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Financial Marketing Analytics Data
Finance & Banking Analytics
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About
Bank marketing data is designed to assist with running marketing campaigns. It provides insights into how banks can select target customers to maximise the chances of selling their products. This information is particularly useful for building classification models that can predict whether a customer will subscribe to a product, such as a term deposit. The data includes various customer attributes, from demographics to their financial history and interactions with previous campaigns.
Columns
- age: The age of the customer.
- age group: The age range the customer falls into.
- eligible: Indicates if the customer is eligible for contact.
- job: The customer's occupation.
- salary: The customer's salary.
- marital: The customer's marital status.
- education: The customer's highest level of education.
- marital-education: A combined field showing marital status and education level.
- targeted: Shows if the customer was targeted in the campaign.
- default: Indicates if the customer has credit in default.
- balance: The customer's account balance.
- housing: Indicates if the customer has a housing loan.
- loan: Indicates if the customer has a personal loan.
- contact: The method of communication used to contact the customer.
- day: The day of the month when the customer was last contacted.
- month: The month of the year when the customer was last contacted.
- duration: The duration of the last contact in seconds.
- campaign: The number of contacts performed during this campaign for this client.
- pdays: The number of days that passed after the client was last contacted from a previous campaign.
- previous: The number of contacts performed before this campaign for this client.
- poutcome: The outcome of the previous marketing campaign.
- y: The final outcome variable, indicating if the client subscribed to the product.
- response: The customer's response to the campaign.
Distribution
The data is available as a single CSV file named
bank-marketing.csv, with a file size of 5.25 MB. It is a tabular dataset containing 45,200 records across 23 columns. The data is expected to be updated quarterly.Usage
This dataset is ideal for building a classification model to predict whether a customer will subscribe to a bank product. It can be used to understand the key factors that influence a customer's decision, enabling more effective targeting in marketing campaigns. Financial analysts and data scientists can use this data for customer segmentation, predictive analytics, and optimising marketing strategies.
Coverage
The dataset provides demographic and financial information about bank customers. It includes details like age, job, marital status, education, and financial standing such as salary, balance, and loan status. The customer ages range from 18 to 95. The data captures contact information over various days and months, with "May" being the most frequent month of contact.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers can use this to build and train predictive classification models.
- Marketing Analysts can analyse the data to identify key customer segments and improve campaign targeting.
- Financial Institutions can leverage the insights to understand customer behaviour and increase product subscription rates.
- Students and Academics can use it for research and educational purposes in the fields of finance, marketing, and data science.
Dataset Name Suggestions
- Bank Campaign Customer Prediction
- Financial Marketing Analytics Data
- Customer Subscription Prediction for Banks
- Bank Term Deposit Marketing Insights
Attributes
Original Data Source: Financial Marketing Analytics Data
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