This [User Behavior Analysis] guide is so professional!

What should you do if there are too many user behavior demands? The author summarizes a [User Behavior Analysis] guide for your reference.

There are so many user behaviors that when we receive such analysis requirements, we firstly don’t know how to start, and secondly, after we finish, we are either complained that “the analysis is not thorough” or “there is no focus, what is the point of analysis!”.

What should I do?

1. Common Mistakes in User Behavior Analysis

Mistake 1: Randomly placing indicators and mixing them all together

The most typical example is to put basic user information such as gender, age, occupation, height and weight at the top. Note that user behavior should be analyzed based on behavior, not basic information. Too many irrelevant indicators will only interfere with the vision and make things even more confusing.

Mistake 2: Listing data without making judgments

The most typical example is that a large number of data such as the number of user logins, clicks, and page jumps are listed. What does it actually show? There is no conclusion. This kind of thing cannot be called "analysis" at all, it is just a basic data display. Since it is an analysis, there must be a conclusion, a problem, and an answer.

Mistake 3: Taking words literally and jumping to conclusions

The most common analyses of this type are:

• There are fewer users logging in, so we need to increase
• This product is bought by many users, so we should sell more
• This content has a lot of users, so continue to release it.

Basically, if the data is low, we raise it, and if it is high, we keep it the same. The conclusion is so stupid that the business department cried...

The above chaos mainly comes from: lack of understanding of the key points of user behavior that different departments are concerned about. Without knowing the key points, they just piece together the data and ignore how to extract conclusions from the data, which results in unnecessary work. If you want to break the deadlock, you have to think seriously: what can the business see from user behavior?

2. What is User Behavior?

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

A complete user behavior includes 6 elements:

• Time: When it happens
• Location: Occurred on XX channel/platform/system
• Character: Who happened
• Cause: The first action
• Pass: The chain composed of all actions
• Consequences: The results of a behavior

These elements are expressed differently on different platforms (as shown below).

Different system platforms have different ways of collecting user behaviors. There are three common types:

1. Background records: user registration form, service request form, transaction order, etc.

2. Tracking records: user browsing records in APP, mini-programs, and H5

3. Feedback from sales staff: Information provided by sales, customer service, and after-sales staff

In short, this is why the user behavior-related indicator data appear to be numerous, complex, and chaotic: there are many kinds of user behaviors, and if they are not combined with specific business needs, they cannot be explained clearly.

III. Demands of different businesses

There are four situations in which the business side pays attention to user behavior.

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 short, I don't have a good understanding of the basic situation.

In this case, it is better to be rough than detailed, and comprehensive 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: Focus on results

This situation usually occurs after a specific business process, product feature, or content is released. The business side has a clear goal: to see how well this thing is doing.

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 cannot talk about it in detail, but instead focus on the functional points that the business is concerned about and display the data from large to small (as shown in the figure below).

Attention! The first big pitfall of user behavior analysis is that 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 3: Performance pressure, overwhelmed

In this case, the evaluation is usually about specific business processes, and the process is the core process, such as new user registration, participation in large-scale activities, 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.

It should be noted here that many students will directly insert the conversion process analysis. Doing so will present too detailed data and easily blur the overall judgment. The judgment of good/bad is always the first priority. If even the judgment of "good" or "bad" is wrong, then the subsequent cause analysis is all wrong. Therefore, it is the first priority to judge the overall situation and see if it is acceptable.

Another point is that remedial measures analysis is often overlooked by many students. The second biggest pitfall of user behavior analysis is that it is an analysis of "knowing what it is, but not why it is". User behavior is the result of various factors. In the actual enterprise, it is impossible to conduct control variable research for each project like in the laboratory. Even if AB tests are done in advance, there will be various differences when it is actually launched due to the right time and place.

So when you really encounter a problem, it is very likely that you will not be able to analyze the cause in a short period of time, or even if you roughly know the cause, you will not be able to stop the activity or change the channel. At this time, the idea is not to worry about whether the user does not like the copy or the product, but to think about what else we can do to save the situation.

Therefore, remedial measures analysis must not be omitted. This is much more valuable than shouting "This process doesn't work!" alone. This is also the reason why many data clearly provide problems with user conversion paths, but the business side still shouts "Not constructive". No one likes the mourning bird that shouts every day: "It's over! It's over!" People want to hear: "Try this! Try this!"

Situation 4: Unclear situation, 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 case, because the analysis goal is completely unclear. There are two basic ideas:

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

Idea 2: First find a heavy customer 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, when exchanging points, the business side just feels that this business is not working, but they can't tell why it is not working. At this time, you can look at the data in two ways (as shown below).

If it is found that high-value users clearly prefer certain gift redemptions, then a gift plan can be designed to attract high-value users. If it is found that heavy users are obviously suspected of taking advantage of the situation, then the reward rules can be modified accordingly. In short, as long as the differences in user group behavior are large enough, a strategy 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 and see."

This requires students who work with data to have a certain level 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 they really care about, and make valuable analysis.