Decide and test Groupon program incrementality

Before you sign, list the concrete factors and metrics you would assess when deciding whether to participate in the coupon program. Include at least: capacity utilization relative to 20 tables/day, expected incrementality (new versus cannibalized tables), average check lift or erosion, daypart mix, repeat purchase rate among coupon users, service/operations risks, and any guardrail metrics.

Next, build a practical experiment to estimate the incremental profit impact. Select the randomization unit (tables, customers, days, or geos), define treatment and control, set daily profit as the primary KPI, identify key secondary metrics, outline the sample size/duration approach, and specify a quantitative decision rule.

Finally, use the following numbers to derive the minimum share of coupon-using tables that must be incremental (not cannibalized from the 20-table baseline) for the program to be at least profit-neutral: baseline of 20 tables/day at $30/table, variable costs equal to 40% of revenue, fixed costs equal to $100/day. With the program, you observe 25 tables/day, 10 coupon tables, an overall average check of $36/table, and a 40% commission applied only to coupon tables. State and justify any additional assumptions you make.

This question assesses experimental design, causal inference, uplift and profitability modeling, operational risk evaluation, and KPI-driven decision rules in the analytics and experimentation domain for data scientists. It tests both conceptual understanding and practical application of A/B testing, sample-size considerations, and guardrail metrics. The typical goal is to see whether a candidate can translate business constraints into a rigorous experiment and a profit-oriented analysis, weighing incremental impact estimates, cannibalization concerns, and operational trade-offs to support go/no-go decisions.