APP operation: 4 ways to implement growth hacking!
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Operation is an art and even more a technology. In the past, the concept of “ traffic is king” made the responsibilities of operations staff focus on attracting new customers . However, in recent years, traffic costs have continued to rise, which objectively requires us to carry out refined operations and do as much as possible with the least money. As the market environment changes, the channels and methods of operation continue to increase, and operations have become more finely classified. How to use data analysis to solve growth problems in traffic operation, user operation , product operation and content operation . Today we will share with you GrowingIO’s practical experience in data operation . 1. Traffic operation: multi-dimensional analysis and channel optimization Traffic operation mainly solves the problem of where users come from. In the past, extensive traffic operations only focused on vanity indicators such as PV and UV, which is far from enough. 1. Traffic Overview Index System We need to use multi-dimensional indicators to determine the basic traffic situation, including magnitude indicators, basic quality indicators, and visitor type ratio indicators. The magnitude indicators involve different platforms. The web side mainly looks at the number of visits, PV and UV, and the APP mainly looks at the number of launches, DAU and NDAU. Basic quality indicators include the average visit duration of users, the average number of pages viewed per session (i.e., visit depth), and bounce rate. These indicators can be used to determine the user's activity. The product life cycle model is widely used in Internet operations . In different product life cycles , the types of visitors must be different. Through the [Overview] page, operators can clearly understand traffic indicators and their changing trends, and can evaluate past work and predict future traffic trends. 2. Multi-dimensional traffic analysis In website traffic analysis, the main angles include access sources, traffic entrances (landing pages), advertising (search terms), etc. First of all, the sources of visits include direct visits, external links, search engines , and social media . Under this analysis framework, we need to break it down layer by layer and conduct traffic analysis on each channel. Take the blog of GrowingIO as an example. This is a sub-site of content operation, which contains many articles on data analysis and growth hacking . Through analysis of visit sources, we found that compared with other channels, the number and quality of users coming from Weibo are relatively low. With limited operating resources, we can re-plan our media promotion strategy and focus our energy on high-quality channels. Secondly, the analysis of landing pages is also crucial to traffic, because landing pages are the entrance for users to reach your website. If users are directed to invalid or irrelevant pages, there will generally be a high bounce rate. Finally, advertising is also an important part of current traffic operations. The advertising analysis generally involved includes advertising sources, advertising content, advertising formats (clicks, pop-ups, effect guidance) and sales sharing, etc. We optimize advertising through multi-dimensional analysis. The above three factors are mainly for web-based analysis. For APP analysis, factors such as distribution channels and app versions need to be considered. 3. Conversion funnel analysis In the growth model, after traffic enters, it needs to be further activated and converted. The definition of activation is different in each product. In any case, activation requires certain processes and steps. We can find each step through the conversion funnel. Taking the above picture as an example, we analyze each step of the conversion separately. By analyzing the funnel on the left, we find that the loss rate from the first step to the second step is the highest, which requires targeted optimization. The conversion rate analysis of different channels on the right shows that the overall conversion rate is 8%, but the conversion rate from Baidu Brand Zone (bzclk.baidu.com) is as high as 44%, and the conversion rate of other channels is less than 3%. With the conversion rate data of each channel, we can optimize the channel operation strategy in a targeted manner. 4. Channel optimization configuration After conducting a series of traffic analysis and conversion analysis, we can formulate corresponding strategies, including search terms, landing pages, advertising optimization, etc. For channels with low cost and high quality, it is necessary to increase investment; for channels with high cost and high quality, it is necessary to evaluate the costs; and for channels with low quality, it is also necessary to conduct a good evaluation. In general, the channel configuration is managed and optimized as a whole based on comprehensive factors such as cost and traffic conversion. 2. User Operation: Refined Operation to Improve Retention If traffic operation solves the problem of where users come from, then user operation is about establishing and maintaining relationships with users. 1. Refined operations Users have many interactive behaviors on the product. We can classify users based on their behaviors; then, based on the characteristics of different groups, we can conduct refined operations to encourage user return visits. Taking the forum as an example, users' behaviors on the forum include: visiting and browsing posts; replying and commenting; posting; forwarding and sharing, etc. We establish a behavior index for each type of user behavior. For example, we establish a "communication behavior index" based on the user's forwarding, sharing and other behaviors, and classify users based on these indices. In this way, users on the forum are divided into four dimensions: A browsing, B commenting, C dissemination and D content production. A user may have only one tag index, or may have multiple index dimensions. In user operations, classification can be carried out according to these tags. For example, UGC forums need to maintain the activity and growth rate of Category D (content production) users; at the same time, in the promotion and dissemination of the forum, it is necessary to stimulate Category C (dissemination) users to expand the dissemination and influence of the content. 2. Improve user retention Internet products generally focus on user retention. Only when users stay can they further promote monetization and dissemination. Retention analysis generally uses cohort analysis, which is to analyze people with the same characteristics within a certain time frame. The retention chart shown above shows the horizontal comparison of the retention rate of new users added each week in the subsequent weeks, and the vertical comparison shows the retention performance of new users in different weeks in the future. Retention time and cycle are related to the complete cycle of product experience. Different businesses and products generally have different time group division methods. For example, the daily retention rate of high-quality products better reflects the relationship between users and products, while the weekly retention rate of tools is more meaningful than the daily retention rate. Through analysis of the time dimension, we can discover the changing trend of user retention ; through analysis of the behavior dimension, we can discover the differences between users in different groups and find the growth points of products or operations: this is a very important point in user operation. 3. Product Operations: Using Data to Analyze and Monitor Functions Product operation is a very large topic, and many operations and products are centered around products. Below we will discuss the analysis and monitoring of product functions. 1. Monitor abnormal indicators and find out what makes users angry about your product In the large product process, there are many small functional points, and the user experience is built on these small functional points; it is the usage of these small functional points that becomes the key to our conversion at each step. Taking the registration process as an example, mobile phone verification is generally required. Sending a verification code is one of the key conversion nodes; when the number of times users click to resend surges, it may mean that there is a problem with this function. This is what makes users angry: they cannot receive the mobile verification code in time. By monitoring key indicators, we can identify problems and fix them in a timely manner. 2. Test the effect of new features through retention curves For products that have been online for a while, new features are sometimes added. After going online, it is necessary to evaluate the effectiveness of the new function, whether it meets the core needs of users, and whether it can bring value to users. Through the retention curve, we can easily find that the proportion of people who used the new function on the first day and continued to use it was very low, which shows that this function did not solve the user problem well; this reminds us that we need to rethink the newly launched functions. 4. Content Operation: Accurately Analyze the Effect of Each Article What is content marketing? Many people think that content management is just editing articles and posting, but this is actually one-sided. Before doing content operations, you need to understand whether your content is intended as a product (such as Zhihu Daily) or an auxiliary function of the product. Only by understanding your own position can you set clear goals. In order to expand the effectiveness of content operations, we need to analyze user needs, such as the content that users are interested in, the proportion of content reading and dissemination, etc. 1. Content-based recommendations Take GrowingIO’s technical blog as an example, this blog belongs to the PGC model. The content in the blog is divided into different categories. In order to reduce the cost of users obtaining information, we have designed entrances to different sections on the blog homepage, including category navigation on the left, article recommendations in the middle, and hot recommendations on the right. We found that users mainly read articles through the navigation bar on the left and the recommendations in the middle, and rarely click on the hot recommendations on the right. Therefore, on the mobile side, we have cancelled the hot recommendations on the right and only retained the category navigation and the recommendations in the middle. It not only saves space, but also maximizes the user's content needs. At the same time, we also analyzed the content of the category navigation bar and found that users were most interested in the content of [Case Analysis]. This is a very good inspiration for our future content selection. 2. User-based recommendations Recommendations in content operations are sometimes closely related to refined user operations. Every user has his or her favorite content and categories. When we push content based on the user's interests, the efficiency will definitely be higher. Taking the blog of GrowingIO as an example, we counted the clicks on articles by visiting users and obtained the results in the above table. Obviously, user 8 has his own preference for "growth secrets", and users 6, 7, and 9 prefer "case sharing" articles. Then in the actual content push, we can push growth secrets articles to user 8, push case analysis articles to user 679, and push to other users indiscriminately. Data-driven refined operations The core of the " Growth Hacker " that has become popular in recent years is essentially to achieve refined operations and achieve the goal of growth through technological innovation and data analysis. An excellent operator should have data-driven thinking and master certain data analysis tools. In actual business work, we constantly raise questions from the data, keep trying, and use data to optimize operational strategies, thereby achieving customer and business growth. Mobile application product promotion service: APP promotion service Qinggua Media advertising The author of this article @GrowingIO compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting! Site Map |
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