Refined user retention and funnel analysis methods!

1. Event Analysis

The application field of event analysis is very broad, and different scholars have explained it from the perspective of this field. Event study is a quantitative analysis method that uses specific techniques to measure the impact of an event based on data statistics before and after the event.

Event analysis during operations is the tracking or recording of user behaviors or business processes. For example, an e-commerce product may include the following events: user registration, product browsing, adding to shopping cart, order payment, etc.

Event segmentation: unlimited segmentation of a certain behavior, locating the factors that influence the behavior, and also the process of grouping users.

Drilling down and drilling down are the characteristics of event analysis. As for how and when to use them, it depends on the specific analysis of the specific problem.

2. Funnel Analysis

Regarding the funnel model, I think the essence is decomposition and quantification. Why do I say this? Here is an example using the marketing funnel model.

The explanation given by the encyclopedia: The marketing funnel model refers to a quantitative conversion model that gradually transforms non-potential customers into customers during the marketing process. The value of the marketing funnel model lies in quantifying the efficiency of each link in the marketing process and helping to find weak links.

In other words, the marketing links refer to the sub-links in the entire process from acquiring users to ultimately converting them into purchases, and the conversion rate of adjacent links refers to the use of data indicators to quantify the performance of each step.

Therefore, the entire funnel model is to first break down the entire purchase process into steps, then use the conversion rate to measure the performance of each step, and finally find out the problematic links through abnormal data indicators, so as to solve the problem and optimize the step, and finally achieve the goal of improving the overall purchase conversion rate. The core idea of ​​the overall funnel model can actually be attributed to decomposition and quantification.

Taking e-commerce as an example, several core funnels can be established as follows:

1. Operational funnel

Banner, event area, discount area, hot sale area, etc.

1. Search Funnel

The search funnel can also be split into smaller funnels, such as segmented keyword ranking, keyword clicks, best match tag selection, browsing time and other data items.

1. Product Funnel

The product funnel can actually be disassembled into many branch funnels, depending on the usage.

3. Retention Analysis

Retention analysis is an analytical model used to analyze user engagement/activity, examining how many users who perform initial behavior will perform subsequent behavior. This is an important method to measure the value of a product to users.

Retention analysis can help answer questions like:

1. Did a new customer complete the behavior you expected him to complete over a period of time in the future? Such as payment orders, etc.
2. A social product has improved the onboarding process for newly registered users, hoping to improve user engagement after registration. How can this be verified?
3. Want to determine whether a product change is effective, such as adding a new feature to invite friends, and observe whether some people use the product for a few more months because of the new feature?

Below is a common retention curve. I divide it into three parts: the first part is the oscillation period, the second part is the selection period, and the third part is the stable period.

So how do we use retention analysis? For example:

Segment user groups and analyze the impact of each product feature on user retention . We can not only compare favorites, but also likes, reposts, comments, and follows.

If it is an e-commerce site, you can also compare no purchases, purchases 1, 2, 3, 4, 5, 6 times, etc.

We analyze the curve to find the functional point or behavior point with the highest retention. Magic numbers and magic functions,

1. For transactional users, the retention rate is the highest if they purchase more than 5 times. So our strategy is to get users to purchase 5 times as quickly as possible (pay attention to the time window).
2. For tool products, find the magic function that will lead to the highest user retention.
3. If it is UGC, find the number of posts made by the user or the functions used by the user as the magic number and magic function.

4. Distribution Analysis

When production is working normally, the quality of the products cannot be exactly the same, but it will not differ too much either. Instead, it will vary and be distributed within a certain range around a certain average value.

Distribution analysis is an important method to discover problems by analyzing the changing distribution state of quality. It can understand whether the production process is normal, whether waste occurs, etc. Its tool is the histogram, so it is also called the histogram method.

As shown in the figure below, we can see the distribution of the number of people and the average transaction price.

A similar approach is user distribution analysis, which focuses on finding concentrations. Of course, we can use the K-means clustering algorithm to make it more advanced.

V. Conclusion

These are just some of the more commonly used methods in data analysis. In reality, what we face are much more complicated and detailed than the online examples, but the logic is that simple. Analysis is just a tool. It depends on what you want to get.

This concentrated analysis method does not exist alone. Often, there is a connection between you and me, and we must operate flexibly. Let me share three tips in data analysis: look at trends, look at distribution, and look at comparisons.

Author: Bai Gaoliang

Source: White Sorghum