User behavior analysis, this is the most practical guide I have ever seen!

User behavior analysis is an important part of data analysis, but many people often feel overwhelmed when faced with massive amounts of data. This article will provide you with a practical user behavior analysis guide, from the definition of user behavior, data collection methods, to analysis strategies in different business scenarios, to help you systematically understand and apply user behavior data.

"We have a lot of user data, but how do we analyze it?" Many people have similar questions. If we simply count how many active users there are and how many paying users there are, we can't see the value of these numbers.

Today I will give you a systematic introduction.

The article is quite long, so please remember to click "like" first and then read it slowly.

1. What is User Behavior?

A user ID and any recordable actions generated in an enterprise's internal system can be called user behavior.

Complete user behavior includes 6 elements:

1. Time: When it happens
2. Location: Occurred on XX channel/platform/system
3. Character: Who happened
4. Cause: The first action
5. Pass: The chain composed of all actions
6. Result: The result of the behavior. These elements are expressed differently on different platforms (see the figure below):

The methods of collecting user behavior vary on different system platforms.

There are three common types:

1. Background records: user registration form, service request form, transaction order, etc.
2. Tracking records: user browsing history in APP, mini-programs, and H5
3. Business staff feedback: information provided by sales, customer service, and after-sales staff

This is why there are many indicators of user behavior and it is not easy to draw conclusions.

There are millions of user behaviors. Only by understanding why the business needs user behavior data can we know which ones are really useful.

2. Requirements of different businesses

The business side is concerned about user behavior in four situations:

Situation 1: I know nothing, let’s wait and see.

Common ones, such as:

• New officer takes office, unclear about the situation
• New business line, no review
• At the beginning of the new year, we need to make new plans

In this case, the data should be rough rather than detailed, and comprehensive rather than precise. First give an overall overview to let the leaders/business colleagues get a feel for it, and then analyze it in depth when there are specific topics (as shown in the figure below). Otherwise, if you start with a bunch of trivial things, it is very likely to confuse people and make them wonder, "What on earth is this all about?"

Situation 2: Have something in mind and focus on the results.

In this case, the goal is usually to observe the business process, product features, and the effects of content release. The business side has a clear goal: to see how well this thing is done.

Common ones, such as:

• Content section: user clicks, participation in discussions, and forwarding actions
• Functional points: number of users, frequency of use, and duration of use
• Goods: User browsing, purchase, repeat purchase, one-time large purchase

At this point, we can no longer talk about it, but focus on the functional points that the business is concerned about (as shown below)

Note! More user behavior does not mean good performance. For example, in e-commerce business, the operator enthusiastically launched an activity where users could get discounts by watering and planting trees, in an attempt to increase the number of active users. However, it was found that users were playing games and waiting for discounts, and the number of people placing orders decreased!

At this time, you can use matrix method, before-and-after comparison method, behavioral relationship analysis and other methods to specifically look at the impact of this behavior on performance (as shown in the figure below).

Situation three: Performance pressure, overwhelmed.

This situation is generally about evaluating the performance of core processes, such as new user registration, participation in large-scale events, transaction processes, key issue complaints, etc.

At this time, the analysis goals are very specific:

• The registration conversion rate must be high!
• The participation rate in activities needs to be increased!
• The transaction ratio must be high!
• Critical complaints are resolutely extinguished!

This kind of user behavior analysis with clear goals can be said to be the simplest and easiest. The core idea is the following four modules

Attention! Many students like to look at the smallest details when looking at data, trying to find problems from every user action. This makes it easy to be overwhelmed by the vast amount of data.

When evaluating the core process, you should first focus on the overall effect (such as overall traffic + overall conversion rate). Once you have made a judgment, then look at the details.

Another point is to look at user data in conjunction with remedial measures.

User behavior is the result of various factors. In actual business practice, it is impossible to conduct controlled variable research on every project like in a laboratory. Even if AB testing is done in advance, there will be various differences when it is actually launched due to time and location.

When faced with complex problems, we don’t have to worry about whether the user doesn’t like the copy or the product, but rather what we can do to save the relationship.

Remedial measures analysis is much more valuable than just shouting "This process doesn't work!"

In the short term, there may be only 2 or 3 possible business remediation measures. Analyze which one is likely to be effective and directly promote business actions, making data analysis effective.

Situation 4: The situation is unclear and everyone is suspicious.

This situation usually happens when a business is not doing well and the business side does not have a clear hypothesis. So they think: "Can we dig deeper into user behavior? Find the cause?" As for what to dig and what the cause is, they may not even know...

This is the most difficult situation, because the analysis goal is completely unclear.

There are two basic ideas here:

Idea 1: The business side first circles out their target customers, and then see what the target customers are doing

Idea 2: First find heavy customers of a behavior, and then ask the business side: Is this what you want? In short, it is easier to find inspiration for solving problems from extreme situations.

For example, regarding points redemption, the business side just feels that this business is not doing well, but they can’t tell exactly what is wrong.

At this point, you can look at the data in two ways as shown below (as shown below):

If you find that high-value users have a clear preference for certain gift redemptions, you can design corresponding gift plans to attract high-value users.

If it is found that heavy users are clearly suspected of taking advantage of the system, the reward rules can be modified accordingly.

In short, as long as the behavioral differences between user groups are large enough, strategies can be generated.

IV. Summary

From the above four situations, we can see that even the same data can be presented in different ways in different situations.

This requires students to carefully understand business needs in their work.

Many students would say: Why not just ask the business directly?

The problem is that, among the four situations, except for situation 3 where there is clear KPI pressure, the other three situations are very vague, and the final verbal demand is: "Do a user behavior analysis." This requires students who work with data to have a certain degree of judgment.

The above four situations are progressive, and their logical relationship is shown in the figure below. Students can guide the business like peeling an onion, find the issues that they really care about, and make valuable analysis.