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Yelp vs Opentable - Restaurant Reservations
- 2. 2 Background Prompt Yelp – disrup-ng OpenTable Jeremy Stoppleman has built an amazing company and product in Yelp by unlocking a powerful network effect. But he’s not sa?sfied… Although his product is well liked, it only delivers on part of the customer benefit – it helps you find great restaurants, but not book a table. It drives him nuts that aGer finding a great place to eat, his users need to open up another app, OpenTable, to book a table, oGen only to find out nothing is available, so back to Yelp to find a new restaurant. Sound familiar? We have all probably experienced this many ?mes. It seems like it would be straighMorward to leverage Yelp’s powerful network effect to bust into the booking space, but OpenTable also has a powerful network effect between restaurants and diners. Jeremy has asked you to take a few hours and solve this problem for him.
- 3. 3 Assump-ons and Notes Assump-ons • The case is based in 2010 • Yelp and Open Table have not entered into a partnership • Other compe?tors such as UrbanSpoon don’t exist Notes • All data is from 2010 or earlier • Events between 2010-2016 have been ignored • Yelp has not launched Yelp Reserva?ons or acquired SeatMe
- 5. 5 Approach 1. Understand problem space • Iden?fy target user personas • Formulate user stories 2. Exploring solu-on space • Iden?fy alterna?ves, evaluate and choose • Outline strategy for chosen solu?on • Evaluate compe??ve landscape (network effects) 3. Experimenta-on and hypothesis tes-ng • Formulate hypothesis and conduct experiments • Determine next steps based on test results
- 7. 7 Restaurant Reserva-on - Two Sided Market • Network effects business • Product value = f(#D,#R) • Therefore important to a`ract both groups • Diners • Restaurants Restaurants Diners Reserva-on Product
- 8. 8 User Persona - Diner Jill • 29 years old college educated • Ast. Sales Mgr in a tech co. • Makes $100K per year • Lives in SF • Always has her iPhone within an arms reach • Dines out 2 – 3 ?mes / week with prospects or friends • Checks online reviews before booking table or shopping “I love to try new cuisines and discover new restaurants. Fun to go out with friends… If only booking a table was easy“
- 9. 9 User Persona - Diner As a diner wan?ng to have lunch or dinner at a full service restaurant I would like to be able to - • reserve a table easily • as it will help me save ?me and • reduce the hassle around restaurant reserva?ons Jill
- 10. 10 User Persona - Restauranter John • 34 years old College diploma • Manager @ single unit full service restaurant in SF • $750K turnover • 3.5% profit margin • 0.7 daily seat turnover • Reserva?ons pen & paper • $15K marke?ng budget • Tech savvy: • Launched FB page • Engages with reviewers on Yelp “I want to get more diners,. Many diners like to book a table online. Therefore, I want a simple, automa?c, real-?me and inexpensive way to accept reserva?ons from such diners.”
- 13. 13 Solu-on space Poten-al Solu-ons (Partner/Repurpose/Make) • Partner with Open Table to enable diners to directly book a table from Yelp • Repurpose Yelp’s messaging feature to help users book a table • Develop a cloud based restaurant reserva?on product with real-?me booking capabili?es à low upfront CapEx
- 14. 14 Evalua-ng Alterna-ves Solu-on Simplicity Booking speed Affordability Scalable OT partnership D: High R: Mid High Low Mid Messaging Low-mid Low-mid Low Low Reserva?on product High High Mid-high High Value to users Reserva?on product High OT partnership Medium Low Messaging Low Medium High Development effort ROI Low Medium High
- 15. 15 Strategy Product Strategy • Cloud based reserva?on product à leveraging Yelp network • Ini?al version à basic func?onality to make reserva?ons • Frontend – Diners: Mob + Web; Restaurants: Tab & Smrtphone • Backend develop proprietary reserva?on algorithm Business Model • Mobile driven SaaS model • Restaurants: nominal subscrip?on + per seat booking fee • Free for diners making reserva?on • Use booking to improve ad targe?ng à increased ad revenue
- 16. 16 Network Effects Analysis • Network size • 39M users à most discover Yelp while searching for restaurants • 307K claimed businesses (11K ac?ve) • Engagement indicators • 15M+ user reviews (23% for restaurants) • 43% users visit Yelp >= 3 ?mes / week • Value to restaurants • 60%+ Yelpers dine >= 3?mes/week • HBS study: +1Star à +5-9% revenue
- 17. 17 Network Effects Analysis (Compe--on) • Network size • 20K users/diners • 15K restaurants (top-end) • Engagement indicators • 6.5M seats reserved monthly • Value to restaurants • 45-80% reserva?ons come from OT • CRM capabili?es à valuable diner data
- 18. 18 Network Effect (Opportuni-es & Threats) Opportuni-es • Yelp has advantage in mid market à target segment • specially single unit restaurants • Many restaurants have claimed profiles and engage with diners • Our target restauranters find OT uneconomical Threats • Perceived conflict of interest • Low ac?ve businesses count • Open Table may enter mid segment
- 19. 19 Vision - Crea-ng Value Increased value to user • Higher engagement • Increased no. of reviews Increased value to restaurants • Increase in claimed businesses • Higher engagement • New revenue source Overall • Higher NPS • Be`er ad targe?ng • Higher LTV of diners and restaurants Yelp Restaurant Reserva-on product
- 22. 22 Hypothesis and Experiment Experiment: Wizard of Oz • Test period 7 days • Randomly select 100 mobile users (like Jill) per day in SF searching for restaurants • Show op?on to book a table à measure click-through • In the background, call the restaurant and book manually Leap of faith hypothesis • Diners like Jill will make a restaurant reserva?on on Yelp
- 26. 26 Evalua-on Criteria Success Factors • 10% click-through rate • Benchmark: es?mated 10% mobile Yelpers have called business • 8% conversion rate • Benchmark: 7% conversion rate for hotels-OTA (higher ?cket size) Measure • No shows à if > 20% experiment with reminder and penalty in future tests and MVP • Benchmark: 18% in hotels and flight reserva?ons
- 27. 27 Follow-up: If Successful • Test H(B): Restauranters like John would want to accept reserva?ons on Yelp • Experiment: Wizard of Oz • Test period 7 days • Select 5 SF restaurants similar to John’s • Give tablet (with 3G if no Wifi) • Prototype with web based shared calendar • Yelp staff receives diner request and manually makes reserva?ons • Restauranter can also edit calendar • Con?nue qual & quant experiments with diners à feedback driven itera?on
- 28. 28 Table 1 Table 2 Table 3 Table 4 Yelp booking 3 guests 2 guests 2 guests Follow-up: If Successful (Experiment Mock-ups) Measure • conversion, cancela?ons, no shows, wait ?mes • Restauranter interview à qualita?ve feedback Rest. booking
- 29. 29 Follow-up: If Successful (Cont.) Product development and launch • Outline MVP v1 Specs • Staffing and ?melines for product development • Obtain Execu?ve approval and get budget sanc?ons • Progressive rollout: dog fooding with select SF restaurants, employees and Community Managers • Work with Marke?ng & Sales for low-touch restaurant acquisi?on strategy • Full scale launch city by city à SF à LA à ….
- 30. 30 Follow-up: If Unsuccessful • Check if there were any unexpected events • Analyze data • If different user (m/f) converted at different rates • If Conversion rate at different dates and ?mes was different • If conversion rate differed for different restaurant types • Conduct user interviews • Obtain feedback on UI & UX • Obtain feedback on usefulness of reserva?on feature • Refine hypothesis based on analysis and feedback
- 31. 31 Appendix
- 33. 33 User Persona - Diner Research sources A. Yelp Analy?cs (Techcrunch) 11/08 B. Local Consumer Review Survey 2010 – BrightLocal Key highlights • Yelp gender ra?o • 49% W, 51% M • Yelp age distribu?on • 37% 20 – 29, 36% 30 – 39 years • Restaurant reviews • 32% W, 30% M • Restaurant online reviews affect purchase decisions • 28% 16 – 34, 29% 34 -54 age Jill
- 34. 34 User Persona - Restaunter Research sources A. StudentScholarships.org 2004 B. Na?onal Restaurant Associa?on reports 2010 Approach • Mid segment • Turnover • 24% à $0.5-1M sales • Average sales 0.75M • Affilia?on • 86% single unit John
- 35. 35 Target Users – Full Service Restaurants Criteria (Figures are medians) Avg check / Person: <$15 Avg check / Person $15-25 Avg check/Person >$25 Profit margin 3% 3.5% 1.8% Salary & wages as % of sales 33.7% 33.2% 33.7% Employee turnover 60% 63% 50% Marke?ng as % of sales 1.6% 2% 2.2% Daily seat turnover 1.9 1.5 0.8 Barriers to entry Low Low-medium High Number of restaurants Large Moderate - large Low – moderate
- 36. 36 Iden-fying target users (Diners) Users/Diners Fast Service Restaurants Full Service Restaurants Breakfast / Brunch • Popularity: Medium-High • Walk-ins • Average party size: 1-3 • Popularity: Low • Many full service restaurants don’t offer a breakfast op?on Lunch • Popularity: Low-Medium • Walk-ins • Average party size: 1-3 • Price and speed of service more important for patrons • High repeat visitor count • Popularity: Medium-high • Reserva?on/walk-ins • Average Party size: 2-4 • business/casual mee?ngs Dinner • Popularity: Low-Medium • Walk-ins • Average party Size: 1-3 • Price and speed of service more important for diners • Popularity: High • Mostly reserva?on • Average party size: 3-6 • Social experience, spend ?me with family & friends
- 37. 37 Return on Developer Effort Scale: 1 – 5 (L-H) Simplicity Speed Economica Scailable Total Reserva?on prod 5 5 4 4 18 Messaging 2 2 3 7 OT partnership 3 5 1 2 11 Value Dev effort Val/effort Reserva?on prod 18 4 4.50 Messaging 7 2 3.50 OT partnership 11 3 3.67 Reserva?on prod
- 38. 38 • Restaurants opera-ons sta-s-cs: • 2009/2010 NRA Restaurant Industry Opera?ons Report • Yelp sta-s-cs • Yelp blog – data • Yelp S1 filing (taken data for year 2010 0r earlier) • OpenTable sta-s-cs • 10K filings • Other: • Local Consumer Review Survey Report 2010 (Part 1-3) Sources & References