well. For example, in this social APP, users are encouraged to add friends and friends are recommended to users more accurately. This will achieve the original goal, cultivate user product usage habits, increase user stickiness, and promote growth. Once retention is improved, we can monetize users or spread recommendations, so that our user base will gradually increase. By continuously attracting new users to the market, the retained users will gradually settle down and become our important users, which can be monetized. As for those unstable users, we still need to make various product and operational improvements to gradually turn them into retained users and then start monetizing them. Only when user retention is improved can we truly achieve growth in active users. Mobile application product promotion service: APP promotion service Qinggua Media advertising The author of this article @檀润洋 is compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting! |
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