What basic data and core indicators need to be collected to analyze product conversion retention?

Each product has its own core function, and therefore has corresponding indicators, and there is only one.

At the same time, the core indicators will be divided into several secondary indicators.

Taking WeChat reading count as an example, if the core indicator of a public account article is reading count, the corresponding secondary indicators and influencing factors will be:

By further subdividing the main influencing factors, the direction of optimization becomes clear: continuously optimize or eliminate these influencing factors, and ultimately achieve the goal of increasing the number of readings.

The data analysis of the entire product is similar to the above ideas, but the product structure is more complicated and there are more influencing factors.

When the core function is used, the page where the core function is located will be exposed first, and then there will be path conversion on the core page.

The calculation for a larger granularity is:

Path conversion rate = number of users browsing the core page / number of active users on the day

In many cases, core pages have different conversion paths. Different paths and corresponding conversion rates are distinguished:

Path 1: Page A browsing users > Number of users who clicked button 1 > Core page

Path 2: Page B browsing users > Number of users who clicked button 2 > Core page

This helps find the path to the optimal conversion rate.

Core page path conversion rate

Now many apps put the core page on the first screen, so the core page path conversion rate = 1. The next thing to consider is the "function conversion rate"

After maximizing the exposure of the core page, you need to optimize and adjust the usage rate of each function in the page.

Function usage rate = number of module clicks / number of users browsing the current page

This is a simple calculation of the modules that users use the most. Because the content that can be displayed on a mobile phone screen is limited, the usage of a function has a huge relationship with the number of times it is viewed. (The core functions must be seen on the first screen, nonsense!) Therefore, you also need to know the conversion rate of the function itself

Function conversion rate = number of module clicks / number of module views

If you find that a module has very few exposures but a very high conversion rate, you should know what to do.

Core page function analysis

The so-called functional retention refers to the number of users who have performed specified operations on the day and return to the user after N days.

User analysis: User stickiness and activity in using different product functions

Sometimes, you may find that after preparing three killer features A, B, and C, you discover that users come back to use your product because of feature D.

( Zuji is an example) Yes, the world is so cruel.

Retention rate of users of a certain function in different cities

1. The above is basic data collection and the establishment of core indicators. Further mining and analysis also requires comparisons in other dimensions: time, user groups, online time, etc.

2. How to distinguish valid clicks from invalid clicks. In addition to data analysis, we cannot fully trust the data. If a considerable proportion of users coming back from operations promotion are non-proportional users, then this batch of data will seriously interfere with judgment. The question is how to distinguish them?

—END—