Introduction
For Domino's Pizza's loyalty program, we're facing a critical trade-off between offering more frequent but smaller rewards to increase engagement or larger, less frequent rewards to drive higher-value purchases. This decision will significantly impact customer behavior, retention, and overall program success. I'll analyze this trade-off by examining the loyalty program's structure, potential impacts on key metrics, and experimental approaches to validate our hypotheses.
I'd like to start by asking a few clarifying questions to ensure we're aligned on the context and objectives of this decision. Then, I'll walk you through my analysis framework, including product understanding, hypothesis formation, metrics identification, experiment design, and decision-making process. Does this approach work for you?
Step 1
Clarifying Questions (3 minutes)
Why it matters: Helps align the program design with broader business objectives Expected answer: Critical for customer retention in a highly competitive market Impact on approach: Would influence the balance between short-term engagement and long-term customer value
Why it matters: Informs the potential impact of reward frequency on different customer behaviors Expected answer: Varied purchase patterns across casual, regular, and power users Impact on approach: Would help tailor reward structures to maximize impact on each segment
Why it matters: Determines the feasibility of implementing more complex reward structures Expected answer: Moderate flexibility with some limitations Impact on approach: Would influence the complexity of reward options we can consider
Why it matters: Ensures successful implementation and adoption of any program modifications Expected answer: Limited additional resources available Impact on approach: Would need to prioritize high-impact, low-resource changes
Why it matters: Aligns program changes with broader business initiatives Expected answer: Aim for implementation before the holiday season Impact on approach: Would impact the scope and timeline of our experiment and rollout plan
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