Lei Feng network press: The author of this article is GrowthIO business analysis teacher.
The essence of “Growth Hacker†is to realize refined operations and achieve growth through technological innovation and data analysis. A good operator should have data-driven thinking and master certain data analysis tools.
Operation is an art, it is a technology.
In the past, the concept of "Traffic is king" has brought operational staff's responsibilities to focus on new initiatives. However, in recent years, the cost of traffic has continued to climb and objectively requires us to conduct meticulous operations and do as much as possible with the least amount of money. As the market environment changes, the channels and methods of operations continue to increase, and operations have been categorized more meticulously. How to use data analysis to solve the growth problems in traffic operations, user operations, product operations, and content operations, today we will share the practical experience of GrowingIO in data operations.
Traffic operation mainly solves the problem of where the users come from. In the past, extensive traffic operations focused on PV, UV, and other vanity indicators were not enough.
1. Traffic Overview Indicator System
We need to use multi-dimensional indicators to determine the basic traffic conditions, including the quantitative indicators, basic quality indicators and the proportion of visiting user types. The quantitative indicators cover different platforms. The Web side mainly looks at the traffic, PV, and UV. The APP mainly looks at the number of starts, DAU, and NDAU. The basic quality indicators include the user's average length of visits, average number of page views per session (ie, 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 the operation of the Internet. In different product life cycles, the types of visitors must be different.
Through the overview page, operators clearly understand the traffic indicators and their changing trends, which can be a good assessment of past work and forecast future traffic trends.
2. Multi-dimensional traffic analysis
In website traffic analysis, it mainly includes visit source, traffic entrance (landing page), advertisement (search word) and other angles.
First, the sources of visits include direct access, outreach, search engines, and social media. In this analysis framework, it needs to be dismantled on a layer-by-layer basis, and traffic analysis should be performed on each channel.
Take BloggingGrowingIO as an example. This is a subsite for content operations. There are many articles on data analysis and growth hacking. We analyzed the source of the visit and found that compared to other channels, the number and quality of users coming from Weibo are low . With limited operational resources, we can re-plan media promotion strategies and focus on high-quality channels.
Second, the analysis of the landing page is also crucial for traffic because the landing page is the entrance for users to your site . If the user is imported into an invalid or irrelevant page, there will generally be a higher bounce rate.
Finally, ad serving is also an important part of current traffic operations. The advertising analysis generally involved includes the advertising source, advertisement content, advertisement form (click, pop-up window, effect guide) and sales share, etc. We optimize advertising delivery through multi-dimensional analysis.
The above three factors are mainly analyzed on the web side. For APP analysis, factors such as distribution channels and app versions need to be considered.
3, analysis of the conversion funnel
In the growth model, after the flow enters, it needs further activation and transformation. The definition of activation in each product is different. However, activation requires certain processes and steps. We can find every step through the conversion funnel.
Taking the above diagram as an example, we analyze each step of the conversion separately. Analyzing the funnel on the left, we find that the churn rate from the first step to the second step is the highest, which needs targeted optimization. On the right side of the conversion rate analysis of different channels, found that the overall conversion rate was 8%, but the conversion rate from the Baidu brand area (bzclk.baidu.com) up to 44%, the conversion rate of other channels is less than 3%. With conversion rate data for each channel, we can tailor our channel operations strategy.
4, channel optimization configuration
After a series of traffic analysis and transformation analysis, we can formulate corresponding strategies, including search terms, landing pages, ad placement optimization, and so on.
For low-cost and high-quality channels, it is necessary to increase the supply. For high-cost and high-quality channels, the costs need to be assessed. For low-quality channels, it is also necessary to make assessments. In general, overall management and tuning of channel allocations are based on the overall situation of cost and traffic conversion.
Second, user operations: refined operations, improve retentionIf the traffic operation solves the problem of where the user comes from, then the user operation is to establish and maintain the relationship with the user.
1, fine operation
There are many interactions between users on the product. We can classify users by their actions . Then, according to the characteristics of different groups, we can carry out refined operations and promote users' return visits.
Taking the forum as an example, the user's behavior on the forum includes: accessing, browsing the post; replying, commenting, posting, forwarding, sharing, and the like. We establish a behavioral index for each type of user behavior, for example, establishing a “propagation behavior index†based on user's behaviors such as forwarding and sharing, and classify users through these indexes. As a result, the users on the forum are divided into four dimensions: A browsing category, B comment category, C communication category, and D content production category. Users may have only one label index, or they may span multiple index dimensions.
User operations can be classified according to these tags. For example, the UGC forum needs to maintain the activity and growth rate of users of category D (content production) users. At the same time, in the promotion and dissemination of forums, users of category C (communication) users need to be stimulated to increase the dissemination and influence of content.
2, improve the user's retention
Internet products are generally concerned with the retention of users, and only if users stay, can they further promote the realization and dissemination. The retention analysis generally adopts the method of group analysis, which is to analyze the population with the same characteristics within a certain time range.
In the retention graph shown in the figure above, the horizontal comparison shows the weekly retention rate of newly added users in subsequent weeks, and the vertical comparison shows the retention performance of new users in different weeks in the future .
The retention time and period are related to the complete cycle of product experience. Different services and products generally have different time grouping methods. For example, the retention of high-end products reflects the relationship between users and products better, and the weekly retention of tools is more meaningful than daily retention.
Through the analysis of the time dimension, we can find out the trend of user retention, find differences between users in different groups through the analysis of behavior dimensions, and find the growth point of products or operations: this is a very important point for user operations.
Third, product operations: use data to analyze and monitor functionsProduct operation is a very big topic. Many operations and products are built around products; below we discuss product function analysis and monitoring.
1. Monitoring abnormal indicators and discovering users' "furious points" for your products
In a large product flow, there are many small function points. The user's experience is based on these small function points; the use of these small function points becomes the key to our transformation at each step.
Take the registration process as an example. Generally, verification of the mobile phone is required. Sending a verification code is one of the key conversion nodes; when the user clicks to resend the number of times, it may mean that there is a problem with this function point. This is where the user's "running point" is located and cannot receive the phone verification code in time.
Through the monitoring of key indicators, we can promptly identify the problems and repair them in time.
2, check the effect of the new function through the retention curve
For products that go online for a while, new features are sometimes added. After going online, we need to evaluate the effectiveness of the new features, whether to meet the core needs of users, and whether it can bring value to users.
Through the retention curve, it is not difficult for us to find that the percentage of people who have used the new feature on the first day of continuous use is very low . This shows that this feature does not solve the user problem well; this reminds us that we need to renew the new online function. Thinking.
Fourth, content operations: accurate analysis of the effect of each articleWhat is content operation? Many people believe that content management is the editing of articles and postings. Actually, this is one-sided.
Before doing content operations, you need to understand whether your content is coming out as a product (such as knowing the daily newspaper) or an auxiliary function of the product. Only by understanding one's own positioning can the goal be clearly defined. In order to increase the effectiveness of content operations, we need to analyze the needs of users, such as the content of interest to users, the proportion of content reading and dissemination, and so on.
1, content-based recommendations
Take the blog of GrowingIO's technology blog as an example. This blog belongs to the PGC model. The contents of the blog have different categories. In order to reduce the cost of users' access to information, we design different sections of the blog entry, including navigation on the left side, recommendation on the middle article, and recommendation on the right side.
We found that users mainly read the articles through the navigation bar on the left and the recommendations in the middle, and less frequently click on the hotspot recommendations on the right. So, on the mobile side, we have removed the hotspot recommendation on the right, leaving only the category navigation and the middle recommendations. This not only saves space, but also maximizes the user's content needs.
At the same time, we also analyzed the contents of the categorized navigation bar and found that the users are most interested in the content of the case analysis, which is a very good inspiration for our future content selection.
2, based on the user's recommendation
Recommendations in content operations are sometimes related to the user's fine-tuned operations. Each user has his own favorite content and categories. When we push based on the user's interest, the efficiency will certainly be higher.
Similarly, using the blog of GrowingIO as an example, we have obtained the results of the above table by counting the clicks on articles visited by users. Obviously, user 8 has its own preference for “increasing cheats†and users 6, 7 and 9 prefer the “case sharing†article. Then, in the actual content push, we can push an increase in Cheats articles to user 8 and push case analysis articles to user 679. Other users push without any differences.
The essence of the "Growth Hacker" that has become popular in recent years is the essence of technological innovation and data analysis to realize refined operations and achieve growth . A good operator should have data-driven thinking and master certain data analysis tools. In the actual business work, we constantly raise questions from the data, and constantly try to use data to optimize our operating strategy and thus achieve customer and business growth.
Lei Fengwang Note: The author is the Department of GrowingIO Business Analysis Teacher, Lei Feng Network (search "Lei Feng Network" public number attention) released, and the first of its WeChat public number GrowingIO. Reproduced, please contact the authorizing and retain the source and author, may not delete the content.
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