[Product Operation] How to grasp user behavior through data analysis?

For operations, it is necessary to grasp user behavior to formulate different operation strategies. User behavior is obtained through data analysis, so what kind of data is the specific data analysis, and what is the difference between different data?

What can I learn from this article?

• Get to know an app quickly.
• Industry trends, market space.
• The status quo of APP's existence, its stage, and the problems encountered.
• Product iterations to discover new growth engine directions.

Data analysis indicators in the user operation process

Generally speaking, the core tasks of i operators can be summarized into two points: the introduction of traffic and the maintenance of traffic.

According to the specific work, it can be divided into user operation, content operation and activity operation. But its core is to deliver value to your user pool through products.

According to different stages, the work to be done by operators is also different. For example, for early users, the work of operating personnel is mainly focused on how to attract new users, generate continuous value for them, and make them stay.

Mature products focus on how to effectively promote user activity and retention rate, discover valuable users from them, stimulate these users to bring value to the product, generate revenue, and promote the survival of the product with quality.

If the user operation is disassembled, it can be divided into several stages: looking for seed users --> mining core users --> attracting more users --> realizing user self-operation --> mining user value (including consumer advertising, etc.) income-generating behavior)

The work of each stage is independent but progresses layer by layer, affecting the overall effect. Therefore, for a single task or operation module during work, it should be quantified into different data indicators to determine the effectiveness of the work.

1. User Retention

What needs to be considered in user retention is the degree of decay of user interest.

There are two situations for retained users: one is to use the product unintentionally, find that it meets their own needs, and stay and use it continuously. The other is that the user's interest in the product is decreasing, the frequency of use is gradually reduced, and the user is gradually away from it until it disappears completely.

Commonly used retention indicators are retention on the next day, retention on the 3rd day, retention on the 7th day, retention on the 15th day and retention on the 30th day.

No matter how good a product is, there is retention and churn. The replacement of old and new is unavoidable, and user retention has always been in a dynamic balance with new users and lost users:

* UR=（SNU/NOW）100%

UR: user retention rate, users who are still using SNU on the Nth day, and new users of NU.

Each of these data has a different internal meaning.

Focus on next-day retention of products:

1. Paying attention to the next day's retention can discover the quality changes of the new version of the product at the first time;
2. The attractiveness and stickiness of cold start to users;
3. Add novice guide design through AB test to judge the usability of the product;
4. Analyze the reasons for user loss through the buried points of new user paths.

Focus on 3-day retention: It can be used to judge the pros and cons of channels, so as to filter channels and adjust delivery strategies.

Focus on 7-day retention: reflect the user's retention after a complete experience cycle, and judge user loyalty.

For the user retention rate at different time points, finer-grained data statistics make it easier to analyze the law of user retention, and formulate corresponding operating strategies according to the actual situation.

2. User Behavior Indicators

The user's behavior data is mainly obtained through buried points, and the user's preferences and expectations for the product can be judged through user behavior analysis.

1. Stickiness: pay attention to the continuous visit of users;
2. Active: pay attention to user access participation;
3. Output: Used to measure the value created by users.

Based on the three major categories of user behavior, add different behavior indicators to each category. It can further mine the value of data and add data standards to make the improvement of team goals more targeted, which is conducive to improving user efficiency.

1. Sticky indicator

The data shows that users press, swipe, and click on their mobile phones 2,617 times a day on average. The average user's mobile phone screen is on for 2.42 hours, and the heavy user's is 3.75 hours.

With the popularization of smartphones and the disappearance of traffic dividends, all traffic growth has stagnated, and the battlefield has changed from snatching traffic to snatching user time.

• Number of openings: The number of openings in user behavior indicators is the most important indicator, because only by allowing users to open them can there be unlimited possibilities;
• Number of visits: The number of visits per unit time by the user is the core indicator of user stickiness. Adding time, age, region, income, gender and other dimensions can effectively formulate corresponding product strategies for different levels of users;
• Interval time: Interval time is the time interval from the user's last visit. Mastering this data can classify the activity of users, which is helpful for the operation to formulate operation strategies and activate users.

2. Active indicators

There is no doubt that the more time users are willing to spend on the product, the greater the possibility of generating value for the product. To increase the use of the market, the first element is of course that the product meets the user's expectations and is easy to use, but what needs to be investigated here is how to use operational means to increase the user's usage time.

For content products, graphics (news information), audio and video (segment videos), etc. are the most important content presentation forms at present. On the content platform, such as the news client, the common method is to continuously strengthen the accumulation of algorithms through the personalized recommendation of content, so as to achieve thousands of people, and the content that users like more will naturally get more recommendation opportunities, thereby attracting users to stay. Increase usage time.

It is similar to e-commerce products, but the recommendation is more complicated, not only for users, but also the portraits of users, products, services, stores and other dimensions, and more recommendation is based on user portraits and similarity . It is also necessary to consider the scene failure of the label, the heat decay of the label, and so on.

• Duration of use: Duration of use refers to the length of time the user uses the APP. Usage is a key factor affecting user conversion, because only by allowing users to stay for a long time can we adopt some methods to attract users to place orders and convert.
• Length of stay: The average length of stay refers to the average length of time a visitor spends browsing a certain page, and the length of stay on a page = the time to enter the next page - the time to enter this page.

The user's stay time is related to the user conversion rate. This is well understood. For a product that is not interested in or cannot arouse the user's desire to buy, the user will not stay for too long.

Dwell time and conversion are not completely positively correlated. Because there are situations where users stay on a certain page for a long time to do other things without jumping.

3. Output indicators

Output indicators have different meanings for different forms of commodities. In the electric fan industry, output indicators are orders, customer unit price, etc. For information flow products, the output indicators are the exposure of advertisements, as well as the click-through rate of advertisements and subsequent conversions.

There are two main ways to increase the exposure of news and information products: one is to rely on the recommendation of the content, recommending it to people who like it, and increasing the number of visits to the information flow; the other is to use gold coins, points and other feedback methods to stimulate users to respond Behavior.

The above indicators follow a principle, and touch the excitement point of users, and obtain the largest number of users with a small investment, that is, satisfy the psychological needs of users. We can analyze and understand the consumer psychology of users through the user's demand pyramid.

Paying attention to output indicators will directly quantify and evaluate the effect of operations and measure the ROI of operation activities.