Airline Loyalty Program Analysis

This project was accomplished using Microsoft PowerBI.

The original data can be found in the data folder

Steps taken with PowerBI;

• Data loading and transformation using PowerQuery
  • Changing data types of several columns
  • Replaced values in the Month column; changing numerical representation to string representation, 7 to "July".
• Creation of data tables to initially understand the dataset. This established the foundation for creating the data visuals as seen below.
• Creation of calculated measures, using the DIVIDE, CALCULATE and SUM functions such as;
  • %_accumulated_points_July:

  %_accumulated_points_July = 
  DIVIDE(
  CALCULATE(
      SUM(customer_flight_activity[Points Accumulated]),
      'customer_flight_activity'[Month] = "July"
  ),
  [total_points],
  0
  ) * 100 
  

• Creation of calculated Date table based on Months using the DATATABLE function;
  • Datetable:

  DateTable = DATATABLE(
  "Month",STRING,{
      {"January"},
      {"Feburary"},
      {"March"},
      {"April"},
      {"May"},
      {"June"},
      {"July"},
      {"August"},
      {"September"},
      {"October"},
      {"November"},
      {"December"}
  }
  )
  

• Creation of model relationships;
  • One to many relationship between; customer_loyalty_history with customer_flight_activity, using the LOYALTY NUMBER column.
  • One to many relationship between; customer_flight_activity with DateTable, using the MONTH column.
• Plotting of visuals which were uniform and easy on the eyes and straightforward for viewing by the audience.
• Drawing observations and conclusions from visualisations.

Please kindly check airline_final.pbix for the powerbi file in the powerbi_analysis folder.

Some images from PowerBI

Conclusion

• From the observations as shown within the visuals, it would seem that the 2018 promotions played a major factor with the increased number of customers in the year 2018.