2024: Week 8 - Prep Air Loyalty

Challenge by: Jenny Martin

For this week's challenge, Prep Air have asked for some What If? Analysis. They're considering 2 different systems for rewarding customer loyalty and want to understand how that might impact cost and how many customers might benefit from the program.

Inputs

  1. Prep Air Customer Details - the team were able to add additional information to Week 5's input 

  2. Prep Air Loyalty schemes 

  3. Prep Air Loyalty Costings 

Requirements

  • Input the data
  • To be part of either Prep Air Loyalty Scheme, Customers must have flown in the last year (on or after 21st February 2023)
  • Create a parameter so that the number of flights a customer has taken is either bucketed into groups of 5 or groups to 10
    • e.g. if the parameter selected is 5, the groupings will be 1-4, 5-9 etc
    • if the parameter selected is 10, the groupings will be 1-9, 10-19 etc
  • Create a field to categorize customers based on the selected parameter, called Tier
  • Estimate the average number of flights a customer takes per year 
  • Filter the Prep Air Loyalty dataset to the selected parameter value
  • Join the Prep Air Loyalty to the Customer dataset in a way that each customer also experiences the benefits of lower Tiers
    • e.g. a Tier 2 customer gets all the benefits of Tier 0, Tier 1 and Tier 2 
  •  Split out the Benefits and make sure that each Benefit has its own row
  • Join on the Costing dataset
  • Calculate the Yearly Cost of each Benefit
    • e.g. if the Benefit Cost is per flight then make sure to multiply it by the Avg Number of Flights that customer takes in a year
  • Total up the Yearly Cost for each Tier and count the Number of Customers in Each Tier
  • Output the data

Outputs

5 Tier Grouping Output

  • 3 fields
    • Tier
    • Year Cost
    • Number of Customers
  • 6 rows

10 Tier Grouping Output

  • 3 fields
    • Tier
    • Year Cost
    • Number of Customers
  • 3 rows
After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

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