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Retail Service Booking and Loyalty Analytics
Retail & Consumer Behavior
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About
Maintaining reliable attendance records is vital for service-based enterprises like salons, where missed slots directly impact revenue. By tracking customer booking habits, staff assignments, and historical behaviour, businesses can identify patterns that lead to no-shows. This information enables the development of predictive models to forecast whether a client will honour their appointment or fail to appear. It covers a variety of factors, including time of booking, specific service categories like styling or colouring, and cumulative customer loyalty metrics, making it a valuable tool for improving scheduling efficiency and operational planning.
Columns
- noshow: A binary indicator where 0 represents the customer attended and 1 indicates they did not show up.
- book_tod: The time of day when the appointment was scheduled, such as morning, afternoon, or evening.
- book_dow: The specific day of the week the appointment was booked for.
- book_category: The type of service requested, often categorised into groups like STYLE or COLOR.
- book_staff: The specific staff member assigned to handle the booking.
- last_category: The service category requested during the client's previous visit.
- last_staff: The staff member who served the client during their previous appointment.
- last_day_services: The total number of services the client received on their last visit.
- last_receipt_tot: The total monetary amount spent by the customer during their last appointment.
- last_dow: The day of the week on which the last appointment occurred.
- last_tod: The time of day recorded for the previous appointment.
- last_noshow: A flag indicating if the client missed their previous scheduled visit.
- last_prod_flag: A binary marker showing if the customer purchased a retail product during their last visit.
- last_cumrev: The total cumulative revenue generated by the customer over their entire history.
- last_cumbook: The total number of appointments the client has ever booked.
- last_cumstyle: The total count of STYLE-related services the client has received.
- last_cumcolor: The total count of COLOR-related services the client has received.
- last_cumprod: The total number of products the client has purchased over time.
- last_cumcancel: The total number of times the client has cancelled an appointment.
- last_cumnoshow: The total number of times the client has previously failed to show up.
- recency: The number of days that have passed since the customer's most recent appointment.
Distribution
The data is provided in a CSV format titled
whether_client_appeared_record.csv with a file size of 145.13 kB. It contains 1,952 records. While many fields like the booking day and category are fully populated, certain historical features regarding the "last" appointment have a missing data rate of approximately 49% to 56%. Despite these gaps, the resource maintains a high usability score of 10.00 and is updated on an annual basis.Usage
This collection is ideal for binary classification tasks aimed at predicting customer attendance. It is well-suited for exploratory data analysis to find correlations between specific staff members and no-show rates, or to see if certain service categories are more prone to cancellations. Businesses can use these insights to implement targeted reminder systems or loyalty rewards for consistent attendees.
Coverage
The scope is focused on the customer base of a service-oriented business, such as a hair or beauty salon. The data includes temporal details across all seven days of the week and various times of the day. A significant portion of the data tracks long-term customer demographics and cumulative habits, though users should note the high frequency of missing values in the historical "last appointment" fields which may affect longitudinal analysis for some clients.
License
CC0: Public Domain
Who Can Use It
Data scientists can leverage this information to build and refine machine learning models for churn and no-show prediction. Salon owners and business managers may use the findings to optimise staff schedules and reduce wasted time slots. Additionally, students can use the records to practice handling missing data and performing feature engineering on real-world business metrics.
Dataset Name Suggestions
- Service Appointment No-Show Predictor
- Salon Customer Attendance and Behaviour Registry
- Retail Service Booking and Loyalty Analytics
- Client Appointment Attendance History
- Service Business No-Show Classification Archive
Attributes
Original Data Source: Retail Service Booking and Loyalty Analytics
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