For Breeze Airways's "Nice" and "Nicer" fare options, how should we optimize the price difference to maximize both customer choice and overall revenue?

Introduction

The core challenge we're addressing is optimizing the price difference between Breeze Airways's "Nice" and "Nicer" fare options to maximize both customer choice and overall revenue. This scenario involves balancing customer preferences with revenue goals, considering factors like price sensitivity, perceived value, and market positioning. I'll approach this by analyzing the trade-off type, understanding the product ecosystem, identifying key metrics, designing experiments, and providing a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming this is a relatively new initiative for Breeze Airways. Could you provide some background on why the company is considering optimizing these fare options now?

Why it matters: Understanding the timing helps prioritize this initiative against other business objectives. Expected answer: Recent market research or competitor moves have prompted this review. Impact on approach: Would influence the urgency and resources allocated to this optimization.

• Business Context: Based on Breeze's low-cost carrier model, I'm thinking revenue per available seat mile (RASM) is a critical metric. How does the current split between "Nice" and "Nicer" fares impact our overall RASM?

Why it matters: Helps quantify the current revenue impact of the two fare types. Expected answer: "Nicer" fares contribute disproportionately to RASM. Impact on approach: Would guide the balance between maximizing "Nicer" fare sales and overall ticket volume.

• User Impact: I'm curious about our customer segmentation. Do we have data on which customer segments typically choose "Nice" vs. "Nicer" fares?

Why it matters: Informs how price changes might affect different user groups. Expected answer: Business travelers prefer "Nicer," leisure travelers choose "Nice." Impact on approach: Would help tailor pricing strategies to specific segment needs and willingness to pay.

• Technical: Considering our pricing system, how flexible are we in implementing dynamic price differences between these fare types?

Why it matters: Determines the feasibility of sophisticated pricing strategies. Expected answer: Current system allows for some flexibility, but has limitations. Impact on approach: Would influence the complexity of pricing models we can test and implement.

• Resource: What's our current capacity for analyzing customer behavior and price elasticity across different routes and seasons?

Why it matters: Affects our ability to conduct detailed analysis and implement nuanced pricing strategies. Expected answer: Limited data science resources available, but prioritized for this project. Impact on approach: Would determine the depth and breadth of analysis possible in our optimization efforts.