5 user analysis rules used by major Internet companies!

This article summarizes the five golden rules of user analysis, aiming to help companies get rid of "indicator-obsession syndrome" and achieve the transition from data collection to deep insights, so as to more accurately meet user needs and improve user experience and corporate performance.

I was chatting with a friend from a large company at the weekend and we talked about user analysis. Many companies do user analysis, but many people’s user analysis is superficial. They just count the number of active days, online time, and cumulative consumption of users, and then start staring at the numbers, not knowing how to make in-depth insights.

After discussing with my friends, I summarized the 5 golden rules of user analysis, which can effectively solve the problem of "staring at indicators in a daze". Let's take a look.

Rule 1: Start with user segmentation

As the saying goes: Long sleeves make for good dancing, and more money makes for good business. When doing data analysis, if the data itself is very small, it is difficult to analyze and draw in-depth conclusions.

In terms of user analysis, if the user is a casual user who only leaves a mobile phone number when registering and logs in once or twice and then never comes back, there is definitely no data to analyze. Only heavy users with a lot of accumulated data can make in-depth interpretations.

Therefore, if you want to make the user analysis more in-depth, you must first stratify and distinguish between light, medium, and heavy users, and then look at:

1. What are the differences in background characteristics among users at different levels?

2. How did heavy users evolve from light and moderate users?

3. Compared with heavy users, at which evolutionary step do light and moderate users differ?

This way, you can see the reason and avoid counting a bunch of averages such as average monthly consumption and average monthly online time, which will erase the differences between users. For specific methods of user segmentation, you can refer to: This is the real user segmentation, not looking at averages

Rule 2: Indicators are divided into deep and shallow, and content depends on needs

After completing the first step, many people naturally think: I see that heavy users log in 7 days a week, and light users log in 1 day a week, so I organize a check-in activity to encourage light users to log in 7 days.

This idea is outrageous. Just think about it, when we use the app, do we carefully calculate the time to log in and the number of clicks? Unless I am trying to get the opening reward, no one would think so.

The user's login, activity, and consumption behavior all have specific goals. Here is the content I like, here are the products I like, and here are the rewards. These are the intuitive reasons. And these reasons need to be obtained by labeling the content and products.

In principle, the more behaviors (consumption, interaction) a user accumulates under a tag, the greater the user's demand for content/products under that tag.

Based on this, when we want to promote a product, we should try it several times to expose the product to users so that we can accumulate data and make reasonable inferences (as shown in the figure below).

Rule 3: Combine testing with exploration

After completing the first step, many people will naturally think of: analyzing how heavy users evolved from light users step by step, summarizing the experience, and copying it to other light users.

The idea is good, but it may not work because the products and services that a company can provide to users are limited and can only attract specific users, so light users and heavy users are not necessarily the same type of people.

Therefore, through the consumption/interaction process of heavy users, a growth path can be summarized in theory:

• The user enters from XX channel and has XX characteristics
• The user first experienced XX product and then repurchased it X days later
• After the user has accumulated purchases of XX amount, he/she starts to expand the consumer category

BUT, this approach may not be useful for all light users, so it may be necessary to develop several more test lines and stimulate light users through different means to see which one works.

There is a classic problem here: many people expect to use data to calculate an optimal recommendation rule to activate light users immediately. This is difficult because light users often have very little data accumulation, and it is difficult to draw effective conclusions in the absence of testing.

Therefore, it is strongly recommended to do more testing and collect some data first. Moreover, operations are not without data analysis. There are many conventional/general recommendation logics that can be used (as shown in the figure below).

For example, if a user buys beer, we should recommend diapers to him, right? No! If he really buys beer, there are too many things that are more suitable than diapers, such as:

• It is recommended to buy a few more bottles (incremental recommendation, suitable for wine lovers)
• Recommended: chicken feet and peanuts (natural category association, both are snacks to go with wine)
• Recommended cigarettes and lighters (cigarettes and alcohol are inseparable, happy you and me)

These products are inherently related to each other and can be recommended without data, so you can first determine the test route based on these innate rules, and then continue to recommend information to stimulate users and see which one they will respond to.

This not only accumulates data and lays the foundation for continuous insight into users, but also accumulates experience and quickly improves performance.

Rule 4: Try more and accumulate continuously

It is not enough to only look at static data when doing user analysis, especially for light users/churned users. The existing data is too little, and subsequent behavior is entirely guesswork, so it is difficult to draw conclusions.

Therefore, we can combine our company’s existing product situation + operating budget to develop a route to increase user base, and then test the effects one by one, accumulating experience while testing.

The best case scenario is that you can find a new way to promote the conversion of light users to heavy users through testing, which is a great achievement. Of course, the worst case scenario is that you find that under the existing conditions, you have exhausted all the combinations of products + discounts + content that can be tried, and still can't do it well.

This is actually valuable. Knowing that existing methods are not effective, at least it can save some waste of resources and promote the upgrading of underlying capabilities such as product upgrades/optimization of operating methods.

Many companies here will have problems in operation:

• Refuse to do tests, always do things the same way
• Do not accept failure when doing testing, force "success"
• Don’t test several solutions at once.

Often, the operations/product departments of these companies like to boast that “we can defeat the masters with random punches” and like to shout: “The purpose of doing activities is to generate benefits!” “Don’t do it if you are not absolutely sure!”

The result is that either there is no data at all and we will never know what users like, or the data is polluted and new products are almost entirely dependent on promotions, with no additional conclusions other than "our users love to get bargains."

Data analysis is not about taking one step and predicting the next 100 steps. It is about checking every step you take: whether there is any deviation, whether you are moving fast enough, and whether you can achieve your expectations. This is something you must remember.

Rule 5: Discuss the effects of profit drivers separately

There is a situation that needs to be discussed separately, that is: the user is driven by interests and completes XX behavior.

Common ones, such as:

• Because of the super low-priced novice gift package, users registered
• Because there are popular products far below the market price, users buy
• Because of the membership activities with large subsidies, users are upgraded to black gold members
• Because of the strong promotion activities, a large number of users are active in a short period of time

In particular, when the goods subsidized by our company are:

• Similar to the new iPhone, a hard currency with high market price and best-selling
• Similar to rice, flour, oil, eggs and milk, these are just-needed products with wide application.
• Products like shower gel and paper towels that are widely used and can be stocked for a long time

This will trigger a large number of users to be active and spend a lot in the short term, but in the long run, these users have not established trust in our company and are simply looking for cheap products. The data generated by this profit-driven approach will interfere with the judgment of normal user needs, resulting in inaccurate subsequent judgments.

Therefore, interest-driven behaviors must be individually identified and analyzed:

• Label activities/products to identify similar "excessive discounts"
• Record the number of times users participate in "excess discounts" and enjoy the discount strength
• Identify new users and add them through "excess discount"
• Identify old users who enjoy a higher proportion of "excess discounts" (50%+)

This can effectively identify who has been bribed, and the rest are likely to be users with real needs.