How to guide product optimization through data

The designer was born to solve the problem. How can we solve the problem better and more efficiently? First of all, we can find the problem, and the data analysis is a common method to find the problem. Through the data location problem, and then use the design scheme to try to solve the problem, and then use the quantified data indicators to assess whether the problem has been solved, how many solved. Through iterative optimization, the problem can be solved better.

This article unifies oneself in the login product experience optimization to accumulate some actual combat experience, the reappearance process design drip, has the effect obvious plan, also has the effect not obvious optimization attempt, finally will summarize some general design idea.

　　What is the reason for the login failure?

Login box is generally a simpler interface, it includes: Account name and login password, check code, other account login entry, registration and other related operations and other elements.

Seemingly a simple interface, every day there are many users try to log in but failed to log in. Some Web sites with large traffic may have millions of logon failures per day. Logging in as the user enters the first door of a product, the experience is critical. Therefore, we use the login success rate as the most important index to measure the quality of the logged in product experience.

After data collection and analysis, we found that the reasons for the user login error accounted for the following figure (in the image of the fuzzy processing): Login password error, account name error, check code error is the TOP3 factor affecting the success rate of login.

Detailed positioning problem, each

For the above top3 problem, we have optimized the iteration. This paper takes the optimization of check code error as an example to illustrate the optimization idea. We used the data to locate the big problem, then we need to locate the problem in detail. So why did the check code go wrong? What's the solution?

Through the page buried point click Data and background log we found that the user refresh picture check code concept is higher, the average user will refresh 2 times check code to enter the correct. So the problem can be summed up as follows: Check code recognition is low, easy to lose error.

What kind of solution should that be? Try this step-by-step approach: beforehand, in the matter, afterwards.

1 Beforehand: We take the input picture check code as an event, that "beforehand" means that does not appear the picture check code, or reduces the check code occurrence probability. The function of the picture checksum code is to prevent the machine from logging in bulk. Under the reverse thinking: if we can ensure the security of the system, through technical means as far as possible in advance to identify the person in the request login, or machine malicious login. Be sure to identify as a person in the case of login, there is no picture check code (as shown below), you can greatly reduce the normal user login when the concept of the input image check code.

After the on-line, through the tracking login success rate data, found this optimization effect is very obvious.

2: The next step is to find that the checksum code is the number and the letter at any time mashup, such as the number "0″ and the letter" O ", the number" 1″ and the letter "I" is easy to confuse.

Solution: Reduce the probability of confusing numbers and letters, and if the number "0″ or letter O" appears, the default user input is correct. If conditions permit, you can also filter out these confusing combinations to increase the user's first input success rate.

Another solution is the instant feedback result when the user enters the checksum: whether the input is correct or wrong.

Enter the correct prompts as follows:

Input error prompts as follows:

Through this optimization, users can know in advance that they have made a mistake without requiring a login request before they know that the error occurred, resulting in a login failure. So the user login is more efficient (shorter), and the success rate of the login is not a small increase.

3 after: The user lost the wrong picture check code, again input how to be more easily successful? In the following figure, the user clicks the Picture Check Code area refresh appears another check code, but relatively obscure, some users have made a mistake, do not know to click to change a check code picture.

Solution: Enhance the operation of the image check code to refresh the button, while increasing the voice check code. Although the Voice check code is designed for the blind user, but the normal user in the picture check code is always wrong, you can also use the Voice check code.

Finally, the general process to guide product optimization through data is summarized.

　　The first step: determine the quantitative indicators of the product experience

As in the case above, we use the success rate to measure the quality of the login product experience indicators. What metrics can be used to quantify a product's experience? This is the current interactive design in the field of discussion more topics, here first do not start elaboration, only provide reference suggestions: first, the user experience change has the vital influence to this index; second, this index is very good to quantify; third, to understand how the indicators are calculated, the project team reached a consensus, This can be more targeted to carry out the design.

　The second step: accurate location of data analysis

Find out where the product issues affecting the above measurements are, such as what happens to a login failure in the above case. Depending on the product, the data source can be the background log data of the product, the user clicks the behavior data or the user calls for help rate, and so on. Another step analysis: Why the password error, account name why error, picture check code why error. For example, we through a large number of users call Customer service recording can find the real reason for the user password error: the payment password and login password confusion, and other products of the secret confusion; password set too complex to remember, password error, retrieve password unsuccessful, and so on.

　　The third step: iterative optimization, with data test results

If you find a problem, you have to design a plan. The solution can refer to the above "beforehand", "in the matter" and "afterwards". Beforehand: Whether the problem can be avoided in advance, reduce the concept of occurrence, in the matter: The problem occurred, how to help users solve quickly, afterwards: how to avoid recurrence. Optimization program quickly online, to see whether the corresponding experience indicators have changed, how much has changed. It is often impossible to solve the problem one-step, the need for iterative optimization, continuous data tracking to revise the design strategy. Through the cumulative effect, to achieve our final design goals.