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Challenge 8 - Airline Customer Loyalty

<p><strong>Challenge 8: Airline Customer Loyalty</strong></p><p><strong>Level:</strong> Easy</p><p><strong>Description:</strong> You work for a Canadian airline company that wants to better understand its most loyal customers. Before creating personalized offerings for this group, the company needs to uncover their demographics from the available data. As a Data Scientist, your task is to identify how these loyal customers are distributed across different demographic segments—such as gender, marital status, and so on—in each city. Who are the most loyal customers and how are they segmented in different locations? <strong>Hint 1:</strong> You can use loyalty points for calculating a basic loyalty score or combine multiple factors to create a more comprehensive one.<br><br><strong>Dataset</strong>: Airline Loyalty Campaign Program<br><br><strong>Beginner-friendly objectives:</strong> 1. Calculate Loyalty for each user; 2. Extract the most loyal users along with their information; 3. Find out how these customers are distributed in different demographic groups in each city.</p>

Challenge 8: Airline Customer Loyalty


Level: Easy

Description: You work for a Canadian airline company that wants to better understand its most loyal customers. Before creating personalized offerings for this group, the company needs to uncover their demographics from the available data. As a data scientist, your task is to identify how these loyal customers are distributed across different demographic segments—such as gender, marital status, and so on—in each city. Who are the most loyal customers and how are they segmented in different locations? Hint 1: You can use loyalty points for calculating a basic loyalty score or combine multiple factors to create a more comprehensive one.

Beginner-friendly objectives: 1. Calculate Loyalty for each user; 2. Extract the most loyal users along with their information; 3. Find out how these customers are distributed in different demographic groups in each city.

customer flight activity
CSV Reader
loyalty history
CSV Reader
"Loyalty" per "Gender"
Pivot
group by the "Loyalty Number"
GroupBy
calculate difference between "Points Accumulated" and "Points Redeemed"
Expression
90th percentile of"Loyalty" column
GroupBy
missing values in "Cancellation Month" and "Cancellation Year"
Row Filter
"Loyalty" per "Marital Status"
Pivot
"Loyalty" >= 0.0
Row Filter
"Loyalty" per "Education"
Pivot
Joiner
Joiner
"Loyalty" value is in the top 10%
Expression Row Filter
Column Appender
Column Filter

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