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Evaluate launching a vegan burger

Capital OneData Scientist · Technical Screen
HardData ScientistTechnical ScreenAnalytics & Experimentation

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Capital One data-scientist technical-screen case: a rival launched a hit vegan burger -- should your fast-food chain add one, and how would you test it? It exercises pricing and willingness-to-pay/elasticity, operational readiness and capacity, cannibalization vs. incrementality, marketing and labeling, success metrics with guardrails, and a matched store-level RCT (difference-in-differences, CUPED, power) feeding an explicit go/no-go rule.

Question

You run a fast-food burger chain, and a rival has just launched a hit vegan burger. Would you add a vegan burger to your own menu? Lay out the decision framework, then design the experiment you would run to make the call. Cover the following:

  1. Pricing and willingness-to-pay. How would you set the price (value-based vs. competitive, target margin)? Estimate willingness-to-pay and own-/cross-price elasticity (e.g. survey methods like Van Westendorp or Gabor–Granger, conjoint, and an in-market price test across matched stores).
  2. Operational readiness. Assess prep time and line capacity (time-and-motion study, bottleneck/throughput analysis), crew training and SOPs, cross-contamination / food-safety controls, and supplier risk (sourcing, fill-rate SLAs, safety stock).
  3. Capacity constraints. Model station utilization (grill, fryer, assembly) at peak with the new SKU; identify bottlenecks and mitigations before scaling.
  4. Competitive landscape and differentiation. Size the addressable demand and segments (vegans/vegetarians, flexitarians, health- and sustainability-minded), map competitor offerings, and define how you would differentiate.
  5. Demand forecasting: cannibalization vs. true incrementality. Forecast demand and separate net-new traffic from substitution away from your existing beef/chicken burgers and sides.
  6. Marketing implications. Target segments, messaging (taste-first vs. sustainability), channels, and launch creative/offer variants to test.
  7. Regulatory and labeling. Allergen labeling (soy, gluten, pea protein), nutrition disclosure, "vegan" vs. "plant-based" claims, and shared-equipment / cross-contact disclaimers.
  8. Success metrics. Define a primary metric (e.g. incremental gross profit / contribution margin per store-week) plus guardrails (throughput/service time, order accuracy, NPS/CSAT, stockouts, waste, food-safety incidents).
  9. Experiment design. Specify a geo-split or store-level randomized test: matched test vs. control stores, randomization, duration, KPIs, success thresholds, the estimator (difference-in-differences with variance reduction), and a sample-size / power calculation.
  10. Go / no-go decision rule. State the explicit rule that turns the experiment readout into a launch, iterate, or stop decision, and name the key risks and mitigations.