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Customer Conversion Analysis for Banking
Finance & Banking Analytics
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About
Data gathered from a Portuguese banking institution's direct marketing campaigns focused on term deposit subscriptions. This resource is designed to help analysts and modellers identify characteristics that make a customer more or less likely to subscribe to the term deposit product. By analysing demographic data, financial history, and detailed metrics related to telephonic contact duration and frequency, the ultimate goal is to maximise conversion rates and optimise future marketing efforts.
Columns
The material provides detailed information about potential customers and their interaction with the bank's marketing efforts. Key columns include:
- age: The age of the customer (Integer).
- job: The type of employment held by the customer (String).
- marital: The marital status of the customer (String).
- education: The education level of the customer (String).
- default: Indicates whether the customer has a credit default (Boolean).
- balance: The balance of the customer's bank account (Integer).
- housing: Indicates whether the customer has a housing loan (Boolean).
- loan: Indicates whether the customer has a personal loan (Boolean).
- contact: The method used to contact the customer (String).
- day: The day of the month the customer was last contacted (Integer).
- month: The month of the year the customer was contacted (String).
- duration: The length of the call with the customer (Integer).
- campaign: The total number of contacts performed during this campaign and previous ones (Integer).
- pdays: The number of days elapsed since the customer was last contacted from a prior campaign (Integer).
- poutcome: The result of the previous marketing campaign (String).
- y: The target variable indicating whether the customer subscribed to the term deposit (Boolean).
Distribution
The data is organised into separate files, typically including
train.csv and test.csv for development and testing of predictive models. The files are generally provided in a CSV format. The test.csv file, for instance, contains 4,521 records across 17 distinct columns. Specific overall row counts for the entire collection are not explicitly detailed, but structure and format are consistent across the provided segments.Usage
This resource is extremely useful for building classification and predictive modelling solutions. It is recommended for running machine learning algorithms, such as Random Forest or Decision Tree models, to accurately forecast which customers possess the highest probability of conversion. It can also be leveraged to segment customers into groups with similar profiles, enabling the development of highly targeted campaigns and better allocating marketing resources for cost efficiency. Regression models can also be applied to assess which characteristics are most strongly correlated with high subscription rates.
Coverage
The material captures specific data points gathered from the direct marketing campaigns of a Portuguese banking institution. The scope includes individual selection characteristics such such as age, job type, marital status, education level, and financial particulars like balances and loan status. The temporal scope includes details of call day and month. The geographic and institutional scope is limited to the customers engaged by this specific banking entity in Portugal.
License
CC0- Public Domain
Who Can Use It
- Data Scientists: Utilising classification models to predict customer subscription likelihood.
- Marketing Strategists: Identifying optimal customer segments for term deposit offers to maximise return on investment.
- Financial Analysts: Understanding customer engagement patterns with banking services and predicting future tendencies towards subscribing to specific financial products.
Dataset Name Suggestions
- Portuguese Bank Term Deposit Prediction
- Customer Conversion Analysis for Banking
- Direct Marketing Outcome Predictor
- Bank Telemarketing Success Data
Attributes
Original Data Source: Customer Conversion Analysis for Banking
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