For Internet platform products, they can be mainly divided into two categories: goods and services. If you want to increase product sales through data analysis, you must first understand what data needs to be analyzed?
What data needs to be analyzed?
1. Operation module
From the perspective of the user's consumption process, it can be divided into four parts: drainage, conversion, consumption, and retention.
Traffic
Traffic is mainly reflected in the traffic diversion link. According to the traffic structure, it can be divided into channel structure, business structure and regional structure. Channel structure can track the traffic of each channel and analyze the quality of each channel through the proportion of channel traffic. The business structure tracks the traffic of activities according to the designated business, observes the changes in traffic before, during and after the activity, and evaluates the effect of the activity.
Conversion rate
Conversion rate = number of people with desired behavior/total number of people taking action. Improving the conversion rate means lower costs and higher profits. The most classic analysis model is the funnel model.
churn rate and retention rate
Users are attracted through various channels or activities, but after a period of time some users will be lost. These users are lost users and stay. This part of the users is the retained users. Churn can be divided into rigid churn, experience churn and competitive churn. Although churn is inevitable, corresponding countermeasures can be made to retain users based on the analysis of churn. Regarding retention, by observing the rules of retention and locating the retention stage, it can assist market activities, market strategy positioning, etc. At the same time, it can also compare the functional retention of different users and products, analyze the product value, and make timely adjustments to the product.
Repurchase rate
Repurchase rate can be divided into "user repurchase rate" and "order repurchase rate". By analyzing the repurchase rate, user stickiness can be further analyzed. , to assist in discovering repurchase rate issues and formulating operational strategies. Colleagues can also conduct horizontal (commodity, user, channel) comparative analysis to refine repurchase rates and assist in problem location.
2. Sales module
There are a large number of indicators in the sales module, including year-on-year comparison, completion rate, sales ranking, key product proportion, platform proportion, etc.
3. Product module
Important indicator analysis: including goods age, sales rate, out-of-stock rate, structural indicators, price system, correlation analysis, slow-selling analysis, etc., used to judge Product value, assisting in adjusting product strategy
IV. User module
Key indicator analysis: including the number of new users, growth rate, churn rate, proportion of effective members, retention, etc.
User value analysis: According to the RFM model, other personalized parameters can be integrated to divide the value of users, and further analysis can be made for each level of users.
User portrait: Add tags and weights to users based on inherent attributes, behavioral attributes, transaction attributes, interests and hobbies, etc., design user portraits, and provide reference for precise marketing.
Choose an analysis model based on the data that needs to be analyzed
1. User model
The user model is a method of describing target users in marketing planning or business design , often have multiple combinations, which are convenient for planners to analyze and set up strategies for different users. There are two traditional methods of building user models: building user models based on interviews and observations (rigorous and reliable but time-consuming), and temporary user models (built based on industry experts or market survey data, fast but not reliable enough).
Improved user model construction method: user model based on user behavior data
Advantages: Simplify traditional methods and lower the threshold of data analysis; make data analysis more scientific, efficient, and Comprehensive, it can be more directly applied to business growth and guide operational strategies.
Method:
1. Organize and collect initial understanding of users
2. Group users
3. Analysis User’s behavioral data
4. Infer target motivation
5. Interview and survey verification of users
6. User model establishment and correction
At the same time, the collected user information can also be mapped into user attributes or user behavior information, and stored to form user profiles; pay attention to the fluctuations of its own data in real time, and make strategic adjustments in a timely manner.
2. Event model
The event model is the first step in user behavior data analysis, and it is also the core and foundation of the analysis. The data structure, collection timing and event management behind it are the three major elements in the event model.
What is an event?
Events are the user's behavior on the product. It is a professional description of the user's behavior. All program feedback received by the user on the product can be abstracted into events, which can be collected by developers through tracking. For example: when the user clicks a button on the page, it is an event.
Collection of events
The structure of event-attribute-value: event (user's behavior on the product), attribute (dimension describing the event), value (content of attribute)
During the event collection process, flexible use of the event-attribute-value structure can not only maximize the restoration of user usage scenarios, but also greatly save the amount of events and improve work efficiency.
Timing of collection: user clicks, web page loading is completed, and server determines and returns. When designing the buried point requirements document, the description of the collection timing is particularly important and is also the core to ensure data accuracy.
For example: event collection from e-commerce sales webpages
Event analysis
The analysis of events usually includes the number of people who triggered the event, the number of times, the number of times per capita, Calculation of four dimensions of activity ratio.
Management of events
When there are many events, group the events and mark important events to manage them by category. At the same time, important user behaviors can be marked from a product business perspective, so that commonly used and important events can be found conveniently and quickly during analysis.
3. Funnel Model
The funnel model originally evolved from marketing business activities in traditional industries. It is a set of process data analysis methods.
Main model framework: by detecting the starting point (user entry) in the target process and completing the target action at the end. The number of users and retention of each node that has been experienced can be used to evaluate the quality of each node and find the node that needs optimization most. The funnel model is an important analysis model for user behavior status and user conversion rates at each stage from the starting point to the end point.
4. Heat map analysis - drawing user behavior
Heat map is the most intuitive tool to record the interaction between users and product interfaces. Heat map analysis helps users optimize website layout by recording the user's mouse behavior and presenting it with intuitive effects. Whether it is web or app analysis, heat map analysis is a very important model.
In actual use, several heat map comparison methods are often used to conduct comparative analysis of multiple heat maps to solve problems:
Comparative analysis of multiple heat maps , especially the comparative analysis of click heat maps (touch heat maps), reading heat maps, and pause heat maps;
Comparative analysis of heat maps for segmented groups, such as different channels, new and old users, and different time periods , heat map analysis of AB testing, etc.;
Interactions with different depths reflect different heat maps. For example, comparative analysis of click heat maps and conversion heat maps;
5. Customized retention analysis
The concept of retention rate has been introduced in the previous article. For a product, the higher the retention rate, the more active users the product has, and the greater the proportion of converted into loyal users, which is more conducive to improving the product's monetization capabilities.
Customized retention: Based on the user’s retention situation in your own business scenario, that is, customizing the retention behavior.
Retention behavior can be customized by setting initial behavior and return behavior.
For example: the 5-day retention rate of users who grabbed the coupons and used Harrow *** to enjoy the bike
Initial behavior: grabbed the coupons
Return visit Behavior: Use Harrow to share bicycles
6. Stickiness analysis
Stickiness: Scientifically evaluate the retention ability of the product from the user's perspective
Through user stickiness Through analysis, you can learn how many days a user uses your product or even a certain function in a week or a month, and further analyze the user's product usage habits.
Stickness analysis is one of Zhuge io’s special features, including overall product stickiness, functional stickiness, stickiness trends and user group comparison. For details, please refer to /advanced/stickiness.html
7. Full Behavior Path Analysis
Full Behavior Path Analysis is a type of data analysis method unique to Internet products. It mainly analyzes the user's behavior in the App or website based on each user's behavioral events in the App or website. The circulation rules and characteristics of each module can be used to mine users' access or browsing patterns to achieve some specific business purposes, such as improving the arrival rate of App core modules, extracting mainstream paths and characterization of browsing characteristics for specific user groups, and designing App products. Optimization etc.
There are two commonly used full behavior path models in the visualization process:
Tree diagram: embodying the user's behavior path in a tree structure
Sun diagram : Use a donut chart to reflect the user's behavior path
In the above figure, each ring represents a step of the user, and different colors represent different behaviors. The larger the proportion of the same ring color, the greater the user's behavior in the current step. The more unified and longer the ring is, the longer the user's behavior path is.
8. User grouping model
User grouping is the labeling of user information. Users with the same attributes are divided into one through the user's historical behavior path, behavior characteristics, preferences and other attributes. groups and perform subsequent analyses.
Group model based on user behavior data: When you return to the behavior data itself, you will find that the insights into users can be more detailed and traceable, and you can find the desired group of people faster by using historical behavior records.
Four user grouping dimensions:
User attributes: age, gender, city, browser version, system version, operating version, channel source, etc.;
Active in: Find the active users within the specified period by setting the active time;
Have done/not done: Analyze the "intimacy" of the user's interaction with the product based on whether the user performs a certain behavior ;
New in: By setting a time period, accurately filter out the time range of new users;
How to increase product sales is a comprehensive issue that requires a combination of multiple models For data analysis, the above content is a summary of some knowledge, I hope it can be helpful to you.