How to do user data analysis, how to conduct data analysis

I want to ask this question at the same time, and I have always wanted to use user data to make a beautiful analysis chart.

Perform data analysis steps:

1. Data collection.

When we do data analysis, the first problem we solve is the data source. It is divided into two main categories. The first category is the data that can be obtained directly, that is, the internal data. The second category: external data, which is obtained after processing and sorting.

2. Data cleaning.

The purpose of data cleaning is to extract and derive valuable and meaningful data from a large amount of disorganized data. The truly valuable, organized data that remains after cleaning reduces the analysis hurdles for subsequent data analysis.

3. Data comparison.

Comparison is the entry point for data analysis. Because if there is no reference.

There is no quantitative evaluation standard for the data.

Typically, we do horizontal and vertical comparisons. Horizontal comparison, compared with industry average data, compared with competitor data, and vertical comparison, is a comparison with the historical data of your own products, around the timeline.

Compare. <>

4. Data segmentation.

When there is an anomaly in the data comparison, data segmentation is required, and data segmentation is usually divided into latitude and then granularity. Latitude is also time or region, **, interviewed, etc. The granularity is also based on days or hours.

By subdividing the latitude and granularity, you can lock the problem area with the difference value of the comparison, and it will be easier to find out the cause of the problem.

5. Data traceability.

Basically, we can analyze the causes of most problems through data segmentation, but we will also encounter special situations, so we need to conduct further analysis, that is, we can find out the cause of the problem through data traceability.

Based on the locked latitude and granularity as the search criteria, querying the original logs and source records involved, and then analyzing and reflecting on the user's behavior based on this often leads to different findings. Or think about it in combination with user usage scenarios.

A few steps to conduct user profiling:

1. First of all, look at the data changes stimulated by user behavior, including jump-out, exit, activity, and daily activity, which will have a monitoring effect on operations, and the trend represents growth or attenuation, and abnormal response problems; These data can be analyzed globally with the Kanban board of Analysys Ark;

2. Secondly, the users can be grouped according to user attributes, contact behavior classification, and the results of the analysis obtained by marketing automation. These can be used to guide decision-making using user operation analysis; Ark can support user segmentation, as well as inherit multiple marketing tools, and can detect the effect of marketing feedback.

3. Finally, the operation of e-commerce users should pay more attention to the user's purchase attributes, analyze the user's age, class, hobbies, etc. according to the user's purchases, and carry out precision marketing.

To do a good job of user profiling, here are a few key steps to consider:

1.Define your goals: Be clear about what your user analytics goals are. Is it to understand user needs, improve product experience, or dismantle to increase sales conversion rate? Specific travel targets can help guide the subsequent data collection and analysis process.

2.Collect user data: Collect user data using a variety of methods, such as analytics tools, questionnaires, user interviews, social monitoring, etc. The data collected can include user behavior data, demographic information, user feedback and opinions, etc.

3.Organize and clean data: Organize and clean the collected user data to ensure data accuracy and consistency. Remove duplicate, invalid, or erroneous data, and make necessary data conversions and formatting.

4.Analyze and interpret data: Analyze user data using appropriate data analysis methods and tools. For example, use statistical analysis, user profiling, behavior path analysis, and other means to discover user behavior patterns, demand trends, and potential problems.

5.Find insights and opportunities: Uncover insights and opportunities from the results of analytics on user data. Identify users' pain points, preferences, and expectations, and identify product improvement directions and opportunities for optimization.

6.Refine user portraits: Based on user data and analysis results, form user portraits or user groups. Segment users into groups with similar characteristics and needs to help you understand your users and target your marketing and product design.

7.Continuous monitoring and evaluation: User analytics is an ongoing process that requires regular monitoring and evaluation of user data to track changes and effectiveness. Adjust policies in a timely manner by continuously analyzing user behavior and feedback.

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.

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.

Hello, it is a pleasure to serve you and provide you with the following answer: User data pre-high group testing is a method that uses historical data and machine learning technology to improve future user behavior. 1.

First of all, it is necessary to collect the user's historical data, including the user's behavior data, such as purchase history, browsing history, etc., as well as the user's attribute data, such as gender, age, etc. 2.Then, the user data is cleaned and preprocessed to better analyze and mine the data.

3.Next, Bi Nianbu wants to use machine learning techniques, such as decision trees, random forests, support vector machines, etc., to establish a model of user data to improve future user behavior. 4.

Finally, the model is evaluated to determine its accuracy and reliability. Personal Tips:1

When collecting user data, collect as much user behavior and attribute data as possible to better analyze and mine the data. 2.When modeling user data, try different machine learning techniques to find the best model.

3.When evaluating a model, pay attention to the accuracy and reliability of the model to ensure the effectiveness of the model.