|Article Description: Google's application of this user-centric indicator system provides a very good reference for quantifying the user experience by providing a broadly adaptable dimension framework, as well as a process of creating specific metrics.
Thank @liuyaping for sharing and exchanging the metrics of the user Center in Ali's work, which is very enlightening. Google's application of this user-centric indicator system provides a very good reference for quantifying the user experience by providing a broadly adaptable dimension framework, as well as a process of creating specific metrics.
The following is based on Google's "measuring the User experience on a Large scale:user-centered Metrics for Web applications" a text of the main content and view If there is a translation of the situation, please refer to the original, forgive Kazakhstan:
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More and more products and services are deployed on the web, which presents a new challenge to the large-scale measurement of user experience. There is a strong need for a user-centric indicator system to measure the process of achieving key goals and to drive product decisions. In this briefing we can see the user-centric heart framework that Google is currently using, as well as the process of creating this metric that is mapped to the product target.
1 , the traditional website measure index pulse
- Page view/number of pages visited
- uptime/Continuous Running time
- Seven days Active USER/7 Active user number
Pulse is a product evaluation system based on business and technology that is widely used by many organizations and companies to follow the health of the product.
These metrics are important and closely related to the user experience, such as a product that is not attractive to users if it has frequent access outages (low uptime) or is very slow (hight latency). The purchase process of an E-commerce website is likely to be difficult to make money if too many steps are made. A product with a great user experience is more likely to grow in PV and user volume.
However, these indicators are either too shallow or not directly related to the user experience, and it is difficult to assess the impact of user interface changes on the user. And they are often embarrassed to interact with each other-for example, the PV rise of a particular feature page may be due to the fact that the feature is really popular, or because the user is confused by the interface and keeps clicking around in order to get away. Bring a short period of value improvement, it is possible to create a bad user experience, resulting in a longer period of user churn.
2 , user-centric indicators heart
Based on the problems existing in pulse, Google's user experience proposed a complementary metric framework: HEART.
- happiness/Pleasure Degree
- engagement/Participation Degree
- adoption/Acceptance Degree
- retention/Retention Rate
- Task success/Degree of completion
These five are only a measure of scope, and different products can thus define specific metrics to monitor progress towards the target. The sense of pleasure is measured by the satisfaction of the user, and the task completion is measured by the effect and efficiency of the task. Participation, acceptance, and retention rates are new categories that are generally developed through a wide range of behavioral data. It is often not appropriate to use all dimensions in an indicator setting, but you can refer to the framework to determine whether a dimension is included or excluded. For example, the user is going to use your product as part of the job, in this enterprise environment participation is meaningless. In this case, you can consider the choice of pleasure or task completion.
A sense of pleasure
The sense of pleasure is the subjective feeling in the design user experience, such as satisfaction, visual perception, willingness to recommend to others, and ease of use perception. You can design a questionnaire for a long time to monitor the same indicators to see the changes brought about by the design.
Participation is the depth of user involvement in a product, which is typically used as a combination of frequency, intensity, or depth of interaction over a period of time. For example, a single user's weekly visits, or the number of photos uploaded by the user each day, are better than the total--because the total increase may be generated by more users, rather than by more use.
Degree of acceptance and retention rate
The acceptance and retention metrics provide strong insights into the differences between new and old users through a large number of user statistics (e.g., 7 days of active users) over a specific period of time. Acceptance monitoring How many new users are starting to use the product for a specific period of time (for example, a newly created account in the last 7 days, and the retention rate monitors how many users will remain in the next period of time for a specific period (for example, 7 days of active users in a particular week are still active for 7 days after 3 months).
Task Completion Rate
The task completion rate dimension includes some traditional user experience behavioral metrics, such as efficiency (such as time to complete a task), effects (such as the percentage of task completion), and error rates.
Regardless of the definition of the User Center metrics, if it is not accurate and target-related, and can track the process to achieve the goal, then it is in vain. Google provides a simple process for setting targets, by clarifying the objectives of the product or function, and then by defining the signals that are reached, and ultimately establishing the monitoring of the specific metrics.
The first step is to define what the goal of the product or function is, especially in terms of the user experience. What tasks do users need to accomplish? Redesign is trying to reach what? Use the heart framework to prompt for related goals (for example, is it more important to attract new users, or is it important to encourage existing users to participate more actively?) Some useful tips:
- Different team members may have different opinions about the objectives of the project. This process provides a good opportunity to gather different ideas and try to reach consensus (and buy-in selected indicators)
- The success of a particular project or function may be different from the overall goal of the product
- At this stage, there is no need to worry too much about whether and how to find relevant signals or indicators
Next, think about how the user's behavior or attitude reflects success or failure. What action will indicate that the goal has been achieved? What feelings or perceptions can be linked to success or failure? At this stage you should think about what the data source of your signals might be? For example, based on the behavioral signals of the log, are these related behaviors currently recorded or can be recorded? Can collect the attitude signal--can you put the questionnaire on a regular basis? Logs and questionnaires are the two sources we use most often, but there are other possibilities (e.g., using a panel or judging the user to score). Some useful tips:
- Choose sensitive and target-specific signals-they should not change because they don't want to, unless the user experience gets better or worse.
- Sometimes failure is easier to define than success (e.g., give up a task, undo, Poke)
Finally, consider whether these signals can be converted to specific metrics and whether they can be easily tracked continuously. Some useful tips:
- The raw statistics will grow along with your user base growth and need to be converted to normal; the ratio, percentage, or average of each user is more useful.
- There are many challenges in ensuring accuracy, based on Web log metrics such as filtering traffic from auto-generated data (such as reptiles, spam), and ensuring that all important user behaviors are logged (by default, especially in AJAX or flash based applications)
- If you need to compare your projects or products with others, you may need to add monitoring metrics to the standard metrics for these products.
4 , Summary
Google has spent years addressing the widely used metrics system for user experience. The heart framework and target-signal-target process has been applied in more than 20 Google products and projects. Whether it's a data-driven or user-centric product, the Haert framework and the target-signal-metric process can help the product team make decisions.