Marketing Behavior Prediction Dataset

Dataset Overview 

This dataset includes detailed user interaction metrics, normalized for uniformity and ease of analysis. Its features enable a wide range of applications in machine learning, customer segmentation, and marketing behavior prediction.

Key Features of the Dataset

User Interaction Metrics

1. User_ID: Unique identifiers for each user (e.g., ‘001’, ‘002’, etc.).
2. Likes: The number of likes per user, normalized to a range between 0 and 1.
3. Shares: Indicates how often a user shares posts, normalized for analysis.
4. Comments: Tracks the number of comments made by the user, normalized for uniformity.
5. Clicks: Represents user clicks on posts, ads, or links, normalized to ensure comparability.

Advertising Engagement

• Engagement_with_Ads: Quantifies interaction with advertisements, normalized for consistency.

Platform Usage

• Time_Spent_on_Platform: Captures the amount of time (in minutes) each user spends on the platform, normalized for easier processing.

Purchase Behavior

• Purchase_History: Binary values indicating if a user has made a purchase (1 for Yes, 0 for No).

Text Features

• Text_Features: Includes textual interaction data with marketing content, transformed using TF-IDF (Term Frequency-Inverse Document Frequency) for keyword extraction and content analysis.

Target Variables

1. Engagement_Level: Categorical values classifying user engagement as “High,” “Medium,” or “Low.”
2. Purchase_Likelihood: Binary target variable predicting purchase likelihood:
   • 1 (Likely): User is predicted to make a purchase.
   • 0 (Unlikely): User is predicted not to make a purchase.

Advantages of the Marketing Behavior Prediction Dataset

1. Comprehensive User Insights
   Provides detailed metrics on user behavior, allowing for in-depth analysis of likes, shares, comments, and clicks.
2. Predictive Modeling
   Includes purchase likelihood and engagement levels as target variables, making it ideal for building predictive machine learning models.
3. Behavioral Segmentation
   Supports segmentation of users based on interaction patterns and engagement levels for targeted marketing strategies.
4. Optimized for Marketing Analysis
   Text features allow for sentiment analysis and content optimization, enabling marketers to identify trends and preferences.
5. Time-Efficient Research
   Normalized data ensures uniformity, reducing preprocessing time and effort for analysts.
6. Versatile Applications
   Useful for customer churn prediction, ad campaign evaluation, personalized recommendations, and more.

Applications of This Dataset

• Develop machine learning models for engagement prediction.
• Analyze ad performance and user interactions.
• Enhance marketing strategies through data-driven insights.
• Create personalized recommendations for improved user experiences.