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Movie Theatre Ticket Sales Analysis
E-commerce & Online Transactions
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Free
About
Ticket sales and customer behaviour at a cinema hall is recorded, offering insights into various aspects such as demographics, movie genre preferences, seat selection, ticket pricing, and customer retention patterns. It is designed to help analyse customer engagement, spending behaviour, and factors that influence repeat visits to the cinema. The data is useful for predictive modelling and can support decision-making processes related to customer retention, marketing strategies, and optimising cinema operations.
Columns
- Ticket_ID (Categorical): A unique alphanumeric identifier for each ticket purchase, consisting of a random uppercase letter followed by a 4-digit number (e.g., B7539). It is essential for tracking specific customer purchases.
- Age (Numerical): The age of the customer who purchased the ticket, ranging from 18 to 60 years. This provides insights into the needs of various age groups.
- Ticket_Price (Numerical): The price the customer paid for the ticket, typically ranging from $10 to $25. The price varies based on factors like movie time or seat type.
- Movie_Genre (Categorical): The genre of the movie the customer attended, including Action, Comedy, Horror, Drama, or Sci-Fi.
- Seat_Type (Ordinal): The type of seat selected, with three categories: Standard (basic), Premium (enhanced comfort), and VIP (exclusive features).
- Number_of_Person (Mixed): The number of people accompanying the customer, which can be 'Alone' or a number from 2 to 7.
- Purchase_Again (Binary): A target variable indicating if the customer is likely to return, with 'Yes' or 'No' values. This is key for predicting customer retention.
Distribution
The dataset is provided in CSV format. It contains 1440 records and 7 columns, with no missing values. The file size is 52.02 kB.
Usage
- Customer Segmentation: Analyse variables like Age, Movie_Genre, and Seat_Type to identify different customer segments for personalised marketing.
- Customer Retention Analysis: Use the Purchase_Again column to identify factors influencing customer loyalty and repeat visits.
- Pricing Strategy Optimisation: Analyse Ticket_Price in relation to Seat_Type and Purchase_Again to understand how pricing influences customer behaviour and loyalty.
- Predictive Modelling: Build machine learning models using the Purchase_Again column as the target variable to forecast the likelihood of future ticket purchases.
Coverage
The data covers customer demographics with ages ranging from 18 to 60. It includes movie genres such as Action, Comedy, Horror, Drama, and Sci-Fi. The dataset does not have a specified geographic scope or time range and is not expected to be updated.
License
CC0: Public Domain
Who Can Use It
- Data Analysts: To analyse customer preferences, purchasing habits, and retention patterns.
- Marketers: To develop personalised marketing campaigns, ticket discounts, and loyalty programs based on customer segmentation.
- Cinema Managers: To optimise cinema operations, movie scheduling, and pricing strategies to maximise revenue and improve customer experience.
Dataset Name Suggestions
- Cinema Customer Behaviour & Retention
- Movie Theatre Ticket Sales Analysis
- Cinema Patron Demographics and Preferences
- Predicting Cinema Customer Loyalty
Attributes
Original Data Source: Movie Theatre Ticket Sales Analysis
QUALITY