What do you think are the 3 most important points in user behavior analysis?

I think the three most important points of user behavior analysis: stickiness, activity, and output.

Stickiness, that is, the frequency of user visits and the length of the visit interval. It is a situation where the user continues to access and use ** over a period of time, with more emphasis on a continuous state. Active, which is the average time a user stays and the average number of pages visited.

Refers to the process of each visit, which examines the user's participation in the visit. Therefore, each visit in the statistical period is averaged, and the average visit duration and average number of pages visited are selected to measure activity. The value generated by stickiness and activity may be explicit, of course, it may also be implicit, such as brand or word-of-mouth.

Output, i.e., the number of orders and the average order value. It is directly based on the business of the user to measure the value output created by the user, such as e-commerce, you can choose the number of orders and the "customer unit price", a measure of the frequency of output, and a measure of the size of the average output value.

Of course, different ** needs for user behavior are different, in the analysis of user behavior indicators, it is necessary to pay attention to the selection of the appropriate time period, the length of the time period can not be too short, otherwise it will not reflect the long-term and continuous behavior characteristics of users, and the analysis of sticky indicators will be inaccurate; At the same time, short-term user behavior will also mislead the judgment of the overall characteristics and value of users, it is possible that users are extremely active or extremely low-key during this period of time, or users may create high output in a short period of time, but in the long run, the value created by users is not so high.

The time period of user behavior indicator statistics can be selected according to the business characteristics and user behavior density, for the general, it is recommended that the statistics will be more appropriate once a month, and the monthly behavior indicator data changes can also be compared for certain users or categories.

The foundation of data analytics is business. Different enterprises pay attention to different data according to their own business, such as the Internet and fast consumption, and different industries have different data concerns. Taking the data analysis of general products in the Internet industry as an example, I believe that the three most important points of user behavior analysis are channel analysis, conversion analysis, and retention analysis.

1.Channels: In order to acquire new users, enterprises generally invest resources in external channels, such as SEM, ad networks, social networks, etc.

However, the investment of resources requires the support of a large amount of money, so the analysis of the channel to obtain customers is the most important, after all, it directly determines whether we can maximize the revenue.

2.Conversion: A conversion is the process by which a user approaches your target point.

For example, the new user first discovers your **, then clicks to visit, then follows the prompts on the webpage to register, and then fills in the registration information that needs to be provided in the next step. The whole process is step-by-step. The same process is also suitable for purchases, in which the user first browses the product, adds the shopping cart after feeling interested, and finally submits the order to pay for the purchase, which is also done step by step.

Retention is an important part of the AARR model, and only when it is retained can it guarantee that new users who have already signed up will not be churned for no reason. It's like a bucket filled with water, if there is a crack in the bottom of the bucket, you don't repair the crack at the bottom, but blindly pour water into the bucket, which is equivalent to doing useless work. We typically create retention charts or retention tables to show how users are retained.

Regarding the significance of user behavior analysis and what kind of environment to use these data, many operations said that they have gained a lot, but there are also some specific operations and operation methods that are not too clear, I hope we can explain the knowledge in this regard, so we will invite the teachers of Peking University Jade Bird to introduce to you today, how to use the data for user behavior analysis.

With user behavior data, what are our application scenarios?

Attracting new users, that is, acquiring new users.

Conversion, for example, e-commerce pays special attention to order conversion rate.

Activation, how to get users to use our products regularly.

Retention, early detection of potential loss of users, reduce churn rate.

Monetize, discover the best value users, and improve sales efficiency.

1) Attract new users. Pay special attention to which search engine and which keyword brings the traffic; The keyword is paid or free. The search engine words from Google have brought a lot of traffic, but whether these traffic are on the order, so this data must be combined with eBay's own data, and then the channel is allocated, which channel is the order.

The entire data chain needs to be connected from beginning to end, and it is necessary to integrate the data of the two shielding edges.

2) Transformation. Taking the registration conversion funnel as an example, the first step is to know what registration entrances are on the web page, and many ** have more than one registration entrance, and each event needs to be defined; We also wanted to know how many people went next, what percentage of people clicked the sign-up button, and how many people opened the verification page; How many people logged in, and how many people completed the entire complete registration.

During the period, there will be user loss at every step, and after the funnel is completed, we can go straight to the loss rate of each link.

3) Promote activity. Another is the fluency of the user using the product. We can analyze specific user behavior, such as the length of the visit, which is particularly long on that page, especially on the app.

Then there is the improvement of user portraits, and it is more accurate to use user behavior analysis to make user portraits.

User behavior analysis is to analyze the data behind the user's behavior and behavior on the product, and change product decision-making, achieve refined operation, and guide business growth by building user behavior models and user portraits. In the process of product operation, DMhub collects, stores, tracks, analyzes and applies the data of user behavior, etc., and can find the virus factors that realize the self-growth of users, the characteristics of the Qingsock group, and the target users, so as to deeply restore the user use scenarios, operation rules, access paths and behavioral characteristics.

1. What is a user?

Behavioral analysis? User behavior can be summed up in 5w2h:

User behavior analysis is to analyze the user's behavior on the product and the data behind the behavior, and build user behavior models and user portraits to change product decisions, achieve refined operations, and guide business growth.

In the process of product operation, DM Hub collects, stores, tracks, analyzes, and applies user behavior data, etc., and can find the viral factors, group characteristics, and target users that achieve user self-growth, so as to deeply restore user usage scenarios, operation rules, access paths, and behavioral characteristics.

Dear Hello, user analysis is mainly composed of two parts, including attribute feature analysis and behavioral feature analysis, whether it is wrong or right, and behavioral event analysis is based on the key operational indicators to analyze the specific events of user songs. By tracking or recording user behavior events, you can quickly understand the trend of events and the completion of the user's New Year. For a specific behavior, comprehensively describe and compare, deeply analyze each dimension of its abnormal appearance, and confirm the reasons for the performance of the behavior data.

2. User Retention AnalysisUser retention analysis is a model used to analyze user participation and activity. Retention and retention rates provide insight into user retention and churn. For example, indicators such as next-day retention, weekly retention, and monthly retention are used to measure the popularity or viscosity of a product.

Retention is an indicator to measure whether users use the product again, and it is also an indicator for the survival of every app, which can reflect the health of any product and the overall performance of the product, operation, and recommendation effect. Fitting business attributes and refining the retention process will be more valuable and instructive for retaining data. Through retention analysis, it is possible to analyze the reasons why users stay in the product, so as to optimize the core functions of the product and improve retention.