yelp vs opentable - restaurant reservations
TRANSCRIPT
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Yelp–Disrup-ngOpenTable
ByApoorvKulkarni
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BackgroundPrompt
Yelp–disrup-ngOpenTableJeremyStopplemanhasbuiltanamazingcompanyandproductinYelp by unlocking a powerful network effect. But he’s notsa?sfied… Although his product iswell liked, it only delivers onpartofthecustomerbenefit–ithelpsyoufindgreatrestaurants,butnotbookatable. ItdriveshimnutsthataGerfindingagreatplacetoeat,hisusersneedtoopenupanotherapp,OpenTable,tobookatable,oGenonlytofindoutnothingisavailable,sobacktoYelp to find a new restaurant. Sound familiar? We have allprobablyexperienced thismany?mes. It seems like itwouldbestraighMorwardtoleverageYelp’spowerfulnetworkeffecttobustinto the booking space, but OpenTable also has a powerfulnetworkeffectbetweenrestaurantsanddiners.Jeremyhasaskedyoutotakeafewhoursandsolvethisproblemforhim.
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Assump-onsandNotes
Assump-ons• Thecaseisbasedin2010• YelpandOpenTablehavenotenteredintoapartnership
• Othercompe?torssuchasUrbanSpoondon’texist
Notes• Alldataisfrom2010orearlier• Eventsbetween2010-2016havebeenignored• YelphasnotlaunchedYelpReserva?onsoracquiredSeatMe
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UserJourney
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Approach
1. Understandproblemspace
• Iden?fytargetuserpersonas
• Formulateuserstories
2. Exploringsolu-onspace• Iden?fyalterna?ves,evaluateandchoose
• Outlinestrategyforchosensolu?on
• Evaluatecompe??velandscape(networkeffects)
3. Experimenta-onandhypothesistes-ng
• Formulatehypothesisandconductexperiments
• Determinenextstepsbasedontestresults
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UnderstandingProblemSpace
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RestaurantReserva-on-TwoSidedMarket
• Networkeffectsbusiness
• Productvalue=
f(#D,#R)• Therefore
importanttoa`ractbothgroups• Diners• Restaurants
RestaurantsDiners
Reserva-onProduct
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UserPersona-Diner
Jill• 29yearsoldcollegeeducated• Ast.SalesMgrinatechco.• Makes$100Kperyear• LivesinSF• AlwayshasheriPhonewithin
anarmsreach• Dinesout2–3?mes/week
withprospectsorfriends• Checksonlinereviewsbefore
bookingtableorshopping“Ilovetotrynewcuisinesanddiscovernewrestaurants.Funtogooutwithfriends…Ifonlybookingatablewaseasy“
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UserPersona-Diner
Asadinerwan?ngtohavelunchordinneratafullservicerestaurantIwouldliketobeableto-• reserveatableeasily• asitwillhelpmesave
?meand• reducethehasslearound
restaurantreserva?onsJill
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UserPersona-Restauranter
John• 34yearsoldCollegediploma• Manager@singleunitfull
servicerestaurantinSF• $750Kturnover• 3.5%profitmargin• 0.7dailyseatturnover
• Reserva?onspen&paper• $15Kmarke?ngbudget• Techsavvy:• LaunchedFBpage• Engageswithreviewers
onYelp
“Iwanttogetmorediners,.Manydinersliketobookatableonline.Therefore,Iwantasimple,automa?c,real-?meandinexpensivewaytoacceptreserva?onsfromsuchdiners.”
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UserPersona-Restauranter
AsamidsizerestauranterIwouldlike-• asimpleandinexpensiveway
toacceptreserva?onsfrompeoplewan?ngtobookatableonline• asitwillhelpmegetmore
diners John
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ExploringSolu-onSpace
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Solu-onspace
Poten-alSolu-ons(Partner/Repurpose/Make)• PartnerwithOpenTabletoenabledinerstodirectly
bookatablefromYelp• RepurposeYelp’smessagingfeaturetohelpusersbook
atable• Developacloudbasedrestaurantreserva?onproduct
withreal-?mebookingcapabili?esàlowupfrontCapEx
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Evalua-ngAlterna-ves
Solu-on Simplicity Bookingspeed Affordability Scalable
OTpartnership D:HighR:Mid
High Low Mid
Messaging Low-mid Low-mid Low Low
Reserva?onproduct
High High Mid-high HighVa
luetousers
Reserva?onproductHigh
OTpartnership
Medium
Low Messaging
Low Medium High
Developmenteffort
ROI Low Medium High
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Strategy
ProductStrategy• Cloudbasedreserva?onproductàleveragingYelpnetwork• Ini?alversionàbasicfunc?onalitytomakereserva?ons• Frontend–Diners:Mob+Web;Restaurants:Tab&Smrtphone• Backenddevelopproprietaryreserva?onalgorithm
BusinessModel• MobiledrivenSaaSmodel
• Restaurants:nominalsubscrip?on+perseatbookingfee• Freefordinersmakingreserva?on
• Usebookingtoimproveadtarge?ngàincreasedadrevenue
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NetworkEffectsAnalysis
• Networksize• 39MusersàmostdiscoverYelpwhilesearchingfor
restaurants• 307Kclaimedbusinesses(11Kac?ve)
• Engagementindicators• 15M+userreviews(23%forrestaurants)• 43%usersvisitYelp>=3?mes/week
• Valuetorestaurants• 60%+Yelpersdine>=3?mes/week• HBSstudy:+1Starà+5-9%revenue
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NetworkEffectsAnalysis(Compe--on)
• Networksize• 20Kusers/diners• 15Krestaurants(top-end)
• Engagementindicators• 6.5Mseatsreservedmonthly
• Valuetorestaurants• 45-80%reserva?onscomefromOT• CRMcapabili?esàvaluabledinerdata
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NetworkEffect(Opportuni-es&Threats)
Opportuni-es• Yelphasadvantageinmidmarketàtargetsegment• speciallysingleunitrestaurants
• Manyrestaurantshaveclaimedprofilesandengagewithdiners• OurtargetrestaurantersfindOTuneconomical
Threats• Perceivedconflictofinterest• Lowac?vebusinessescount• OpenTablemayentermidsegment
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Vision-Crea-ngValue
Increasedvaluetouser• Higherengagement• Increasedno.ofreviews
Increasedvaluetorestaurants• Increaseinclaimed
businesses• Higherengagement• Newrevenuesource
Overall• HigherNPS• Be`eradtarge?ng• HigherLTVofdinersand
restaurants
YelpRestaurantReserva-onproduct
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Experimenta-onAnd
HypothesisTes-ng
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Experimenta-on–Hypothesistes-ng
Sequen-alTes-ngTestH(A)
IfsuccessfulàTestH(B)Ifunsuccessfulàanalyzeresultsandfindrootcauseoffailure
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HypothesisandExperiment
Experiment:WizardofOz
• Testperiod7days• Randomlyselect100mobileusers(likeJill)perdayinSF
searchingforrestaurants
• Showop?ontobookatableàmeasureclick-through
• Inthebackground,calltherestaurantandbookmanually
Leapoffaithhypothesis
• DinerslikeJillwillmakearestaurantreserva?ononYelp
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ExperimentMock-ups(1/3)
Usersinthetestgroupseean“ReserveNow”.
Iftheusertapsonthe“ReserveNow”bu`on,s/heistakentothenextscreen
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ExperimentMock-ups(2/3)
Usercanenterdesiredreserva?on?mehere
AGerenteringdesiredreserva?on?me,usertapsthisbu`ontofinalizethebooking
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ExperimentMock-ups(3/3)
UserseesthismessageasYelpreservesatableinthebackground
S/hecanchoosetoto:
• Wait?llanon-screenconfirma?onisdisplayed(notpicturedhere)àconversion
• Choosetobeno?fiedoncereserva?oniscompleteàconversion
• Canceltherequest
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Evalua-onCriteria
SuccessFactors• 10%click-throughrate• Benchmark:es?mated10%mobileYelpershavecalled
business• 8%conversionrate• Benchmark:7%conversionrateforhotels-OTA(higher?cket
size)Measure• Noshowsàif>20%experimentwithreminderandpenaltyin
futuretestsandMVP• Benchmark:18%inhotelsandflightreserva?ons
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Follow-up:IfSuccessful
• TestH(B):RestauranterslikeJohnwouldwanttoacceptreserva?onsonYelp
• Experiment:WizardofOz• Testperiod7days• Select5SFrestaurantssimilartoJohn’s• Givetablet(with3GifnoWifi)• Prototypewithwebbasedsharedcalendar• Yelpstaffreceivesdinerrequestandmanuallymakes
reserva?ons• Restaurantercanalsoeditcalendar
• Con?nuequal&quantexperimentswithdinersàfeedbackdrivenitera?on
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Table1 Table2 Table3 Table4
Yelpbooking
3guests2guests
2guests
Follow-up:IfSuccessful(ExperimentMock-ups)
Measure• conversion,cancela?ons,noshows,wait?mes• Restauranterinterviewàqualita?vefeedback
Rest.booking
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Follow-up:IfSuccessful(Cont.)
Productdevelopmentandlaunch• OutlineMVPv1Specs• Staffingand?melinesforproductdevelopment• ObtainExecu?veapprovalandgetbudgetsanc?ons• Progressiverollout:dogfoodingwithselectSFrestaurants,
employeesandCommunityManagers• WorkwithMarke?ng&Salesforlow-touchrestaurant
acquisi?onstrategy• FullscalelaunchcitybycityàSFàLAà….
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Follow-up:IfUnsuccessful
• Checkiftherewereanyunexpectedevents• Analyzedata• Ifdifferentuser(m/f)convertedatdifferentrates
• IfConversionrateatdifferentdatesand?meswasdifferent
• Ifconversionratedifferedfordifferentrestauranttypes• Conductuserinterviews• ObtainfeedbackonUI&UX• Obtainfeedbackonusefulnessofreserva?onfeature
• Refinehypothesisbasedonanalysisandfeedback
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Appendix
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Userjourney(Addi-onalDecisionPoints)
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UserPersona-Diner
ResearchsourcesA. YelpAnaly?cs(Techcrunch)11/08B. LocalConsumerReviewSurvey
2010–BrightLocalKeyhighlights• Yelpgenderra?o• 49%W,51%M
• Yelpagedistribu?on• 37%20–29,36%30–39years
• Restaurantreviews• 32%W,30%M
• Restaurantonlinereviewsaffectpurchasedecisions• 28%16–34,29%34-54age
Jill
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UserPersona-Restaunter
ResearchsourcesA. StudentScholarships.org
2004B. Na?onalRestaurant
Associa?onreports2010Approach• Midsegment• Turnover• 24%à$0.5-1Msales• Averagesales0.75M
• Affilia?on• 86%singleunit
John
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TargetUsers–FullServiceRestaurantsCriteria
(Figuresaremedians)
Avgcheck/Person:<$15
Avgcheck/Person$15-25
Avgcheck/Person>$25
Profitmargin 3% 3.5% 1.8%Salary&wagesas%ofsales
33.7% 33.2% 33.7%
Employeeturnover 60% 63% 50%Marke?ngas%ofsales
1.6% 2% 2.2%
Dailyseatturnover 1.9 1.5 0.8Barrierstoentry Low Low-medium HighNumberofrestaurants
Large Moderate-large Low–moderate
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Iden-fyingtargetusers(Diners)Users/Diners FastServiceRestaurants FullServiceRestaurants
Breakfast/Brunch
• Popularity:Medium-High• Walk-ins• Averagepartysize:1-3
• Popularity:Low• Manyfullservicerestaurants
don’tofferabreakfastop?on
Lunch
• Popularity:Low-Medium• Walk-ins• Averagepartysize:1-3• Priceandspeedofservice
moreimportantforpatrons• Highrepeatvisitorcount
• Popularity:Medium-high• Reserva?on/walk-ins• AveragePartysize:2-4• business/casualmee?ngs
Dinner
• Popularity:Low-Medium• Walk-ins• AveragepartySize:1-3• Priceandspeedofservice
moreimportantfordiners
• Popularity:High• Mostlyreserva?on• Averagepartysize:3-6• Socialexperience,spend?me
withfamily&friends
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ReturnonDeveloperEffort
Scale:1–5(L-H)
Simplicity Speed Economica Scailable Total
Reserva?onprod
5 5 4 4 18
Messaging 2 2 3 7
OTpartnership 3 5 1 2 11
Value Deveffort Val/effort
Reserva?onprod 18 4 4.50
Messaging 7 2 3.50
OTpartnership 11 3 3.67
Reserva?onprod
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• Restaurantsopera-onssta-s-cs:• 2009/2010NRARestaurantIndustryOpera?onsReport
• Yelpsta-s-cs• Yelpblog–data• YelpS1filing(takendataforyear20100rearlier)
• OpenTablesta-s-cs• 10Kfilings
• Other:• LocalConsumerReviewSurveyReport2010(Part1-3)
Sources&References