Decide whether to partner with Groupon

# Solution ## 1) Economics, thresholds, and decision criteria First, quantify per-coupon-table contribution. Definitions: - a = average incremental spend per coupon table beyond the $30 voucher value (paid at full price by the guest). - s_f = average full-price table spend (assume $40 for examples; adjust for your restaurant). - m_f = margin per full-price table = 0.6 × s_f (because VC = 40%). If s_f = $40, m_f = $24. Cash flows for a coupon table: - Cash in from Groupon = 60% of $15 = $9 - Guest overspend beyond voucher = a (at full price) - Variable cost = 40% of total served value = 0.4 × (30 + a) = 12 + 0.4a Per-coupon contribution (before fixed cost): - CM_coupon = (9 + a) − (12 + 0.4a) = −3 + 0.6a Implications and thresholds: - Break-even on the visit: −3 + 0.6a ≥ 0 ⇒ a ≥ $5 - Useful scenario checkpoints: - a = $0 ⇒ −$3 loss - a = $5 ⇒ $0 - a = $10 ⇒ $3 contribution - a = $15 ⇒ $6 contribution - a = $20 ⇒ $9 contribution Because one coupon may replace a full-price table during peak times, opportunity cost matters. If a coupon table displaces a full-price table, the net effect is: - Net incremental contribution if displaced = CM_coupon − m_f = (−3 + 0.6a) − 0.6 s_f - With s_f = $40 (m_f = $24) and a = $15, net = $6 − $24 = −$18 (bad during peak). Decision criteria and numeric thresholds: - Minimum check requirement to guarantee contribution: - Want CM_coupon ≥ c (target contribution per coupon table) - −3 + 0.6a ≥ c ⇒ a ≥ (c + 3) / 0.6 ⇒ required minimum total check X_min = 30 + a - Examples: - For break-even c = $0: a ≥ $5 ⇒ X_min ≥ $35 - For c = $6: a ≥ $15 ⇒ X_min ≥ $45 - For c = $10: a ≥ $21.67 ⇒ X_min ≈ $52 - Explicit threshold (recommended): - Minimum check to redeem: X_min ≥ $45 (ensures a ≥ $15 and CM_coupon ≈ $6) - Beverage/upsell attach: ≥ 60% of coupon tables purchase at least one beverage/dessert; target overspend a ≥ $15 - Coupon mix cap and daypart rules: - Max coupon mix: ≤ 20% of daily covers; ≤ 10% on shoulder periods; 0% Fri–Sat 6–9pm - Allowed dayparts: Mon–Thu lunch, early (before 6:30pm) and late (after 8:30pm) dinner; blackout peak windows - Daily voucher redemptions cap: ≤ 10 per day (tune to fill slack only) - Cannibalization and incrementality: - Peak-time cannibalization target: ≤ 5% of coupon redemptions occur in peak windows (proxy for displacement) - If cannibalization is unavoidable, require higher a: solve −3 + 0.6a − 0.6 s_f ≥ 0 ⇒ a ≥ s_f + 5. For s_f = $40, a ≥ $45 (unrealistic). Hence strict blackout of peaks is essential. - Return/LTV requirement (to justify discount marketing): - Let r = expected number of full-price return visits per coupon table within 90 days (incremental vs. baseline) - Program-level viability: CM_coupon + r × m_f ≥ 0 - Required r ≥ max(0, (3 − 0.6a) / m_f) - With s_f = $40 (m_f = $24): - If a = $5: r ≥ 0 (already breakeven) - If a = $10: r ≥ (3 − 6)/24 = 0 (already positive) - If a = $0: r ≥ 3/24 = 0.125 ⇒ need ≥ 12.5% of coupon parties to return once at full price - Explicit threshold (recommended): ≥ 15% of coupon parties return for at least 1 full-price visit within 90 days at average check ≥ $40 - Operational guardrails: - Average ticket time and service KPIs must remain within ±10% of baseline - Average rating (Google/Yelp/Toast) must not drop >0.2 stars during the pilot window Summary of explicit thresholds to run the program: - Minimum check to redeem: ≥ $45 (implies a ≥ $15; CM ≈ $6) - Coupon mix: ≤ 20% of daily covers; 0% Fri–Sat 6–9pm; cap ≤ 10/day - Return behavior: ≥ 15% return within 90 days at full price (avg check ≥ $40) - Service/brand: no more than −0.2 star rating change; ops KPIs within ±10% ## 2) Top risks and mitigation tactics - Capacity constraints and peak-time displacement - Risk: Coupon tables replace high-margin full-price tables - Mitigations: Peak blackout windows; tight daily caps; reservation controls; require minimum check ≥ $45 to raise per-table contribution - Cannibalization of full-price guests - Risk: Existing customers use coupons instead of paying full price - Mitigations: New-customer targeting (geo-fencing, first-time buyers); loyalty members excluded; enforce first-time use only; limit frequency per customer - Insufficient overspend (a too low) - Risk: −3 + 0.6a < 0 per coupon table - Mitigations: Minimum spend to redeem ($45+); pre-bundled upsell menus (prix fixe with appetizer or beverage); server incentives for attachments - Brand dilution and discount anchoring - Risk: Perceived as a “discount” brand; lowers willingness to pay - Mitigations: Limited-time/limited-quantity offers; emphasize tasting menu/chef’s special; restrict creative to off-peak “try us” messaging; rotate off the platform seasonally - Operational strain (service times, food cost variance) - Risk: Slower turns, waste, stockouts - Mitigations: SKU-constrained menu for coupon redemptions; prep planning by daypart; limit party size; training and pacing controls - Economics/contract rigidity - Risk: 40% commission yields base loss at low a - Mitigations: Negotiate lower commission (target 25–30%); move from $30-for-$15 to “$45 for $25” structure to lift a; push minimum spend clause into terms; limited comped items (exclude premium SKUs) ## 3) Exec-ready recommendation Decision: Conditional GO via a 4–6 week off-peak pilot with strict guardrails. NO-GO if guardrails cannot be contractually enforced. Key assumptions that must hold: - Minimum redeemable check ≥ $45 (a ≥ $15) producing CM ≈ $6 per coupon table - Daypart controls prevent peak-time displacement (≤ 5% redemptions in peak) - Return behavior ≥ 15% of coupon parties return once at full price within 90 days (s_f ≈ $40, m_f ≈ $24) - Service/brand metrics stable (rating change ≥ −0.2 stars triggers review) Illustrative daily math (pilot target): - 10 redemptions/day, a = $15 ⇒ CM ≈ $6 each ⇒ ≈ $60/day contribution - If 15% return at full price, added expected contribution ≈ 10 × 0.15 × $24 = $36/day - Total incremental ≈ $96/day during off-peak periods (incremental vs. near-zero opportunity cost). If any of these occur during peak and displace 5 full-price tables (m_f ≈ $24), lost margin ≈ $120 wipes out gains ⇒ hence strict blackout is mandatory. Contingency triggers (pause or scale): - Pause if any for 2 consecutive weeks: average a < $10; peak-time redemptions > 5%; return rate < 10%; rating −0.2 stars or worse; ops KPIs drift > 10% - Scale up (increase daily cap to 15 and/or add dayparts) if for 2 consecutive weeks: a ≥ $15; return rate ≥ 20%; no measurable cannibalization; ratings stable Contract asks before launch: - Enforceable minimum spend to redeem ($45) - Peak blackout windows and daily redemption caps in platform controls - Customer-level controls (limit 1 voucher per customer; new customers prioritized) - Access to redemption data (emails or hashed identifiers) to measure returns; 30/60/90-day reporting Bottom line: With enforceable minimum spend, strict off-peak controls, and measurable returns, the program can generate positive incremental contribution and demand shaping on slow periods. Without those controls (especially minimum spend and peak blackouts), the economics are unfavorable—default to NO-GO.