Method to implement the statistical report function of product recommendation position in PHP developer city

How to implement the statistical report function of product recommendation position in PHP Developer City

In a mall system, product recommendation position is an important promotional tool. It can be displayed on the website homepage, category page or product details. The page displays recommended products to enhance users’ purchasing desire and purchasing efficiency. However, the number of product recommendation slots in the mall is usually large. In order to better grasp the effect of the recommendation slots, we need to implement a product recommendation slot statistical report function.

1. Demand analysis

1. Statistical indicators: It is necessary to count the number of product impressions, clicks and conversion rate in each recommended position.
2. Statistical period: Recommended data can be counted by day, week, month, etc.
3. Statistical range: Statistics can be based on all recommended positions, or statistics can be filtered based on specified recommended positions.
4. Statistical results display: Statistical results need to be displayed in the form of tables or charts.

2. Technology Selection
Considering that the mall system may have a large amount of data, in order to ensure the performance and scalability of the system, the following technologies can be selected for development:

1. Back-end language: PHP, as a widely used server-side scripting language, has rich development resources and a large number of open source frameworks.
2. Database: MySQL, as a commonly used relational database, is suitable for large-scale data storage and efficient data query.

3. Database design
In the MySQL database, you can create a data table named "recommendation_report" to store statistical data of recommendation positions. The table structure is as follows:

recommendation_report table:
Field name type comment
id INT primary key
recommendation VARCHAR(20) recommendation bit name
date DATE statistical date
impressions INT display Number of times
clicks INT Number of clicks
conversion_rate DECIMAL(10,2) Conversion rate

4. Function implementation

1. Data statistics logic implementation: You can write a PHP script, Used to count the number of impressions, clicks and conversion rate of each recommended position. By traversing the recommendation positions in the mall system, the statistical results are inserted into the "recommendation_report" table.
2. Statistical results display page design: You can use HTML and CSS to write a statistical results display page. This page can display the statistical data of each recommendation position, and you can choose different statistical periods and statistical ranges for filtering. JavaScript can be used to display tables or charts.
3. Data query and export: A simple query form can be implemented to query the recommended statistical data in the database by entering the corresponding conditions (recommended position name, date range, etc.), and provide export to Excel files. function to facilitate saving and analysis.

5. Function Expansion

1. Scheduled tasks: You can use Linux's Cron service to automatically execute statistical scripts every day or every hour to update recommended position statistics.
2. User behavior analysis: You can combine the user behavior logs of the mall system to analyze the click behavior of recommended positions to gain an in-depth understanding of user preferences and purchasing behavior.
3. Big data analysis: Recommendation statistics can be integrated into the big data platform, and technologies such as data mining and machine learning can be used to conduct more in-depth analysis and predictions.

6. Summary
Through the above function implementation methods, we can implement a product recommendation statistical report function in the mall system to help mall operators better grasp the effect of recommended positions. And make corresponding optimization and adjustments. At the same time, this function also provides basic data for data analysis of the mall system and provides a reference for the development and optimization of the mall. In the actual development process, functions can be customized and optimized according to specific needs to meet the actual needs of the mall system.

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