Allow users to study the wings of data insertion

Whether it is product managers, designers, engineers, we are all for the user service. User tastes. You like this, I like that, and we all like it. So what kind of secret is hidden in the user's psychology? There are usually two ways to discover the mysteries: qualitative research, quantitative analysis. Qualitative information tells you why it happens, it is flexible, fast, and rich in detail, but lacks universality, we can only hear the voice of a small number of users, whether they represent the majority of users is impossible to judge. Another way is to let the data speak, and the quantitative information tells you what happened, it's real and accurate. In other words, user research does not always have to be done using the "qualitative research" approach. The use of data analysis can also achieve understanding of user preferences.

The role of "data analysis" in "User research"

How does "data analysis" work for "user research"?

(1) To understand the user profile

Understand the target user "background information": Through the data statistics target user "demographic" information, such as age composition, sex ratio, etc. (below) to achieve the target user background mapping effect

(2) distinguish between user groups

According to a variety of dimensions, found that users of different characteristics, the same characteristics of the user classification, and then accurately formed a user group, for further user analysis work on this basis, for product optimization design work to indicate the direction of the user group (pictured below)

(3) Analysis of User preferences

To research products as the core, according to a variety of dimensions statistics "frequency", "content ratio, so as to tap the target users of various" preferences ", so that" product optimization design "to meet the needs of users, targeted: The following figure I, product use location ranking, mining user preferences for the location; Figure two, product category ranking, Mining user preferences for product classification

The "Data analysis" method in user research

Collect user Data-> develop coding classification-> data Analysis (user feature extraction)-> determine the optimization direction-> improve the business return, the following is a brief introduction

(1) Coding classification

Extract data from the last few months, establish coding rules based on the analyzed product targets, and execute coding until no new encoding is generated. Coding can be on any dimension, as long as it helps with subsequent analysis

(2) Data analysis (user feature extraction)

After the establishment of the code, around the research "target product" user characteristics of the center, according to a variety of useful dimensions for data statistics, through the data analysis results, analysis and extraction of "user characteristics"

(3) to determine the direction of optimization

In the analysis of a number of "user characteristics", according to business objectives and user experience to win the principle of mutual benefit, to find the direction of product optimization design

Iii. for "data analysis" put on a beautiful coat

(1) Data description "graphical", so that the results of analysis easier to understand

Add "graphical data description" to the statistical chart, can communicate the conclusion more directly and quickly, it is easier for the reader to understand, such as the two graphs below, give the horizontal axis "gender", "age", "peak reason" to increase the image of the graphic description

(2) Data analysis chart, to be able to intuitive response to the conclusion

In a statistical chart, when there are differences in the different categories of ratios or frequencies, the size of the figure itself and the feedback of the proportion of the frequency is directly proportional to the reader to watch the analysis report, at a glance, quickly understand the meaning of the chart, such as the bottom of the graph, "Yes class" accounted for more so "graphic area" large; "no category" accounted for less , so the "graphic area" is small

Finally, "data analysis" needs to be combined with "qualitative research" in order to find the rules and trace the roots, more efficient guidance of design and products.

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