User Research: User Experience Measurement and Metrics
Introduction
All things and phenomena are measurable. Usually we think it's not measurable, just haven't found the right way.
1. Why measure user experience?
In our daily work, user experience designers are often challenged. Frequently asked questions:
- How to prove that this functional optimization is better than the original one?
- How much impact does this optimization have on the market data?
- How much did your experience optimization contribute to the company? How much economic value will it bring?
- ...
"Experience" seems to be a subjective feeling of users, and its concept is relatively abstract. From this situation, it is difficult to measure.
But as an experience designer, we need to be clear: we need to highlight the value of design in the project, which requires a complete set of scientific, systematic, and quantifiable user experience measurement methods.
Data metics are the digital expression of the abstract concept of "value", which can form a specific focus for all participating roles, establish a unified coordinate system and judgment criteria, and intuitively reflect the relationship between the program effect and the target value, and become the source of thinking for subsequent iteration.
The setting of data indicators should follow the derivation idea of "VSM". The so-called VSM refers to from Value to Signal to Metric. The specific meaning is that once the design value is realized (in fact, the design goal is realized), there will definitely be a corresponding phenomenon or signal, and with phenomena or signals, according to the above concepts, we should be able to find data Metrics that can express and measure them, and reflect changes in design values.
Example: Suppose we want to design a bed that improves sleep quality.
- [Goal] Define business goals and gather design goals - improve users' sleep quality by designing a bed;
- [Signal] set key design goals - easier to fall asleep, less turnover before falling asleep, and recommend to others;
- [Metric] Identify measurable data metrics - the time from sleep start to sleep, the number of turnover before falling asleep, what percentage of all buyers are recommended by others.
GSM model from design goal to data metrics
From the examples, it is not difficult for us to find out how to reflect the value of our designed bed? It must be through measurable data metrics to reflect value, rather than subjective feelings.
2. Dimensions of user experience measurement
On the basis of the HEART model, combined with daily work, we can measure the user experience through the following three major dimensions.
HEART model: It is a user experience measurement framework proposed by Google based on the "PULSE" evaluation system, combined with the quality of user experience and the need to make data more meaningful. It includes five, namely: Happiness, Engagement, Adoption, Retention, Task success.
On the basis of the HEART model, combined with daily work, we can measure the user experience through the following three major dimensions.
1. User feeling
Net Promoter Score (NPS): An index that measures the likelihood that a customer will recommend a business or service to others. The purpose is to understand the willingness of users to actively recommend the brand or product.
0-10 points, 10 points means very willing, 0 points means very unwilling, according to the user's recommendation willingness, users are divided into three categories: recommenders, passives, and detractors.
Recommenders (9-10): Die-hard fans who not only patronize themselves, but urge their friends to do the same.
Passive (7-8 points): Satisfied but unenthusiastic customers who can be easily wooed by competitors.
Detractors (0-6 points): dissatisfied customers who are trapped in a bad relationship for some reason.
Promoters and detractors are users who have an impact on the company's actual product reputation. The difference between the percentages of these two users in the total number of users is the Net Promoter Score (NPS).
Calculation formula: NPS=(Number of recommenders/Number of total samples)x100% -(Number of detractors/Number of total samples)x100%
Satisfaction (CSAT): refers to the subjective feelings and satisfaction of users after completing the operation. The main user experience data metrics include (but not limited to) ease of operation, rationality of layout, interface aesthetics, and ease of reading and other subjective evaluations.
The customer satisfaction index (CSAT) is a measure of customer satisfaction with business, purchase or interaction. It can be obtained by a simple question, such as "how satisfied are you with your experience?".
The CSAT requires users to rate their satisfaction with a specific event/experience, generally using a five-point scale, including: very satisfied with 5 points, satisfied with 4 points, general with3 points, dissatisfied with 2 points, and very dissatisfied with 1 point.
The CSAT value is obtained by calculating the proportion of users who choose 4 and 5 points.
Calculation formula: CSAT= (number of satisfied customers/total number of samples) x 100% (number of satisfied customers refers to the total number of users with 4 and 5 points)
Loyalty: Refers to whether the user will use the product again after one use. The main user experience data metrics include (but are not limited to) the 30-day/7-day retention rate, the usage overlap rate of different platforms, etc.
Customer retention rate (CRR) can also be used to reflect user loyalty, that is, the proportion of enterprises that continue to maintain trading relationships with old customers.
Calculation formula: CRR =((E-N)/S))X 100%
- S: The customer at the beginning of the period
- E: Customers at the end of the period
- N: New customers acquired during this time period
Pleasure of product use: refers to the degree to which user needs are satisfied: Satisfied = pleasure = stay, dissatisfied = unhappy = loss.
Users who are satisfied and stay can be reflected by the above loyalty. Users who are not satisfied and lost need to calculate the user churn rate.
Calculation method: user churn rate = (number of lost users / total number of users) × 100%
2. User behavior
3. Performance of hardware system
It refers to the performance indicators displayed by the hardware system when the user performs corresponding processing during the operation. It can be measured by the loading speed of the product, the clarity/fluency of the page, the synchronization of multi-port information, the crash rate, etc.
3. Application of data
According to the dimension of user experience measurement, we can effectively measure the value of our user experience and obtain the corresponding data. So with these data, how can we more effectively serve the improvement of user experience?
1. Horizontal comparison
2. Longitudinal comparison
- Base ratio: Compared with the level of a fixed period, it indicates the general development trend in a longer period of time.
- Chain ratio: Compared with the overall level of the previous cycle, it shows the development trend from period to period, mainly including week-to-week and month-to-month.
- Year on year: compared with the same period of last week / last month / last year, it shows the development trend, mainly including week on week, month on year and year on year.
3. Type comparison
4. Viewing data from the perspective of users
Design according to the needs of users. Different users have different needs and operation behavior, so different user groups have different data performance. From the data differences of different user groups, we can understand the needs and operation behavior of different users.
Users can be segmented from identity attributes and behavioral attributes. Common identity attributes include gender, age, province, industry, membership, etc.; common behavior attributes include new/old users, whether there is a transaction, return visit frequency, user source, membership level, etc.
Conclusion: Being good at measuring user experience with data can better guide our daily design work, reflect the value of design, and find problems before design. Through the horizontal comparison, Longitudinal comparison and type comparison of data, we can understand user demands from the perspective of data, discover the existing problems of products, and provide inspiration and breakthroughs for product design.
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