What are the common metrics for app data analysis?

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1. Monitoring of conventional data indicators is a cinch. such as user volume, new user volume, UGC (social product), sales volume, payment amount, various data during promotion, etc. These are the most basic and fundamental, but also bosses are the most concerned about indicators. When you took over the job, the first task was to comb the data.

2. Channel analysis, or traffic analysis. For an app in the ascendant, you'll spend resources to channel traffic and pull users into other channels. At this time need to monitor the quality of each channel, which effect is good, which unit price is cheap, this is the need for channel data monitoring to complete. Of course, you also need to monitor the follow-up performance of different channel users, to the users of each channel scoring, let boss know which channel is worth casting, which channel is rubbish. It also monitors the quality differences between iphone and Android users, which generally have a slightly higher quality of iphone users than Android users. Of course, if you have extra energy, you can also monitor the performance differences between different models. In short, it is in different dimensions to monitor the performance of different users.

3. The user's core conversion rate. Think about what the core function of your app is, and then monitor the conversion rate of this core function. In the game app may call the pay rate, in the electric quotient app may call the purchase rate. Different industries have corresponding conversion rates, you can compare their products and industry averages to see their products in the industry position. At the same time, through long-term monitoring, you can also have this data to judge the quality of different versions of the app.

4. Monitoring of the user's use of time. On the one hand, this is a very good indicator for monitoring user activity. A user's use of a long time means that the activity is high and vice versa. On the other hand, think about how your app was designed to predict how much time a normal user would spend each day, and whether the user actually spent the same time as you expected. If there is a big deviation, it means that the user's perception of the app is different from what you thought it would be. This time you need to think about how to adjust your product to meet the user's awareness. (Here is a digression, personally think that the product to make changes must be to find ways to cater to users, rather than find ways to change users to adapt to products. Here, the microblogging as an example, the user has been a micro-blog as a media products, an information exchange tool. And Weibo has been trying to make it into a comprehensive social platform, the introduction of micro-blog members, user referrals, various letters review rules, and so on, backstage fact that all of this has not changed the user's awareness of micro-blogging, micro-Bo did everything is ineffective. So when you worry about why users do not follow my vision to use products, must think how I can be changed to meet the needs of users, rather than to think how I can be changed to allow users to recognize the design of the product? ）

5. User Churn. On the one hand need to monitor the loss rate of users, such as new users come in, 第一、三、七、三 ten days are still using the product how many people. Loss rate changes can be intuitive response to the development of the app again in a good direction or bad direction. There are also some average levels in the industry, you can refer to these indicators to judge the quality of your app. On the other hand need to find the user lost place, to see where the user lost, and then targeted, make the corresponding changes. If you have the ability, modeling will be the loss of the user's various situations are depicted, so in the subsequent changes in the product will be more comfortable.

6. Active user Dynamics. Keep an eye on the dynamic of the app's active users and listen to their voices. Once the anomaly is discovered, organize people to discuss countermeasures. Active users (or core users) are the most valuable resources of the app, paying attention to their every move, and this importance doesn't need to be said much.

7. User Profile description. This has little to do with indicators, a bit of modeling meaning. Describe the user's individual indicator features, the more detailed the better. such as sex, age, geography, cell phone model, network model, career income, hobbies, etc. This data is usually useless, but for the product staff, sometimes it gives them great inspiration. If possible, can also be divided into the following dimensions: such as the characteristics of active users, the characteristics of the more silent users, the loss of the characteristics of the user.

8. User lifecycle Monitoring. This is specifically for those social, game-like apps. When your app is online for a period of time (6-12 months), you can look back at a normal user and complete the experience of how your app is going, and how long it will take. By combining some other data with this data, you can roughly estimate how large your product will be, and let your boss know what the product will eventually develop. Of course this is difficult, the development of the product is affected by too many factors, rely on you a data analyst to predict obviously is not so reliable.