Example: Excess Airline Baggage Charges
Data analysis is a key part of all research. It anchors down the “What” to inform the “Why”. A fun deep dive into the data analytics was in planning an observational research trip to view passengers being charged for excess baggage by our Departure Gate Sales App. We needed to find a flight route with a high turnover of charges. This would favour our chances in seeing the app in action. To do this we reviewed the departure baggage charges data gathered over a 3 month period from a single Airline. Out of the higher sales revenue routes that were identified, we selected a Bristol flight that flies every Friday with an average of 3 to 4 bag charges per route.
Goals
To analyse an data detailing all Departure Gate Sales Baggage Charges made during Aug-Oct 2019 from a chosen Airline (281,250 charges in total)
To identify what is the best flight to perform our observational research for the Gate Sale app
To gather information on sales patterns (time, location, date)
To analyse flights that have the highest and lowest generated revenue
Key Findings
Majority of passengers was charged €25 for a 10kg checked bag
The most revenue generating flights were scheduled for early morning/afternoon and near the weekend.
Highest revenue generating flight was one between Germany and Spain
Highest revenue generating Airport was London Stansted
Thursday was the highest revenue generating day of the week
Next Steps
To select a flight route and date to run our observational research at the departure gates
To use the data to build more validated Journey Maps and Passenger/User Persona's