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Simulated User Marketing Engagement
Data Science and Analytics
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About
Simulated marketing interaction data for 500 users is available, featuring a variety of engagement metrics and user behaviour features. This data is designed to help understand and predict consumer actions, offering valuable insights for marketing analytics and strategy development. The text-based features have been processed using TF-IDF to isolate important keywords from user interactions.
Columns
- User_ID: A unique identifier for each user.
- Likes: The number of likes a user has given, normalised to a range of 0 to 1.
- Shares: The number of times a user has shared posts, normalised to a range of 0 to 1.
- Comments: The number of comments a user has made, normalised to a range of 0 to 1.
- Clicks: The number of times a user has clicked on content, normalised to a range of 0 to 1.
- Engagement_with_Ads: The user's level of interaction with advertisements, normalised to a range of 0 to 1.
- Time_Spent_on_Platform: The amount of time in minutes a user spends on the platform, normalised to a range of 0 to 1.
- Purchase_History: A binary value indicating if a user has made a purchase (1 for yes, 0 for no).
- Text_Features: Text data from user interactions with marketing content, transformed using TF-IDF.
- Engagement_Level: A categorical value classifying user engagement as High, Medium, or Low.
- Purchase_Likelihood: A binary target variable predicting the likelihood of a purchase (1 for Likely, 0 for Unlikely).
- brand: (No description provided in sources).
- buy: (No description provided in sources).
Distribution
The data is provided in a single CSV file named
marketing_data new.csv, with a size of 66.54 kB. It contains data for 500 users across 12 columns. All records appear to be valid with no missing or mismatched data.Usage
This dataset is ideal for developing and testing predictive models for customer behaviour. Key applications include building machine learning models to forecast purchase likelihood, analysing user engagement patterns, and segmenting customers based on their interactions. It can also be used for data visualisation to uncover trends in marketing campaign performance.
Coverage
The dataset is a simulation and does not represent a specific geographic location, time period, or demographic group. It contains simulated behavioural data for 500 users.
License
CC0: Public Domain
Who Can Use It
- Data Scientists can use this dataset for building and validating predictive models related to marketing outcomes.
- Marketing Analysts can analyse the data to understand engagement drivers and segment audiences for targeted campaigns.
- Machine Learning Engineers can leverage it for feature engineering and developing recommendation systems.
- Students and Researchers can use it for projects in data analytics, consumer behaviour, and predictive modelling.
Dataset Name Suggestions
- Simulated User Marketing Engagement
- Consumer Behaviour and Purchase Prediction Data
- Digital Marketing Interaction Simulation
- User Engagement and Conversion Analytics
Attributes
Original Data Source: Simulated User Marketing Engagement
QUALITY