PODAUDIENCE Documentation

Overview

This project provides a database-driven system for podcast audience segmentation. It includes various audience types, each with a unique score based on their preferences or interactions with different podcast feeds. This data can be used to tailor content for specific listener segments.

Features

• Audience segmentation based on podcast feed interactions.
• Audience scoring and categorization.
• Data storage and management for audience types, scores, and interactions.

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Setting Up the Database

Prerequisites:

Ensure you have the following installed on your system:

• MySQL or MariaDB for the database.
• PHP for interacting with the database.
• Apache or any other web server (if running PHP-based scripts).

Step 1: Create a Database

You need to create a MySQL/MariaDB database where the podcast audience data will be stored. Here’s how:

1. Open your terminal or command-line tool.

2. Log into MySQL:

   mysql -u root -p

3. Create the database:

   CREATE DATABASE podcast_system;

Step 2: Create Tables

Now, create the necessary tables for storing the podcast audience data. Below is the SQL schema to create the necessary tables.

USE podcast_system;

CREATE TABLE podcast_audience (
    auto_id INT AUTO_INCREMENT PRIMARY KEY,
    feed_id INT NOT NULL,
    apple_id VARCHAR(50) NOT NULL,
    audience_type VARCHAR(255) NOT NULL,
    score INT NOT NULL,
    created_date DATETIME DEFAULT CURRENT_TIMESTAMP
);

This schema includes the following fields:

• auto_id: A unique identifier for each record.
• feed_id: ID associated with the podcast feed.
• apple_id: A unique ID for the audience (can be a user or listener identifier).
• audience_type: A string representing the type of audience (e.g., True Crime Enthusiast, Casual Listener).
• score: A numeric score based on user interaction or engagement.
• created_date: The timestamp when the record was created.

Step 3: Insert Sample Data ( In my real project that is hosted , the response is generated using CHATGPT API for each Podcast )

You can insert sample data into the podcast_audience table. Here’s an example SQL query to insert some data:

INSERT INTO podcast_audience (feed_id, apple_id, audience_type, score, created_date)
VALUES
(5024672, '1322200189', 'True Crime Enthusiast', 95, '2025-05-05 05:20:17'),
(5024672, '1322200189', 'Crime Story Aficionado', 90, '2025-05-05 05:20:17'),
(5024672, '1322200189', 'Podcast Listener', 85, '2025-05-05 05:20:17'),
(5024672, '1322200189', 'Law Enforcement Professional', 80, '2025-05-05 05:20:17');

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Project Setup and Usage

Step 1: Setting up PHP Scripts

This project includes several PHP files for handling database queries and generating suggestions. Below are the key files:

• index.php: This is the main entry point for interacting with the database. It includes functions to view, add, and update audience data.
• search_suggestions.php: This file contains the logic for generating suggestions based on user queries.
• type.php: Contains predefined types of audiences and functions to fetch the type of podcast user based on the audience segment.

Step 2: Setting up the Web Server

If you're using Apache, ensure that the following is configured:

1. Place all PHP files in the root directory of your web server.
2. Update the index.php file to connect to your database:

<?php
$host = 'localhost'; 
$dbname = 'podcast_system'; 
$user = 'root'; 
$pass = 'yourpassword'; 

try {
    $pdo = new PDO("mysql:host=$host;dbname=$dbname", $user, $pass);
    $pdo->setAttribute(PDO::ATTR_ERRMODE, PDO::ERRMODE_EXCEPTION);
} catch (PDOException $e) {
    echo "Connection failed: " . $e->getMessage();
}
?>

Step 3: Running the Application

Once your web server is set up:

1. Open the web browser and navigate to http://localhost/index.php to interact with the system.
2. Use the web interface to manage and view podcast audience data, as well as make search queries using the search_suggestions.php and type.php files.

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How This Project Can Be Used

1. Audience Segmentation: Use the podcast_audience table to segment users based on their interests (True Crime Enthusiast, Casual Listener, etc.).
2. Personalized Recommendations: By analyzing the scores and audience types, you can offer personalized podcast recommendations for users.
3. Analytics: Generate reports based on audience preferences, which can be used for marketing strategies or content development.
4. Dynamic Audience Updates: Update audience scores based on user interactions (listening patterns, likes, or feedback) to refine recommendations over time.

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Contributing

Feel free to fork the repository and contribute to the project. To contribute:

1. Fork the repository to your own GitHub account.
2. Clone the repository to your local machine.
3. Create a branch for your changes.
4. Make your changes and test them.
5. Submit a pull request.

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License

This project is open-source and available under the MIT License.

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