Artificial intelligence-based enterprise WeChat SCRM system

Artificial intelligence-based enterprise WeChat SCRM system

2022-09-05 0 866
Resource Number 38215 Last Updated 2025-02-24
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Value-added Service: Installation Guide Environment Configuration Secondary Development Template Modification Source Code Installation

In this issue, we recommend an AI-based enterprise WeChat SCRM system – LinkWeChat.

 

Artificial intelligence-based enterprise WeChat SCRM system插图

LinkWeChat is an AI-based enterprise WeChat SCRM system, which integrates the basic customer management and background management functions of enterprise WeChat, and can establish a strong link relationship between customers and enterprises through flexible and efficient customer operation modules such as customer drainage, customer retention, and community operation.

Features
Operation center: full-featured data reports such as customers, customer groups, and conversations, and the data is clear at a glance
Drainage and customer acquisition: multi-channel drainage such as live code, group live code, high seas, customer service, etc., to achieve accurate customer acquisition
Customer Center: Help enterprises build private domain traffic pools and operate customers efficiently
Customer retention: The operation of enterprise customers is refined, and the circle of friends and red envelope tools improve customer activity
Community operation: Full coverage of customer group operation scenarios and rapid group pulling
All-round marketing: Provide multi-type and multi-scenario customer marketing tools
Enterprise risk control: Compliant archiving of sessions and global risk control of sensitive content
Enterprise management: full integration of organizational structure and self-built applications to achieve “one background”
Technological advantages

Based on WeCom’s ability to fully open itself from inside the service to the outside world, LinkWeChat can provide enterprise and micro private domain management infrastructure for vertical scenarios such as e-commerce, retail, education, and finance, with the following main advantages:

Fully docking with the open API of enterprise and micro, no need to repeat the docking, and quickly get started
Secondary integration and encapsulation of the enterprise micro API to avoid repeated pitfalls
It adopts the mainstream Java architecture, which has high scalability and flexibility, and avoids the shortcomings of PHP architecture
Provide internal APIs externally for low-cost secondary development
Intelligent semantic analysis of session content based on NLP to automate tags and alarms

Artificial intelligence-based enterprise WeChat SCRM system插图1

Tech stack
Front

 

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  • back end

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Install and deploy
Minimum server configuration

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  • Environment preparation
    JDK > = 1.8 (1.8 recommended)
    Mysql > = 5.7.0 (5.7 recommended)
    Redis >= 3.0
    Maven >= 3.0
    Node >=10
    Running the system

    1 Back-end run

    Import into IDEA
    Create the database LW-vue and import the data script
    Open the Run com.linkwechat.LinkWeChatApplication.java

    2 Front-end operation

    # Go to the project directory
    cd linkwe-ui

    # Install dependencies
    npm install

    # It is strongly recommended not to use cnpm installation directly, there will be all kinds of weird bugs, you can solve the problem of slow npm installation by respecifying the registry.
    npm install –registry=https://registry.npm.taobao.org

    # Local Development Start the project
    npm run serve

    Open a browser and enter http://localhost:80, the default password is: admin/123456.

    3 Required Configuration

    To modify the database connection:

    Edit the application-druid.yml in the resources directory
    URL: the address of the server
    username: the account
    password: the password

    Development Environment Configuration:

    Edit the application.yml in the resources directory
    port: port
    context-path: the deployment path
    Deploy the system

    1 Back-end deployment

    bin/package.bat is executed in the project’s directory
    The target folder will then be generated under the project containing the war or jar (multi-module generation is found in linkwe-admin)
    JAR deployment method: Use the command line to execute the java –jar LinkWeChat.jar
    War deployment method: pom.xml packaging is modified to war and put it into the Tomcat server webapps

    2 Front-end deployment

    # Package the live environment
    npm run build:prod

    # Package the pre-release environment
    npm run build:stage

    After the build is successfully packaged, a dist folder will be generated in the root directory, which is the build packaged file, usually .js , . Static files such as CSS, index.html, etc.

    Normally, the static files of the dist folder can be published to your nginx or static server, where the index.html is the entry page of the backend service.

    System screenshot

 

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You can read more on your own.

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