Customer Engagement Dashboard

Overview

The Customer Engagement Dashboard provides insights into user activity, engagement metrics, retention, and churn risk. It includes AI-powered recommendations and filtering options for improved analysis.

Notes

Screenshots are attached in the EngageIt.pdf

Prerequisites

Ensure you have the following installed:

• Node.js (v16+ recommended)
• MongoDB (running locally or using a cloud instance)
• TypeScript (npm install -g typescript)
• Nodemon (for development mode, npm install -g nodemon)

Setup Instructions

1. Clone the Repository

   git clone https://github.com/your-repo/customer-engagement-dashboard.git
   cd customer-engagement-dashboard

2. Install Dependencies

   npm install

3. Set Up Environment Variables Create a .env file in the project root and configure the following:

   MONGO_URI=mongodb://localhost:27017/customer-engagement
   PORT=5000
   AI_API_KEY=your-ai-api-key

4. Build the Project

   npm run build

5. Run the Project

   • Development Mode (auto-restart on file changes)

     npm run dev

   • Production Mode

     npm run start

6. Database Seeding (Optional: Populate database with mock data)

   npm run mock-seed

Available Scripts

Script	Description
npm run dev	Starts the server in development mode with Nodemon
npm run start	Starts the server in production mode
npm run build	Compiles TypeScript to JavaScript
npm run seed	Seeds the database with predefined data
npm run generate-mock	Generates mock data for testing
npm run mock-seed	Generates and seeds mock data

API Documentation

Base URL

http://localhost:PORT/api/user

Endpoints

1. Get Processed Data

Endpoint:

GET /api/user/processed-data

Description: Retrieves pre-processed user engagement data stored in processedData.json. This includes calculated engagement scores, churn risk, retention category, and AI-generated individual and collective recommendations.

Response:

{
  "overviewMetrics": {
    "dailyActiveUsers": 0,
    "weeklyActiveUsers": 9,
    "monthlyActiveUsers": 38,
    "retentionRate": 13,
    "collectiveEngagementScore": 29,
    "churnPredictionList": [
      {
        "id": 1,
        "name": "User 1",
        "email": "user1@email.com",
        "last_login_date": "2024-12-26",
        "engagementScore": 31,
        "retentionCategory": "Low",
        "churnRisk": true,
        "aiRecommendation": "Personalized Onboarding Session, Incentivized Feature Exploration, Regular Check-Ins."
      }
    ]
  }
}

Errors:

• 500 Internal Server Error: If processed data cannot be read.

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2. Get All Users

Endpoint:

GET /api/user/getUsers

Description: Fetches all users along with calculated engagement metrics, retention categories, churn risk, and AI-generated recommendations. The response is enriched with insights based on user activity from initialData.json.

Response:

{
  "overviewMetrics": {
    "dailyActiveUsers": 0,
    "weeklyActiveUsers": 9,
    "monthlyActiveUsers": 38,
    "retentionRate": 13,
    "collectiveEngagementScore": 29,
    "churnPredictionList": [
      {
        "id": 1,
        "name": "User 1",
        "email": "user1@email.com",
        "last_login_date": "2024-12-26",
        "engagementScore": 31,
        "retentionCategory": "Low",
        "churnRisk": true,
        "aiRecommendation": "Personalized Onboarding Session, Incentivized Feature Exploration, Regular Check-Ins."
      }
    ]
  },
  "users": [
    {
      "id": 1,
      "name": "User 1",
      "email": "user1@email.com",
      "last_login_date": "2024-12-26",
      "number_of_logins": 152,
      "number_of_features_used": 2,
      "time_spent_on_platform": 487,
      "features_used": ["Feature N", "Feature N", "Feature R"],
      "engagementScore": 31,
      "retentionCategory": "Low",
      "churnRisk": true,
      "aiRecommendation": "Personalized Onboarding Session, Incentivized Feature Exploration, Regular Check-Ins."
    }
  ]
}

Errors:

• 500 Internal Server Error: If there is an issue fetching user data.

---

Data Processing Logic

Engagement Score Calculation

• engagementScore = (logins * 0.2) + (featuresUsed * 0.3) + (recencyScore * 0.5)
• Score capped between 0 and 100.

Churn Risk Calculation

• Users who haven't logged in for 30+ days and have engagementScore < 40 are marked as at-risk.

Retention Categories

• High → Engagement Score > 70
• Medium → Engagement Score > 40
• Low → Engagement Score <= 40

AI Recommendations

• AI-generated recommendations are based on engagement scores, feature usage, and churn risk analysis.

Error Handling

Error Code	Meaning
500	Internal Server Error
404	Resource Not Found (if applicable)

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Contributing

Feel free to open issues or submit pull requests to enhance the dashboard.

License

This project is licensed under the MIT License.