Yelp vs Opentable - Restaurant Reservations

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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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Usercanenterdesiredreserva?on?mehere

AGerenteringdesiredreserva?on?me,usertapsthisbu`ontofinalizethebooking

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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

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ThankYou.Let’sbuildit!

ApoorvKulkarniStanfordMBAProductPerson

[email protected]

Yelp vs Opentable - Restaurant Reservations

Technology

Transcript of Yelp vs Opentable - Restaurant Reservations