Data-aided design-practice in search

Source: Internet
Author: User
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Design can not be based on experience and intuition, because the target groups involved, the scene, the operation of the different habits. In order to obtain more accurate and effective information to assist and detect the design, the designer chooses the qualitative (user interview, focus Group) and quantitative (survey questionnaire, website data analysis) in the way of user research. The "Website Data analysis" This way does not need to spend a long time and human cost, while avoiding the user and the environment and other unstable factors of the analysis results caused by the interference. As long as we have accurate and applicable data, we should choose this method to assist design.

What data do you usually get?

1. Website data

The search for common data is as follows:

query– Search Key Words

PV (Page view)-pages view, every time the page is refreshed, it is computed once

UV (Unique Visitor)-Number of user visits

click-page Total number of clicks, each function will have a corresponding number of clicks

l->d-Search the list page to the detail page of the click Data, that is, conversion rate, different pages have different data.

CTR-CLICK/LPL,LPV is the search volume on the list page, CTR the number of clicks per browsing.

2, user interviews, qualitative research, focus groups

3. Reports of the findings

4, online testing (such as A/b test, search in the commonly used in-house development can be multiple solutions on-line test buckettest)

What information can be learned from the website data?

1, the key word loss rate analysis

Figure 1 is the user input "women's Shoes" related keywords and the corresponding keyword UV loss rate (that is, the search page does not have any action behavior of the number of users as a percentage of all search users), from the data to see the added leather, Guangzhou, fashion and other attributes of the key word loss rate is relatively low a lot.

The more detailed the keyword description, the more accurate the search match to the product, and the faster the user can find the target product. But it costs more to get users to enter keywords precisely (such as when users don't know which descriptors are more appropriate). How to reduce this cost? We can use suggestion (keyword recommendation) (see Figure 2) and the SN region (Class View property filter area) (see Figure 3) to give users the appropriate recommendation and guidance.


2, rapid screening after the revision of data analysis

Figure 4 is the filtered item on the search. The goal of the search should be faster and more accurate to help users find products, the filter area is one of the important components, so that users find a faster filter and simple to complete the screening operation, is the central purpose of each revision.

where each filter item should be placed is more appropriate, depending largely on which dimension of information the user is looking for in the product. For the functions that have been on line, we can analyze through the data, such as the CTR data of the filter area, we can find that the user uses the area, the ordering, the unit price, the operation mode to operate more, explained the user to this aspect screening demand is bigger, also concerns these several dimensions information, This can be adjusted to facilitate the user to find the location, but also reduce the user's memory burden, because users are generally from left to right browsing, so you can adjust the important screening to the first or with visual highlights. And some low data filtering, can be hidden or offline according to the situation, but also increase the expansion of the filter area.

Figure 5, based on the data in Figure 4, we have adjusted the location of the filtered items and the way the sort buttons interact.

This version of the adjustment on the line two weeks after we found that there are many changes in the data, green for the obvious data rise, red data drop, other data a small amount of increase, the user's attention to the filter items to the left of the position, found that the user's attention to the information CTR data significantly increased

To sum up, the design, iterative process is as follows:

Data validation, a relatively successful design this time.

3, the contrast function after the on-line data analysis

At the same time, we look at an unreasonable product design (see Figure 7). The figure is Alibaba's contrast function over the last year, the user tick the product to add the contrast (Figure 7 the 1th step) only occupies the entire search ctr0.6% about, but to the last clicks the contrast button (Figure 7 2nd) The conversion rate only then 10% not to have, the contrast function utilization rate is very low.

According to this discovery, we have gathered 5 testers (1 product managers, 1 operators, 3 users) for focus testing, get the following feedback 1, users just look at a few of the information, do not need so multidimensional information comparison. 2, the user is more accustomed to by the point open detail to compare.

Many vertical industry search has a comparative function, such as the Pacific, Zhongguancun, Taobao mobile phones, and so on, the function through the comparison of information to help users select more consistent with the target products. But not suitable for our website, but also need to consider more. Combined with the above data, the comparison function is more suitable for the vertical industry which needs to pay attention to the multi-dimensional information contrast, and the attention dimension is less, the user can reach through the short-term memory.

Using Data detection design

Generally before the full line will pass the Buckettest test each scheme, compares the user behavior data to detect which scheme is more reasonable, the experience is better.

Through the bucktest can be compared to figure 8 of the data, red for data decline, green for the data rise.

The application of Buckettest test is an effective method for detecting design, especially for small detail experience optimization. Data management in the program Adjustment department can monitor the corresponding data changes. For example, button adjustment, check whether the adjustment is better than the original scheme experience, before the test request development students in the monitoring data to the button, after testing can understand the button before and after the comparison data. If you want to know if the button position adjustment can enhance the experience, in the design should avoid other factors may cause impact such as button style, in order to continuously optimize the design scheme.


Analyzing data, reading data and using data can help us to design products and improve experience, which is a required course for designers to master.

The data is powerful but don't get obsessed with it, don't just focus on the ups and downs of the data, but analyze the real user reasons and requirements behind the data, which is just an analysis aid tool.

No design relies on testing and data detection, and the development and time costs required for each test are too high. Although designers can not make 100% of the correct judgment, but combined with effective work habits and summary analysis, still can greatly improve the accuracy of the design and the effectiveness of the solution, which is also the value of designers.

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