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Travel Insurance Sales Forecast
Data Science and Analytics
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About
This dataset contains historical information on customers of a Tour & Travels Company, aiming to predict their interest in purchasing a travel insurance package, which includes Covid Cover. The data was collected from the performance and sales of an introductory insurance offering in 2019. The primary purpose is to enable the development of an intelligent model that can forecast which customers are likely to buy the travel insurance, based on their past behaviour and demographic details. This will facilitate customer-specific advertising and can help families save money by offering relevant insurance options, particularly for travel resuming after events like a corona lockdown.
Columns
- Age: The age of the customer.
- Employment Type: The specific sector in which the customer is employed (e.g., Private Sector/Self Employed, Government Sector).
- GraduateOrNot: Indicates whether the customer holds a college graduate qualification.
- AnnualIncome: The customer's yearly income in Indian Rupees, rounded to the nearest 50 thousand rupees.
- FamilyMembers: The total number of individuals in the customer's family.
- ChronicDisease: A boolean flag indicating if the customer suffers from any major chronic disease or condition, such as diabetes, high blood pressure, or asthma.
- FrequentFlyer: Derived data showing if the customer has booked air tickets on at least four different occasions within the last two years (2017-2019).
- EverTravelledAbroad: Denotes whether the customer has ever journeyed to a foreign country, irrespective of whether they used the company's services for that travel.
- TravelInsurance: The target variable, indicating whether the customer purchased the travel insurance package during its initial offering in 2019.
Distribution
The dataset is typically provided in a CSV file format (specifically
TravelInsurancePrediction.csv), with a file size of 115.41 kB. It comprises 10 distinct columns and contains data for 1987 of the company's previous customers. While specific row counts for all categories are available within the dataset's metadata, the total valid records are consistent across all columns at 1987.Usage
This dataset is ideal for:
- Developing intelligent machine learning models to predict customer interest in travel insurance.
- Performing exploratory data analysis to uncover interesting insights into customer behaviour and characteristics.
- Informing customer-specific advertising strategies for travel insurance packages.
- Forecasting potential insurance sales and customer demand once travel activities resume, for instance, after global events like the Corona lockdown.
Coverage
The dataset focuses on customer profiles and their purchasing behaviour related to travel insurance.
- Geographic Scope: While not explicitly stated as global, the inclusion of "Annual Income" in Indian Rupees and details about "Ever Travelled Abroad" suggests a primary focus on customers within India, with an interest in or history of international travel.
- Time Range: The sales data was extracted from the year 2019, specifically relating to an introductory insurance offering. The 'FrequentFlyer' attribute covers booking history from 2017 to 2019. The models built using this data are intended for future predictions, such as post-lockdown travel.
- Demographic Scope: The dataset includes a diverse range of customer demographics, encompassing various ages (e.g., 25-35 years old), employment types, education levels, annual incomes (from 300,000 INR to 1,800,000 INR), family sizes (from 2 to 9 members), health conditions, and travel habits.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly useful for:
- Data Scientists and Machine Learning Engineers: For building and testing predictive models, such as classification models, to identify potential travel insurance buyers.
- Business Analysts and Marketing Teams: To understand customer segmentation, identify target audiences for insurance products, and optimise marketing campaigns.
- Researchers: To study consumer behaviour in the travel and insurance sectors.
Dataset Name Suggestions
- Travel Insurance Prediction
- Customer Insurance Interest
- Travel Insurance Sales Forecast
- COVID Travel Insurance Propensity
- Customer Travel Risk Assessment
Attributes
Original Data Source: Travel Insurance Sales Forecast
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