5 key factors for user growth

In this era where traffic is king, traffic dividends are extremely important to all walks of life. However, in this red ocean with extremely high competitive pressure, acquiring users is becoming increasingly difficult and the cost is increasing.

In the PC Internet era, the annual growth rate of Internet users reached 50%, and you could get a lot of traffic by just building a website; in the early days of mobile Internet, APP also experienced a wave of traffic dividends, and the cost of acquiring a customer was less than 1 yuan; but in recent years, as the traffic growth dividend has faded, competition has become increasingly fierce, with hundreds of peers competing in each field, the cost of acquiring customers has soared to an unbearable level, and business growth has slowed down or even regressed.

In such a high-cost, highly competitive environment, if an enterprise cannot use data analysis to carry out refined operations internally, it will result in a huge waste of resources, which will inevitably increase the enterprise's operating costs and make it lack competitiveness.

For Internet platforms, traditional data analysis mainly focuses on result-based data, but lacks analysis of the user behavior process that produces the results. Therefore, the value of data analysis is relatively limited. This is why in recent years many companies feel that they have done sufficient data analysis but have not seen much effect.

The previous generation of user behavior analysis (more accurately, website statistics or APP statistics) tools were mainly limited to the analysis of browsing behavior, without analyzing the user's in-depth interactive behavior. Therefore, the analysis value was relatively limited. Currently, most Internet practitioners still have this impression of user behavior analysis.

Many people still don’t understand the causal relationship between user behavior and business growth. Even if they have purchased a user behavior analysis system, they only focus on conventional PV and UV. They do not understand the value of user behavior analysis and underestimate the difficulty of user behavior analysis, which ultimately causes the system to be idle.

So how do we solve this dilemma?

——Let’s start by understanding user behavior analysis.

What is User Behavior Analysis?

User behavior analysis is to collect and analyze these data to discover the patterns of user use of products, and combine these patterns with the website's marketing strategy, product features, and operational strategy to discover possible problems in marketing, products, and operations. Solving these problems can optimize the user experience, achieve more refined and accurate operations and marketing, and enable the product to achieve better growth.

By analyzing the 5W2H of user behavior, we can understand where the users come from, what operations they perform, why they leave, where they leave, and so on. This will improve user experience and platform conversion rates, and enable companies to achieve business growth through refined operations.

5W2H: Who (who), What (what behavior was done), When (when), Where (where), Why (what is the purpose), How (how to do it), Howmuch (how long it took and how much it cost).

How to conduct user behavior analysis?

First of all, we need to clarify business goals, deeply understand business processes, identify key data nodes that need to be monitored based on goals, and do a good job of collecting and organizing basic data. With enough data, we also need to master scientific methods and analysis models, and apply them in practice to achieve effective analysis, so that we can truly achieve data-driven business analysis and decision-making.

1. Observe the changes in data trends from the overall user dimension

New users, active users, number of visits, average usage time and usage time distribution are commonly used in daily operations and are rough indicators for measuring overall data dimensions.

We can use these indicators to view the approximate data of different time periods, different channels, and different products from a macro perspective. Once data anomalies occur, such as a significant decrease in the number of active logged-in users today, the analysis scope can be narrowed down to locate product problems.

Taking "reading" products as an example, it can be seen from the figure that in each user's use, most users use this product for between 1-5 seconds and 3-10 minutes. After having a basic understanding of product stickiness, you can analyze the behavioral characteristics of users with a usage time between 1-5 seconds, or analyze the behavioral characteristics of users with a usage time of more than 3 minutes, so as to find the key growth points for increasing user usage time.

2. Conduct specific analysis on different user behaviors

We can define user behavior on the product as events, so that all program feedback obtained by users on the product can be abstracted as events for collection. Events can fully record user behavior.

For example, we can know the time and minute when user A entered the product details page, and through the attributes we can collect the product name, product ID, product type, etc. of the current page, to maximize the restoration of the user's usage scenario. We can conduct focused analysis based on different user behaviors.

• Trend analysis: Analyze the changing trend of a single event over time;
• Comparative analysis: group and compare based on user attributes or event attributes;
• Screening analysis: select events that meet certain characteristics through screening conditions and analyze them;
• Regional analysis: You can view the map for the province and city breakdown of environmental attributes and user attributes.

3. Analyze the conversion rate between different behaviors through funnels

For process analysis of relatively standardized business processes with long cycles and many links, funnel analysis can intuitively discover and explain the problems. Companies can monitor user conversions at all levels, focus on the most effective conversion paths throughout the user purchasing process, identify shortcomings that can be optimized, and improve user experience.

At the same time, scientific funnel analysis can show the conversion rate trend curve, which can help companies accurately capture changes in user behavior. It has scientific guiding significance for improving the accuracy and efficiency of conversion analysis, locating process anomalies and verifying the effectiveness of strategy adjustments.

Operations personnel can observe the conversion rates of user groups with different attributes (such as new registered users and old customers, customers from different channels) in each link, and compare the differences in conversion rates of each process step. They can understand the user group with the highest conversion rate, analyze the rationality of the funnel, and make adjustments to the links with abnormal conversion rates.

4. Judge user retention based on initial behavior and return visit behavior

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

Through retention analysis, we can mainly check whether the new function has different retention effects on different groups after it is launched? Can you tell whether a new product feature or activity has improved user retention?

Taking into account many factors such as version updates and marketing promotions, we cut off functions with low frequency of use, achieved rapid iteration verification, and formulated corresponding strategies.

Retention rate is the most important criterion for judging product value, revealing the product's ability to retain users. In fact, it is a conversion rate, that is, the process of converting initial unstable users into active users, stable users, and loyal users. As the statistics change, operators can see changes in users over different periods of time and thus determine the product's appeal to customers.

5. Record the user’s real behavior and determine the preferred path

Once users come to your app, they typically follow different paths to use your product. At this point, we need to explore all the different paths that users take in the application from a global perspective.

Path analysis allows us to see the most common paths that users take within a specified time, understand what users do after entering the application, and understand how users leave the application step by step.

Taking e-commerce as an example, from logging into the website/APP to successfully paying, buyers have to go through the process of browsing the homepage, searching for products, adding products to the shopping cart, submitting the order, and paying the order.

The actual purchasing process of users is an intertwined and repetitive process. For example, after submitting an order, the user may return to the homepage to continue searching for products, or may cancel the order. There are different motivations behind each path.

After in-depth analysis in conjunction with other analysis models, it can help find user motivations quickly, thereby leading users to the optimal path or the expected path.

VI. Final

For anything, if we want to do it well, we must first understand it. Only after understanding it can we better control it. So how can we clearly know the market performance of a product?

This requires us to analyze the user's usage behavior of the product. Being good at obtaining data, analyzing data, and applying data is the basic skill for everyone to do their job well. Every company should strengthen the application of big data in user behavior analysis, find patterns from the data, and use data to drive business growth.

Author: Zhuge io

Source: Zhugeio