Henry Ford has a well-known saying, "If I ask people what they want in advance, they will definitely say faster horses". This phrase was later quoted by many people, including Steve Jobs, to argue that users often don't really know what they want, and that it's only when you make a brand new product that they realize "this is what I want".
To some extent, it is true that people living in the age of horse-drawn carriages do not rush to ask for cars, just as users in the age of feature phones do not think of smartphones. However, the lack of imagination about the specific form of the new product does not mean that users do not understand their specific needs. For example, the description of the "faster horse" clearly includes people's imagination and hope for a more convenient and faster means of transportation, although the "horse" itself is dislocated from the specific form of the later automobile.
In New Position's view, this case illustrates the importance of accurate consumer insights from less direct descriptions of information: "horse" is not a real need, "faster" is.
In fact, this capability is becoming more and more important for today's businesses. Because with the full popularization of the Internet on the C-side, everyone can express their views on a certain product in various online communities and exchange different user experiences. These comments and interactions form a bonanza of user needs, and the effective exploitation of this bonanza is the basis for enterprises to achieve "agile development" of products.
Nowadays, in practice, it is more common for product design and R&D teams to actively collect user feedback in the online community. If you happen to be a KOL, then not long after your post appears on Xiaohongshu or Weibo, there will be an official person from the company to connect with you. Sometimes it's because the employee happened to see the post, and sometimes it's because someone @ the official or sent a message.
However, an objective situation is that due to the emergence of various comprehensive or vertical platforms, it is difficult to be comprehensive, objective, timely and efficient in this way of relying on active search or passive response to discover user needs. Under the premise that the relevant technical conditions have matured, user insights based on UGC content on mainstream platforms urgently need to change from "labor-intensive" to "intelligence-intensive", and in this regard, a user research tool recently launched by Jiuqian Zhongtai may lead the industry trend.
01. New topics for research and development
In a fully competitive business environment, the essence of doing a good job lies in how to better meet the needs of users. This process can be roughly divided into two steps, one is to identify what the user's needs are, and the other is to design or improve products and services according to the user's preferences. One of the more difficult steps is step one, because once you have a clear enough understanding of the user's needs, making the product is more of a process of following the map.
A recent case in point is Li Auto. With the "range extension route, which is generally regarded as a backward technology", and the "refrigerator color TV sofa" that seems to have no hard threshold, the ideal sales have exceeded 40,000 units in the past two months, and it has beaten BBA in the luxury SUV market of more than 300,000 yuan. The ideal success is undoubtedly Li Xiang's success as a product manager, and it is the user perspective of the "second child daddy" that defeats the parameter perspective of the technical party.
In principle, user research is the process of gathering information and abstracting business insights from that information. Therefore, the validity and richness of the information, as well as the ability to understand the information in combination with the actual situation of the enterprise, constitute the elements of user research.
Traditional user research generally obtains information through interviews, surveys, etc., in which case the quality of information is highly restricted by the individual ability of the investigator, and if the selected sample group is not representative, it will eventually draw misleading conclusions and damage the business development. At the same time, it is difficult to strike a balance between the breadth and cost of the investigation due to the cumbersome and labor-intensive process of information collection.
The advent of the Internet has solved this problem to a certain extent, as many users will take the initiative to share their views and experiences on various platforms. The information generated by this spontaneous process is naturally more representative than questionnaires or interviews, and can better reflect the actual pain points of the product. In today's situation where the Internet has penetrated into everyone's daily life, this has become an indispensable position for all brand users to observe.
For businesses, the more discussions users post on the platform, the more it helps to convey rich and valuable information. But this brings a new problem: information overload. And the bigger the brand, the wider the product audience, the more serious the problem. In the era of mobile Internet, each company has its own app, go to each platform to search for keywords in turn, and then browse all related posts and comments below the posts, and further analyze whether these discussions are positive or negative, which is obviously beyond the scope of "human power".
The real challenge is that a lot of information is there, but it is scattered in fragmented form in various fragmented cyberspace, and the key is how to exert its value in an efficient way. This requires a complete technological overhaul.
02. "Mining" has to use algorithms
A viable alternative is algorithms.
The biggest difficulty with using algorithms to process user comments in the past was that all of them were unstructured text, often including all kinds of "noise" introduced by users when using natural language. Nowadays, with breakthroughs in natural language processing (NLP), the processing of unstructured text has matured.
The current algorithm system can complete the quantitative processing of text data very well, not only the basic keyword extraction, but also the identification of the emotional tendency of the text. Therefore, with the support of such a set of technical bases, from the collection of text information, to the semantic analysis and sentiment analysis of the collected information, to the generation of data-based consumer insights, can be completely completed through automation.
At the beginning of this article, we likened the massive amount of comments and interactions in the Internet community to a "bonanza" of user needs, and algorithms are a technological leap forward in mining this "bonanza". And to some extent, the technological leap we talk about here bears a strong resemblance to the recent shale oil and gas revolution in the energy sector.
It has long been known that shale oil and gas reserves are extremely abundant, accounting for 32% of the world's total recoverable natural gas reserves and 20% of the world's oil reserves. It's just that due to the special form of existence of resources, there has been no way to exploit and utilize them. However, thanks to technological breakthroughs in hydraulic fracturing and horizontal wells, shale oil and gas have recently become the dominant energy supply in some countries.
Similarly, a reliable judgment is that in the future, the monitoring and analysis of mainstream platforms by brands will also be fully transformed from "human mining" to "algorithm mining". For the field of user research, this could be a turning point in the paradigm of how the industry operates.
However, at present, the continuous monitoring and analysis of UGC content on platforms such as social networking and e-commerce, and then improving the quality of user research, is still an emerging trend in China, and it is probably still in the early adopter stage of the technology adoption life cycle theory. Relatively speaking, foreign countries are moving faster in this regard, and tools such as "Brandwatch" and "Sprout Social" are already more mature.
In China, Jiuqian Zhongtai has recently launched a similar user research tool, covering various mainstream platforms including social **, traditional e-commerce, and interest e-commerce. "New Position" actually tried out this user research tool provided by Jiuqian Zhongtai, and the effect was not bad.
This tool will crawl typical articles and comments in real time, and quickly conduct structured automatic analysis and opinion summarization. Each idea can be quickly traced back to the original text, which can not only help companies better understand users, but also can be used for rapid PR response. The keyword characteristics corresponding to each idea, as well as the positive and negative sentiment tendencies, are also clearly presented through the data.
For example, for the ideal car mentioned above, you can find that the "energy & battery life" and "power acceleration" that are more important in the impression of new energy vehicles are actually not very prominent, and the "quality control" and "patented technology" may also lack of accumulation because of the new forces in car manufacturing. And the best places to do the ideal are "appearance & design", "channel & service", "car machine configuration", "space" and other aspects, it is precisely these strengths that hit the pain points of middle-class dads.
If we want to learn more about consumer product preferences at a certain latitude, we can also generate a summary of opinions for a specific latitude. For example, in the latitude of "interior & space", consumers are obviously satisfied with the ideal large space and seat function, but they are somewhat dissatisfied with the materials used in leather and the quality of the audio. These specific feedbacks generated after algorithm analysis will play a good reference role in subsequent product updates.
03. Write at the end
The consumer Internet has brought brands closer to users than ever before, which will bring a layering effect to enterprises. Companies that are adept at capturing user feedback and making adjustments from this channel will gain a competitive advantage by being more agile and closer to the needs of their users, and will gradually move away from those who are sluggish and slow-moving.
From this point of view, it is not optional but necessary to use algorithms to replace manual continuous monitoring and analysis of UGC content on mainstream platforms to completely transform the process paradigm of user research.
But this is not surprising, as the same script has played out for every technological change in history.
The title picture and the accompanying pictures in the text** are on the Internet.