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Cafe Rewards Member Behaviour Simulation
Data Science and Analytics
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Free
About
Simulated data tracks the behavior of members of a Cafe Rewards programme over a 30-day duration. This resource provides valuable insights into customer interactions with various offers and is suitable for detailed business analysis regarding response rates and demographic segmentation. The data quality is rated highly at 10.00, ensuring reliability for analytical purposes.
Columns
- customer_id: A unique 17,000-count identifier assigned to each rewards member.
- became_member_on: The date on which the customer joined the rewards programme, detailing enrollment over a period spanning from 2013 through to 2018.
- gender: The reported gender of the customer, primarily Male (50%) and Female (36%), along with an 'Other' category. Approximately 13% of records have missing gender values.
- age: Customer age, which ranges from 18 to 118.
- income: The customer's annual income, distributed between £30,000 and £120,000. Similar to gender, 13% of records are missing income data.
Distribution
The data is delivered in a single CSV file,
customers.csv, which measures 908.28 kB in size. The structure includes 5 distinct columns and 17,000 valid customer records. This is a static resource and has an expected update frequency of 'Never'.Usage
This dataset is ideally suited for marketing analysts, data scientists, and researchers. Uses include calculating how many reward offers were completed, identifying the offers with the highest success rates, determining how many informational offers resulted in subsequent customer transactions, and studying the overall distribution of customer demographics.
Coverage
The data simulates member behavior over a specific 30-day period. Customer membership dates primarily cover the range between 2013 and 2018. The demographic scope is extensive, featuring age, income, and gender data, although users should account for the 13% rate of missing values within the income and gender columns.
License
CC0: Public Domain
Who Can Use It
- Marketing Strategists: To explore relationships between customer demographics and specific offer completion patterns, allowing for better campaign targeting.
- Business Intelligence Professionals: To map customer demographics and their distribution within the rewards programme.
- Data Analysts: To answer key business questions regarding offer effectiveness and customer loyalty performance metrics.
Dataset Name Suggestions
- Cafe Rewards Member Behaviour Simulation
- 30-Day Customer Offer Response Data
- Loyalty Programme Demographic Analysis
- Customer Rewards Simulation Data
Attributes
Original Data Source: Cafe Rewards Member Behaviour Simulation
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