Read the data indicators and data analysis models that designers should understand.
Why should designers understand data?
The current market situation is not optimistic. With the decline of dividends (population and flow), the cold winter of capital and serious product homogenization, the market puts forward higher requirements for designers. In addition to the design execution at the aesthetic level, designers need to have data vision, start from the experience side and business side, take data as the goal orientation, lean design and achieve growth. At the same time, with the wide spread of data methodology, it is necessary for designers to apply scientific data model to practical work.
Common data indicators
1. What are data indicators
Not all data are called indicators. Indicators must have reference value for business. Data indicators are a series of statistical data obtained directly or calculated indirectly by means of collection according to business needs. Data indicators run through the whole design process, explain user behavior and business changes, provide basis for design and verify the results.
2. Common data indicators
There are many data indicators, which are classified in this paper for easy understanding.
We divide common data indicators into three categories:
- Comprehensive indicators: reflect the overall situation of the product
- Process indicators: reflect the user's use behavior
- Operational indicators: reflect specific business conditions
Active users:
Users who have successfully started or operated the core functions of the product in a specific statistical cycle (de duplication according to the equipment). According to different statistical periods, it can be divided into daily active (dau), weekly active (Wau) and monthly active (MAU). It directly reflects the user scale of the product and is a very important indicator. Different products correspond to different use frequencies. For social products and information products, such as wechat and today's headlines, the KPI assessment index generally includes the number of daily active users. However, for some apps with low-frequency demand and temporary demand, such as travel tools and products, more attention is paid to the monthly active number.
New users:
Users who successfully start the product for the first time after installing the application. According to different statistical cycles, it is divided into daily new addition (dnu), weekly new addition (WNU) and monthly new addition (MNU). New users are de duplicated according to the device dimension. If the device uninstalls the application, reinstalls the application after a period of time, and the device is not reset and opens the application again, it will not be counted as a new user. The number of new users represents the potential of the company, which is particularly important in the initial stage of the company. The index of new users is mainly the most basic index to measure the effect of marketing promotion channels.
Retention rate:
Users who use the product for a certain period of time and continue to use it after a certain period of time are called retained users. Retention rate = users still in use / total number of users at the beginning. Whether the user can stay after coming reflects the user's satisfaction with the product. Usually focus on the next day, 3 days, 7 days and 30 days, and observe the attenuation degree of retention rate.
Next day retention rate: the proportion of new users who successfully start the application again the next day.
7-day (week) retention rate: the proportion of new users who successfully start the application again on the 7th day. During this period, users usually experience a complete product experience cycle. If users can stay and continue to use the product at this stage, they are likely to become loyal users of the product;
30 day (month) retention rate >: the proportion of new users who successfully start the application again on the 30th day. Generally, the iteration cycle of mobile end products is 2-4 weeks, so the monthly retention rate can reflect the retention of users of a version. The update of a version will more or less affect the experience of some users. Therefore, by comparing the monthly retention rate, we can judge the impact area of each version on users, so as to locate similar problems for optimization.
According to the 4:2:1 theory proposed by Facebook, the retention on the next day can reach 40%, 20% on the 7th and 10% on the 30th, which is a very good retention index. In today's Internet industry, retention is a more important indicator than new and active. Because the demographic dividend of the mobile terminal is gone, the cost of obtaining customers is higher and higher, and the competition is more and more fierce. How to retain users is more important than obtaining users.
Service time per capita:
In a specific statistical time period, when browsing a page or using the whole product, the total time users stay is divided by the number of visitors to the page or the whole product.
This data is one of the important indicators to analyze user stickiness, and can also reflect the user experience of the website. The shorter the average access time, the less attractive the product is to users. For example imperceptibly cannot do without the strong user stickiness, tiktok will spend a lot of time without knowing it. The business logic here is that the longer the user stays, the greater the possibility of business transformation. Of course, it is not applicable to tool products that are ready to use and go. On the contrary, if users stay on a single page for a long time, it is likely that the page function is vague and the meaning is unclear. Users spend a long time to understand how to use it, which proves that the efficiency of information transmission is low.
GMV (Gross Merchandise Volume)：
The total transaction amount refers to the amount of the order taken, including the paid, unpaid, cancelled and returned parts, that is, once the order number is generated, it will be included in Gmv. The actual orders may be fully paid, so Gmv must be greater than the actual sales. Therefore, we often see reports that Gmv is used to show the scale of the platform.
Per capita passenger unit price > (ARPU):
Average revenue per user. ARPU = total revenue Gmv / payment UV.
The number of users can be the total average number of online users, paid users or active users. Different product standards may vary. ARPU can be further subdivided. When there are too many ordinary users, the number of paying households is often taken as the denominator to calculate ARPU. There is no absolute difference between the level of ARPU and the level of ARPU. There needs to be a certain standard when analyzing. High customer unit price industries, such as some luxury industries, are very concerned about this indicator. Although the number of paying users is small, the individual consumption ability is very strong.
PV (page views):
Each user's access to the page > face is recorded once, and the number of visits is accumulated. Theoretically, PV is directly proportional to the number of visitors, but it cannot accurately point to the real number of visits to the page. For example, a very high PV can be produced by constantly refreshing the page at the same IP address.
UV (number of unique visitors):
An IP address to visit the website is a visitor. In a fixed period of time, the same client is calculated only once. Using the number of independent users as the measurement, you can more accurately understand how many visitors actually came to the corresponding page in a certain period of time.
Conversion rate:
The ratio of the number of completed conversion actions to the total number of clicks / exposures in a statistical cycle.
Conversion rate = (conversion times / hits) × 100%。 Take the user login behavior as an example. If there are 10 login websites in every 100 visits, the login conversion rate of this website is 10%, and the last two users pay attention to the goods, the conversion rate is 2%. If one user generates an order and pays, the payment conversion rate is 1%. Conversion rate is one of the important indicators of product profitability, which directly reflects the profitability of products. Different industries pay different attention to the conversion rate. For example, for e-commerce products, we should pay attention to the sales conversion to see how many users participate in the activity pay after the activity. If necessary, we can also analyze the per capita purchase times and purchase amount according to the data. For another example, when we monitor the registration volume, we should pay attention to the registration conversion rate and see how many new users this activity has brought to the product. Therefore, the conversion rate can analyze the shortcomings of the product in which aspects, and can quickly locate the problem point.
Here's the solution:
Loss rate:
Users who have used the product and no longer use the product for various reasons. It can be understood as the opposite of retention rate. High turnover rate means low retention rate. The loss rate of the next day, the 7th day and the 30th day also needs attention.
The definition of lost users varies according to product types. For products with high use frequency, such as social products, users should open them many times a day. If users of such products have not logged in for more than one month, we can think that users may have lost. In extreme cases, such as wedding products, the opening frequency of users is quite low, so not every product has a fixed loss period, but judged according to the product attributes. Designers and product managers need to find the abnormal data lost, locate the reasons for the loss of users, and fix the problems in the product in the next version. You can even locate the lost specific user ID and follow up through the personal information registered by the user at that time.
Jump rate Br (bounce bate):
The proportion of users who leave the landing page without going to > operation. It is a key indicator to evaluate whether the landing page is attractive to users. The reason for the high jump out rate may be that the product / activity itself is not attractive enough, or such users themselves are not the target group of the product.
Exit rate Er (exit rate):
It refers to the percentage of the "last page" of the session in the website.
Exit rate = exit times of current page / total session visits * 100%
The exit rate reflects the attraction of the website to users. If the exit percentage is very high, it means that users leave after browsing only a small number of pages. Therefore, it is necessary to improve the content of the website to attract users and solve users' content demands.
Access Depth
As the name suggests, the depth of user access to the product (the degree of completion of the product process)
Function utilization
In addition to paying attention to active users, we should also pay attention to the important functions of the product. Such as collection, likes, comments, etc. these functions are related to the development of products and the depth of user use. Function utilization rate is also a very broad concept. For example, when a user browses an article, how many users comment and how many users like it can be expressed by two indicators: praise rate and comment rate. Another example is the video website. The core function utilization rate is the video playback volume and video playback duration.
Number of starts
That is, count the number of times the user opens the application in the time period. Focus on the startup times per capita, which can be analyzed in combination with the use time. When the user actively closes the application or enters the background for more than 30s, and then returns or opens the application, it is counted as > two starts. The number of starts mainly depends on the frequency distribution.
Service duration
Count the total time length of a device from starting the application to ending the use within the time period. Generally, it is analyzed according to the per capita use time, average use time and single use time to measure the stickiness of user's product landing, which is also the reference basis for measuring activity and product quality.
Use interval
The time difference between the last time the user used the app and the time he used it again. Use the frequency distribution to observe the stickiness of the application to users and the depth of operation content. Although it is the use interval, the use interval statistics are completed by calculating the time difference between two successive starts of the same equipment, and the characteristics of application periodicity and fragmented use are fully considered.
Rate
Proportion of willing paying users in all users. Users in the video industry, e-commerce industry and other industries are more willing to pay, while some tool > apps are embarrassed. They are unable to find the charging mode, or if users of the existing charging mode do not buy it, the natural payment rate is very low, such as ink weather and master key.
Repurchase rate
If you say the repurchase rate as the retention rate of revenue, you will know how important it is. Like new users, the cost of acquiring a new paying user is already higher than the cost of maintaining regular customers. In many analysis scenarios, the first single user will be taken out as a label, and users who consume more than twice will be regarded as regular customers. The user's first consumption may be to experience the product, and the first payment may be promoted by preferential policies. The difficulty of the second payment will be greatly improved, and the transaction rate of the second payment will also drop precipitously (compared with that retained the next day). Non first order payment means that users have real recognition and strong trust in the product.
Return rate
The return rate is a risk indicator. The lower the return rate, the better. It not only directly reflects the financial level, but also relates to the user experience and the maintenance of user relations.
Data acquisition method
There are three main ways to obtain data:
- Second hand data: research results of others
- Questionnaire survey data: collect "what users say"
- Application buried point data: see what users "do"
1. Secondary data:
Target information:
Industry data and competitive product data. Including: business transaction data, user group's attitude and willingness, user public opinion index, user scale and profitability of competitive products, etc. Market data, the project approval period has certain guiding significance for product direction, understanding the market and differential positioning.
Access means:
Baidu Index, Penguin Zhiku, iResearch, Nielsen, major technology information platforms
2. Questionnaire survey data: collect "what users say"
Definition: issue questionnaires to target users and collect, sort out and analyze data
Information focus: historical behavior, subjective attitude or evaluation described by the user
Examples: > user satisfaction query, investigation of reasons for losing users, etc
Essence: establish assumptions - use the statistical method of sampling survey - get the answers described by users
Common indicators: recommended net value of NPs (Net Promoter Score).
Use a simple question to measure customer loyalty to corporate brands / products. "0-10 points, how willing would you be to recommend our products or services to your relatives and friends?", 0-6 points are called detractors, 7-8 points are called neutrals, and 9-10 points are called recommenders. The proportion of recommenders minus the proportion of your detractors is the NPs of an enterprise. From this value, we can see which of the enterprise's customers has more recommenders and critics. A positive score indicates that the majority of customers are willing to continue to buy, add to buy or do word-of-mouth, that is, the so-called loyal customers, so the enterprise will have positive growth, and vice versa.
3. Application buried point data: see what the user "does"
Definition: Embed Code in the product, set trigger conditions, and record logs to obtain user behavior data when the conditions are met.
In practice, the setting of trigger conditions is very important. It is necessary to define data indicators and communicate with the development in advance.
Classification:
Exposure buried point: > capture the number of times the page is displayed, which can be for the whole page or an area in the page. Such as PV and UV.
Operation embedding point: when the user performs gesture operation on an area of the page, it will record it. Corresponding, also known as PV and UV of an operation.
Time length buried point: it is obtained by marking the above two types of buried points and calculating the time difference. For example, the calculation of the page dwell time can be obtained by the time T1 of leaving the page - the time T2 of entering the page. Definition of leaving: click the upper left corner of the page to return or click the specific module of the page to jump to the secondary page.
Based on the above three kinds of raw data, it can be calculated:
Click through rate, function penetration rate, per capita click times, per capita use time and other data with comparative value.
Compared with second-hand data and questionnaire survey, the selling point data is more consistent with the real performance of users, with high sensitivity, strong mining and objective measurement indicators to help iteration.
Data analysis method
1. Behavior event analysis:
Study the impact of a behavior event on the enterprise and the degree of impact. Enterprises use this to track or record user behavior or business processes, such as user registration, browsing the product details page, successful investment, withdrawal, etc. by studying all factors associated with the event, they can explore the causes and interactive effects behind the user behavior event. Pay attention to different event indicators according to the actual work situation.
2. Funnel analysis:
It is a set of process analysis, which can scientifically reflect the user behavior state and the user conversion rate from the starting point to the end point. The general user shopping path includes five stages: activating app, registering account, entering the live broadcast room, interactive behavior and gift spending. The funnel can show the conversion rate of each stage. Through the comparison of relevant data in each link of the funnel, the problem can be found and explained intuitively, so as to find the optimization direction.
Value:
Monitor the transformation of users at all levels and the most effective transformation path in the whole process; At the same time, find the short board that can be optimized to improve the user experience;
Multi dimensional segmentation and presentation of user transformation, forming a single bottleneck with nowhere to hide;
Compare the user groups with different attributes, and peep into the optimization ideas from the perspective of differences.
Note:
The loss between steps is inevitable. The more steps, the more loss. Reducing the number of steps is helpful to reduce the loss rate at the funnel > analysis level. However, according to the principle of complexity conservation, the increase of complexity in each step will lead to the decline of user experience. Therefore, a balance needs to be found between the number of steps of the path and the complexity of the page. The effect is measured by the final conversion rate (completion rate).
3. Retention analysis:
It is an analysis model used to analyze user participation / activity, and investigate how many users who conduct initial behavior will conduct subsequent behavior. This is an important method to measure the value of product > to users.
It can reflect the overall retention of the product or the retention of a functional module.
4. Comparative analysis:
Pay attention to the control variables. If the daily life of the product suddenly increases significantly over a period of time, there may be many reasons: revision, hot event related, operation promotion, etc. the impact of a specific condition on the results can be measured only when other conditions are consistent.
Data analysis model
Heart model:
Participation refers to passive user behavior, including user activity and exposure to UV, PV, etc. It reflects the willingness to use the product.
Acceptance, focusing on active forms of user behavior, such as the use of a function.
Retention degree: for a period of time, continuously active users can be regarded as retained users or loyal users. These users are the key factor to promote product profitability.
Task completion degree and completion of core functional links.
Pleasure is the sum of users' subjective emotions when using the product. Regular body is now in user evaluation.
More analysis of this model:
Aarrr model:
Pull new, understand and locate the target user population, and drain and absorb them to their own products as much as possible. Landing page is the key point of design, which needs to clearly convey the core value of the product and attract users in a short time.
After activating and absorbing new users, it is necessary to guide users to use the product again within a period of time. The two key indicators of activity are the average use time and the average number of starts per day.
Improving the retention rate and whether the product can truly retain users reflect the stickiness of users to the product.
Realize and obtain revenue from users through some means.
Communication, encourage users to share products with friends by improving user experience or fission reward.
There are four ways to improve activity and retention:
1. effective touch up, wake-up call: it means touching users through mobile phone PUSH, SMS and WeChat official account, and waking up sleeping to activate APP. It is one of the most effective ways to improve retention. For example, the recall of old game users via SMS and the recall of old e-commerce users must have costs. Therefore, it is necessary to analyze and determine the users with the highest recall rate according to the user's past behavior (for example, the RFM model is determined as the core user)
2. Build an incentive system to retain users: a good incentive system can make the platform develop healthily and continuously, make users sticky to the platform, and is very effective in improving retention. > The commonly used incentive methods include growth value member system, sign in system and integral task system.
3. Enrich the content and increase the online time of users: the game products do very well. Various play activities themselves attract users to invest time cost. The game continues to strengthen social attributes and increase users' viscosity and cost investment.
4. Push back the data and find your key points: for example, if you comment more than 3 times, users will remain and it is difficult to lose. For example, some game products >, once players cross a certain level, it is difficult to lose. These are the key nodes you need to find through data analysis.
Rarra model:
Different from aarrr model, rarra model focuses more on the product itself. Don't worry about getting customers. First do a good job in the product, create a good reputation, ensure that users have a good use experience and let users voluntarily spread the product. Department > sub products create a sense of circle through invitation codes. Compared with the aarrr model that focuses on innovation, rarra model is more robust and conservative, with less investment on the operation side.
Rarra model emphasizes user operation and drives secondary purchase, cross sales and new customers with fine operation.
Summary
Role of data
- Monitor products and find problems: monitor product status in real time through data buried point monitoring, so as to provide direction and reference for revision.
- Validation Design: measure the effect of revision by qualitative and quantitative means.
- Explore opportunities and boost growth: find new business opportunities and product explosion points, and focus on user and data growth.
Become a designer who solves product problems
With the development of the industry, designers need to have higher and higher literacy. Simple design execution can no longer meet the needs of daily work. More and more posts require designers to have data thinking. In addition to ensuring aesthetic online, designers should also understand the source and verification of design and make the design more rational.
Thanks for reading.
