Xunlei Kankan CEO: This is how you should analyze user needs!

Xunlei Kankan CEO: This is how you should analyze user needs!
There is no need to elaborate on how important user needs are. Just look at the four words that product and operation people always talk about, and you can see it. However, if we only talk about how important user needs are, but keep silent about how to discover user needs, this will not help us solve the problem at all, and we can only be standing in circle. So how can we discover user needs? Some time ago, I read "User Power" written by Hao Zhizhong, CEO of Xunlei Kankan. The author's logic is very clear: taking "user demand-driven" as the main line, connecting products, operations and business models. Many of them are conducted with a reverse thinking approach, such as how to discover user needs: Unlike the common analysis approach, the author does not first find the user group and then analyze the user needs, but first clarifies what the user needs are and then finds the group that is most sensitive to this need. This idea is indeed very interesting. When we compare it with common ideas, it may give people more inspiration. Here I will sort out the content in the book about discovering user needs and share it with you. 

 Let me first state the conclusion. According to the author's thinking, the logic of analyzing user needs should be as follows: discover the real needs of users - verify whether the needs of fan users exist - quantify the purpose, behavior and reasons of user needs - filter out unreasonable and niche needs - sort the needs - classify users to more clearly define the user level and intensity of needs. The following is a specific description, which may be a little difficult to read. If you don’t have time to read, I suggest you save this article: First of all, we need to know what the users’ real needs are, and at the same time find the fan users who are most sensitive to those needs, so as to “find users” based on their needs and describe these fan users clearly. The author believes that the real needs of users can be summed up in one word: "more" - cheaper, faster, more fun, and these four "mores" are typical user needs. Looking at the excellent and representative products on the Internet, all of them have done a great job in these four aspects and achieved success by better meeting user needs. For example, Didi actually meets the needs of users to get a taxi faster and cheaper. For example, Dianping meets the needs of users to find more food, drinks, and entertainment faster. The key to discovering user needs is to discover the users’ “real needs” and then identify users based on those needs. The key here is to find “fan users” – those who have the highest demand for the product and have the highest usage rate. Finding fan users can not only verify whether the demand is real, but also have an important impact on the use, dissemination and feedback of new products after they are launched. For example, Dianping discovered that the real needs of users are to find more delicious food, drinks and fun things faster. What needs to be done next is to determine who has this need and at the same time find the group that is most sensitive to this need (i.e. fan users). The process of finding fan users is actually the process of describing users - we can describe our fan users through three dimensions: user characteristics (including basic attributes such as gender, age, habits, etc.), user scenarios (imagine the time, place and situation when users generate needs), and demand frequency (i.e. the average number of times users use the service in a period of time). For example, Dianping’s fan users may be young white-collar workers in big cities who have certain spending power and love food. Secondly, through the method of demand collection and analysis, we can verify whether the demand exists and quantify the purpose, behavior and reasons of user needs. In the book, the author recommends four commonly used methods for demand collection and analysis: user interviews, usability testing, questionnaires, and data analysis. According to their nature, they can be divided into two categories: "qualitative" and "quantitative". Qualitative is to subjectively summarize user needs through observation and communication; quantitative is to objectively judge needs through data. In terms of form, it can be divided into "saying" and "doing". "Say" comes from direct expression by users, and "doing" comes from the analysis of user behavior.

 These four methods have their own characteristics. In specific use, we can choose according to the actual situation:
  • The focus of user interviews is to directly collect user needs through interactive conversations with users, which plays the role of "discovering" user needs;
  • Usability testing is a qualitative analysis method, which is used to "verify" user needs through actual user use;
  • Questionnaires and data analysis are two methods for "quantifying" user needs. Their use depends on the maturity of the product. Immature products naturally use questionnaires, while data analysis is more suitable for products with relatively complete data.
 Finally, if the demand does exist, the next step is to further refine the user information, feedback, and demand data obtained during the demand analysis - confirm what the user needs are and sort them; confirm who the users are and classify them. The first step in demand extraction is to complete the filtering and aggregation of demands: first filter out obviously unreasonable demands, niche demands, and demands without application scenarios. Then, summarize the requirements and users: need
  • Question: What are the main issues and data reported by users? These questions are the basic needs of users.
  • Behavior: What are the user's reactions, actions, and data?
  • Cause: What are the reasons and data for user issues and behaviors? The reason is the user’s “real needs”
 user
  • Attributes: mainly include user gender, age, occupation, income, preferences and other data.
  • Scenario: When, where, and under what circumstances does the user generate demand and scenario data.
  • Frequency: The cycle and data of user needs.
The second step in demand extraction is demand ranking. Sort the needs according to the number, proportion and importance of user feedback. (Demand sorting can actually be understood as product decision-making. I will write another article later titled "Xunlei Kankan CEO: How to Make Product Decisions".) The book gives the example of video websites: the purpose ranking of users' video websites shows that 92.6% of users go there to watch movies, 87.2% of users go there to watch TV series, and only 29.6% of users go there to watch original UGC content. From this, it can be seen that the priority of video websites to provide more and richer movie and TV series content is obviously higher than encouraging UGC. Therefore, in subsequent product planning, what needs to be done more is to purchase the copyrights of TV series and movies, rather than thinking about how to better encourage UGC. The third step of demand analysis is user classification. Through the three dimensions of user description (user information, frequency and intensity of user needs), users can be classified into different levels (ordinary users, target users and fan users), thus identifying "fan users".
  • Ordinary users: users who theoretically have the need to use new products or new features.
  • Target users: user groups with clear user attributes, user scenarios and high usage frequency among ordinary users.
  • Fan users: loyal users among target users who frequently use the product and have the ability to spread the word.
 User classification is actually a further identification and definition of users. Analyzing users is a necessary part of demand analysis, as it can lay a solid foundation for subsequent product design and operation: In the product design phase, demands should be ranked according to different levels of users to make demand decisions: For example, if a product demand seems very niche, then generally speaking, it may not be considered, but if this niche is for fan users, then you need to think carefully about it, because meeting the needs of fan users is the core competitiveness of the product. In the operation stage, more targeted operation activities can be carried out according to user classification. The above content seems somewhat theoretical, and more needs to be verified in specific work. Let us encourage each other.

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