a study of hotel occupancy - simple...
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IN DEGREE PROJECT TECHNOLOGY,FIRST CYCLE, 15 CREDITS
, STOCKHOLM SWEDEN 2016
A Study of Hotel OccupancyUsing Multiple Linear Regression and Market Strategy Analysis
MICHAELA KAREFLOD
JENNIFER LJUNGQUIST
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ENGINEERING SCIENCES
A Study of Hotel Occupancy
Using Multiple Linear Regression and Market Strategy Analysis
M I C H A E L A K A R E F L O D J E N N I F E R L J U N G Q U I S T
Degree Project in Applied Mathematics and Industrial Economics (15 credits) Degree Progr. in Industrial Engineering and Management (300 credits)
Royal Institute of Technology year 2016 Supervisors at KTH: Fredrik Armerin, Jonatan Freilich
Examiner: Henrik Hult
TRITA-MAT-K 2016:20 ISRN-KTH/MAT/K--16/20--SE Royal Institute of Technology SCI School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci
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AbstractThispaperisbasedoncollaborationbetweenacompanycalledStayAtHotelApartABandtwoKTH
students.Itexamineswhichfactorsthatareinfluencingthehotel’soccupancyandhowthismaybe
increased by enhancing the market strategy. The aim is to provide a foundation for strategy
development to the company. The study is performed by connecting applied mathematics with
industrial management. The mathematical part is based on a multiple linear regression on
occupancywithhistoricaldatafrom2011to2016mainlycollectedfromStayAt.Theanalysisofthe
market strategy is performed bymeans of themathematical results and by using twomarketing
models,SWOTanalysisand4P’s.Theresultshowsthatrelativeprice,weather,high-andlowseason
forthehotel,monthsonmarket,occupancyforthecompetitiveset,locationandmarketsharesare
significant factors influencing the hotel’s occupancy. Themain recommendations concluded from
the analysis of the market strategy are to put effort on digitalisation, visualising the brand,
publications, CSR initiatives, exploiting existing resources and carefully considering timing of
marketing.
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Sammanfattning
Denhäruppsatsenbaseraspåett samarbetemellan företaget StayAtHotelApartABoch tvåKTH-
studenter.Denutvärderarvilkafaktorersompåverkarhotelletsbeläggningochhurdennakanöka
genom en förbättrad marknadsstrategi. Syftet är att leverera en grund för strategiutveckling till
företaget. Studien är genomförd genom att sammankoppla tillämpad matematik med industriell
ekonomi. Den matematiska delen baseras på en regressionsanalys av hotellets beläggning med
historiskdatafrån2011till2016somfrämstärförseddavStayAt.Analysenavmarknadsstrateginär
genomfördmedhjälp av dematematiska resultaten samt genomatt applicera tvåmodeller inom
marknadsföring,SWOTanalysoch4P.Resultatenvisarattrelativtpris,väder,hög-ochlågsäsongför
hotellet, månader på marknaden, beläggning för konkurrenter, läge och marknadsandelar är
signifikantafaktorersompåverkarhotelletsbeläggning.Deprimärarekommendationernasomtagits
framutifrånanalysenavmarknadsstrateginärattläggaresurserpådigitalisering,publikationeroch
CSR initiativ, att visualisera varumärket, utnyttjaexisterande resurser samtatt grundligtöverlägga
timingavmarknadsföring.
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TableofContents
1. Introduction……………………………………………………………………………………..…………………..51.1. Background…………………………………………………………………………………………………………..51.2. Aim……………………………………………………………………………………………………………………….71.3. ResearchQuestion………………………………………………………………………………………………..7
2. TheoreticalFramework………………………………………………………………………………………….82.1. MultipleRegressionAnalysis………………………………………………………………………….…….8
2.1.1. AssumptionsforLinearRegression………………..………………..………………..…..……82.1.2. OrdinaryLeastSquare……..………………..………………………..………………..…….………92.1.3. PossibleErrors………………..………………..………………..………………………..……..………92.1.4. ModelSelection………………..………………..………………..……………..………….……..…12
2.2. AnalysisoftheMarketStrategy……………………………………………………………….…………162.2.1. SWOT………………..………………..………………..…………………..………………..………….…162.2.2. 4P’s………………..………………..………………..…………………..………………..……………….172.2.3. PENCILS………………..………………..………………..…………………..………………..…………17
3. Methodology………………………………………………………………………………………………………183.1. LiteratureStudy……………………………..………………………………………..…………………………183.2. QuantitativeResearch–MultipleRegressionAnalysis……………………………..…………18
3.2.1. MainModel…………..………………..……………………..………………..………………..…..…183.2.2. CategoryModels………………..………………..………………..………………….….…..………22
3.3. QualitativeResearch…………………………….…………………………………………….………………233.3.1. MeetingwithManagementofStayAt………………..………………..……..…………..…233.3.2. InterviewwithDeputyCEOatStayAt………………..………………..…………………..…23
4. MathematicalResults…………………………………………………………………………………….……244.1. LinearRegressionAssumptions…………………………………………………………………..………24
4.1.1. Quantile-Quantileplot………………..………………..………………..….………………..……244.1.2. VarianceInflationFactor………………..………………..………………..…….………………..24
4.2. TestingtheMainModel………………………………………………………………….……….….………254.2.1. Estimatedbetas,p-valuesandETA-squared………………..…….….………..…………254.2.2. ConfidenceIntervals………………..………………..………………..…………..…………..……26
4.3. ReductionoftheMainModel………………………………………………………..……………………264.3.1. AkaikeInformationCriterion………………..………………..……………..…..………………264.3.2. AdjustedR2………………..………………..………………………………..…………………………..26
4.4. FinalMainModel…………………………………………..……………………………………………………274.5. CategoryModels…………………………………………………….…………………………………………..27
4.5.1. LinearRegressionAssumptions…………………..………………..………………..…………274.5.2. DifferencesinRegressions………………..………………..………………...………………….27
5. InferencesfromtheRegressionAnalysis…………………………………………..…………………296. DiscussionandMarketStrategyAnalysis……………………………………….……………………32
6.1. SWOT………………………………………………………………………………………….………………………326.2. 4P’s…………………………………………………………………………………………………….………………346.3. PENCILS………………………………………………………………………………………………………………37
7. Recommendations………………………………………………………………………………………………418. Criticism………………………………………………………………………………………………………………449. References…………………………………………………………………………………………….…………….4610. Appendix…………………………………………………………………………………………….………………52
10.1. ListofTables……………………………………………………………………………….………………………5210.2. ListofPlots……………………………………………………………………………….………………………..55
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1.Introduction
1.1. Background
This study isbasedon collaborationbetweenKTH studentsandStayAtHotelApartAB, a company
currently running three apartment hotels based in Bromma, Kista and Lund. StayAt’s business
conceptistooffershort-andlong-termleasesonhotelapartments;furnishedaccommodationwith
a fully equipped kitchen. Along with the lease, customers receive a complete service package
includingareceptionopenatallhours,breakfastondemandandweaklycleaning.Thepricesofthe
apartmentsare currentlydivided into three lengthsof stay:Daily (1-4days),Extended (5-29days)
andLongTerm (>29days).Thecontributionmargins forExtended andLongTerm accommodation
aremuchhigher than theone forDaily. The reason for this is that services aremore limited and
therebythecostsarelowerforthelongerstays.(Frisell2016)
HistoryoftheHotel
Originallythehotelchainisestablishedin1999asthecompanyinitiatestheiroperationsunderthe
nameCityApartments.The facility inBromma inaugurates inOctober1999,Kista in January2003
and Lund in September 2006. In 2004 the chain changes name toAccomeand in 2007 the name
StayAtistaken.Duetofinancialissuestheorganisationisforcedintobankruptcyandanewfirmis
foundedunderthenameStayAtHotelApartABonthe16thofApril2010.Thisleadstoacomplete
reformation of the internal structure, which is performed by the old management. The
reconstructionservesasafoundationforthecurrentoperatingorganisation.(Schwalm2016)
CompetitiveSet
The main competitive set for StayAt in Bromma is 2Home Hotel Apartments, BW Plus Sthlm
Bromma,CourtyardbyMarriottStockholm,MorningtonHotelBromma,ParkInnbyRadissonSolna,
ScandicAlvik,ScandicBrommaandSkyHotelApartmentsStockholm.InKistatherivalsareprimarily
GoodMorningKista,MemoryHotel,MorningtonHotelBromma,MrChipHotel,ScandicJärvaKrog,
ScandicVictoriaTowerandWelcomeHotelBarkarby.ForthehotelinLund,ClarionCollectionHotel
Planetstaden,GoodMorningLund,GrandHotelLund,HotelFinn-Lund,HotelLundia,ScandicStar
Lundconstitutetheircompetition.(BenchmarkingAlliance2016)
BusinessModel
StayAt’s primary strategy is B2B (Business to Business), 80% of their customers are national and
international companies sending consultants to Lund or Stockholm for work. Many of these
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consultantsarrivefromAsia.Greateffortisputintoachievinglonglastingcustomerrelationshipsby
creating contracts with corporations, especially within the R&D industry. (Schwalm 2016) The
implicationofthisisthatamajorityoftheguestshavebookedtheaccommodationfarinadvance.
ThiscustomersegmentisalargeandstablepartofStayAt’stargetgroup,butoftenthehotelisnot
fullybookedpartlysincelastminutecancellationsandlowseasonsforthistargetoccur.Inaddition
to thecorporatecontractsandselling roomsdirectly fromthehotel receptionsand theirwebsite,
StayAtusebooking.com,hotels.com,expedia.com,hotelbeds.comasdistribution channels. (Frisell
2016)
StayAt’smonthlyaverageoccupancy, idestnumberof sold roomsdividedbynumberofavailable
rooms, is 77% (Financial Statement of StayAt January 2016). Because of this, there are ongoing
discussions at the companyofhow to reachout to awider audience. Theywish to appeal guests
whocanbookapartmentsshortinadvanceoftheirvisit inordertoreachfulloccupancy(Schwalm
2016, Frisell 2016). A reformulation and enhancement of the organisation’s market strategy is
required;theirbrandestablishmentneedstobedirectednotonlytobusinessesbutalsototheend
user.Today,thereisnosubstantialmarketingtowardstheB2Efield(BusinesstoEnduser).Further,
the company has noticed that the contracted consultants receive an increased influence on the
choiceofstay,andthereforetheneedforaB2Estrategyisevenmoreessential.
Webringpeopletogetherandhelppeopletoabetterstay isStayAt’sstatedmission.Theirvisionis
to be the Stay of the Future. They have formulated their fundamental values as Passion,
ConsiderationandCompetenceandfromthesethreeperspectivestheorganisationhascreatedtheir
business idea and model. StayAt’s original concrete business idea is to offer fully equipped and
furnishedapartments.Today,thisisratherconsideredahygienefactorandthefocusoftheideais
staginganexperienceforthecustomer.(Schwalm2016)
Future
StayAtiscurrentlyinanexpansionphase.Thecompanyisplanningtoincreasenumberoffacilities
and introducetwonewconcepts. (Schwalm2016,Frisell2016)Becauseoftheexpansiontheneed
forabroadertargetgroupandmarketingtowardtheenduserisevenmoreimportant.Therewillbe
moreroomstofill,andsoananalysisofthehotel’soccupancyandmarketstrategyisnecessary.
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1.2. Aim
TheaimofthispaperistohelpStayAtfindthefactorsinfluencingtheiroccupancy,andconsequently
their profit. The analysis will be performed by examining the average occupancy and each price
category separately to enablea comparing study in thediscussion chapter. StayAt is interested in
howtheentireorganisationmaydevelopandexpand,andsotheanalysiswillbebasedondatafrom
allthreefacilities.Thestudywillassesshoweachfactoreffectsoccupancy.Theresultsaimtoserve
asafoundationforananalysisofthecompany’smarketstrategy.Thevariablesfound,especiallythe
ones StayAt have not considered or enlightened earlier, can be useful to improve their way of
operating.Hence,thefinalaimofthispaperistoexaminehowthemarketstrategycanbeimproved
inordertoreachouttotheenduserandtherebyreceiveimprovedoccupancy.Thepartyinterested
intheresultswillmainlybethemanagementofStayAt.
1.3. ResearchQuestions
1. WhichfactorsimpactStayAt’soccupancyandhowdothesematter?
2. HowcanStayAt’smarketstrategyfocusonB2E inadditiontoB2B inorderto improvethe
occupancy?
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2. TheoreticalFramework
2.1. MultipleRegressionAnalysis
Inmultipleregressionanalysistheessentialpartis investigatingifonespecificvariabledependson
severalothers,andinthatcasehow.Thedependentvariable,𝑦,iscalledresponsevariableandthe
variablesconstructing𝑦arecalledexplanatoryvariablesorcovariates:𝑥$.Themodelisconstructed
infollowingway:
𝑦% = 𝑥%$𝛽$ + 𝑒%*
, 𝑖 = 1, … , 𝑛
Orinmatrixform:
𝒀 = 𝑿𝜷 + 𝒆
Where
𝒀 =𝑦4⋮𝑦6
, 𝑿 =1 𝑥44 ⋯ 𝑥4*⋮ ⋮ ⋱ ⋮1 𝑥64 ⋯ 𝑥6*
, 𝜷 =𝛽9⋮𝛽*
, 𝒆 =𝑒4⋮𝑒6
Here𝑥4, … , 𝑥* compose the𝑘 number of factors onwhich𝑦 depend. Both the𝑥%$′𝑠 and𝑦%’s are
alwaysgivendatawhilst𝛽4, … , 𝛽* aretheonesintendedtobeestimated.The𝛽$′𝑠arethecovariates
correspondingcoefficients,calledregressioncoefficients.Thelastpartofthemodel,the𝑒%′𝑠,arethe
regressionsresiduals.Thesearerandomvariables,sotheyarenotgivenbeforehand.Theconstant𝑛
correspondstothenumberofobservationsusedwhenrunningtheregression.(Lang2015)
2.1.1. AssumptionsforLinearRegression
Whenusinga linear regressionmodel, fiveassumptionshave tobemade for theprediction tobe
accurate:
● There exists a linear relationship between the dependent variable and the explanatory ones.
Inaccuratecovariatesandnon-constantestimatesof𝛽cancausenon-linearity.
● The model is homoscedastic, which means that the variance of the residuals is constant:
𝐸 𝑒%> = 𝜎%>.
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● Theresidualsareindependent,normallydistributedrandomvariableswithexpectedvaluezero:
𝐸 𝑒 = 0.Thiscreatesamorecorrectexpectedvalueforthebetas.
● Thereisnoorlittlemulticollinearity.
(Williams,GómezGrajalesandKurkiewicz2013;HayesandCai2007)
2.1.2. OrdinaryLeastSquare
Ordinary Least Square (OLS) is amethodused toestimate valuesof the regression coefficients𝛽.
Theestimatedvaluesaredenotedwithahat,𝛽.ThepurposeofOLSistominimisethesumofthe
squaresoftheresiduals(|ê|>).Toachievethis,oneneedstosolvethenormalequationsfor𝛽:
𝑋Fê = 0(1)
Where
ê = 𝑌 − 𝑋β (2)
Equation(2)in(1)gives:
𝑋F 𝑌 − 𝑋𝛽 = 0
𝑋F𝑌 − 𝑋F𝑋 𝛽 = 0
𝑋F𝑋 𝛽 = 𝑋F𝑌
→ 𝛽 = (𝑋F𝑋)M4𝑋F𝑌
(Lang2015;Belsley,KuhandWelsch2004)
2.1.3. PossibleErrors
Heteroskedasticity
The difference between homoscedasticity and heteroskedasticity lies in the structure of the
variances.Inaheteroskedasticlinearregression,thevariancesoftheresidualsareunequalwhilstin
ahomoscedastic, theyareequal.Whenassuminghomoscedasticityoneof thebenefits lies in the
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great simplification of the theoretical calculations (Hansen 2015). One can express
heteroskedasticity as:𝐸 𝑒%> = 𝜎%>. Consistent residuals areoneof the conditions touseOLS. The
inconsistent variances effect the standard deviations and the significance of the estimates. The
consequenceisanincoherenthypothesisresult,seesection2.1.4.underHypothesisTest.Remedies
forheteroskedasticityisthereforenecessaryifOLSistobeusedforinference.(Lang2015)
One of the remedies is to useWhite’s Consistent Variance Estimator. This estimation includes a
covariancematrix,expressedbelow,wherethestandarddeviationsarederivedasthesquarerootof
thediagonalelements inthematrix.WhenWhite’smethodhasbeenperformed,onecanuseOLS
withoutconsequences.
𝐶𝑜𝑣(𝛽) = (𝑋F𝑋)M4( ê%>6%Q4 𝑥%F𝑥%)(𝑋F𝑋)M4
TheBootstrapisanapproachonemayusetomanageheteroskedasticityinsmallsamples.Bootstrap
isusedwhenthestandardmethodshavepoorproperties.Themethod includesaresampleof the
dataaftertheregression.Theresidualsarekeptandtheregressionisrunagainwithtwooutcomes.
With a probability of 0,5 the dependant variable has changed. The re-estimated parameters is
intendedto result inan improvedresult.Themethod is repeatedseveral (over1000) times. (Lang
2015)
Non-normalityofResiduals
Measurementerrorscanhavesubstantialconsequencesonstatisticalrelationships(Andrews1984).
Non-normalityoftheresidualsleadstoinaccurateestimatesofthebetavalues,justasinthecaseof
heteroskedasticity.DetectingthiscanbedonebycreatingaQuantileQuantile-plot,readmoreabout
thisinsection2.1.4.underQuantileQuantile-plot.(Lang2015)
Ifthenon-normalityiscausedbytheresidualsmeanvaluenotequallingzeroitwillcreateerroneous
results (Verbeek2004). If it is causedby thevariancesdiffering, the remediesare thesameas for
heteroskedasticity.
Multicollinearity
Multicollinearityoccursastwoormoreofthecovariatesarelinearlydependentandcorrelateswith
each other. The correlation of the covariates causes the standard errors of the coefficients to be
large.Theresultisimprecisepointestimatesoftheconcernedcoefficients.Thestandarderrorsare
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decreasingasthenumberofobservationsincrease.Theimplicationofthisisthattheproblemwith
multicollinearity is equivalentwith fewobservations.Hence, a remedy is to addobservations into
the regression. If the multicollinearity remains, a plausible solution is to remove one of the
correlatingcovariates.Alternatively,onecanmergetheaffectedvariables.Toexamineifthemodel
holdsmulticollinearity,onemayperformaVIF-testwhichisfurtherexplainedinsection2.1.4.under
VarianceInflationFactor.(Lang2015)
Endogeneity
Endogeneity means that one or several covariates are correlated with the error term. The
consequence of this is that the expected value of the residual is not zero, which causes the OLS
estimatestobe inconsistent.Aremedycommonlyusedforthis isreplacingtheOLSmodelwith2-
SLS. This method implicates substituting the endogenous variable with one or more instrument
variables. An instrument is a variable which is related to the endogenous covariate but not the
residual.Thisresultsinmorepreciseestimatesoftheregressioncoefficients.Endogeneityiscaused
byoneorseveralofthefollowingsituations.
SampleSelectionBias
Thissituationariseswhenthedataassembledissomehowsubjectivelychosencausingoneormore
groupstobeoverrepresented.Thismaycreatemisleadingresults.
Simultaneity
When the response variable affect one or several of the covariates there is simultaneity in the
model. Thismeans that the cause and effect relationshipmove in two directions, leading to the
endogeneityissue.
MissingRelevantCovariates
Amissingrelevantcovariateinthemodelcanhaveeffectontheerrorsincethemissinginformation
ofthisvariableisinsteadembeddedintheresidual.
MeasurementErrors
Measurementerrorscauseendogeneitysinceitraisesinaccuraciesinthe𝑥valueswhichisdirectly
relatedtotheresidual.
(Lang2015)
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2.1.4. ModelSelection
QualitativeorQuantitativeVariables
When producing a regressionmodel there are several options of how to express chosen factors.
Therearetwomainalternatives;qualitativeorquantitativevariables.Qualitativevariablesarecoded
numerically,butthenumbersareinfactmeaningless.Inthespecificcasewhenaqualitativevariable
onlytakesonthevalues0or1itiscalledadummyvariable.
Theoppositeofthequalitativevariableisthequantitativeone,whichismeasuredonaquantitative
scale.Thenumberrepresentingthisvariableisinfactessentialfortheregression.(Lang2015)
QuantileQuantile-plot
In aQuantileQuantile-plot the standardized residuals represent the values on the y-axes and the
theoreticalquantilestheonesonthex-axes.Ifalinearrelationshipisfoundbetweenthesetwo,the
residualsarenormallydistributed.(Ford2016)
VarianceInflationFactor
TheVarianceInflationFactor(VIF)isatestperformedasmulticollinearityissuspected.Itmeasures
theincreaseinvarianceofanestimatedcoefficientiftheindependentvariablescorrelates.
VIFisperformedbyrunningaregressionwiththesuspectedcorrelatedcovariateasthedependent
variable on the remaining covariates. The formula includes the coefficient of determination,
explainedinsection2.1.4.underGoodnessofFit.
𝑉𝐼𝐹 = 1
1 − 𝑅>
If VIF exceeds 10 one can suspect severe multicollinearity, a VIF exceeding 5 warrant further
investigationbutisnotnecessarilyasignoflineardependence.(PennsylvaniaStateEberlyCollegeof
Science2016)Thedisadvantageofthemodelisthatthepractitionercannottellwhichvariablesare
correlating,onlythatacorrelationwiththetestedvariableexists(O’Brien2007).
HypothesisTest
To evaluatewhether a covariate fits into amodel, a hypothesis testmay be performed. The null
hypothesis𝐻9 insinuatesthatthecoefficientfortheconcernedcovariateiszero. Ifthenullcannot
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berejected, thecovariateshouldbeexcludedfromthemodel.Thecontradictiontothenull is𝐻4,
implyingthatthecoefficientdoesnotequalzeroandarejectionofthenullmaybeconcluded.
Theformulationofthemathematicalapproachisasfollows:
𝐻9:𝛽% = 0
𝐻4:𝛽% ≠ 0
Thetestisperformedonstatisticaldatabyanalysingtheobservedpointestimates.Derivationsare
basedonagivendistributionunderthenull.Thetestcomputesap-valuetoobtainanunderstanding
oftheprobabilitythattheestimatesbelongtothedistribution.Readmoreaboutthecomputationof
thep-valueinsection2.1.4.underF-test.(Lang2015)
F-test
AnF-testisahypothesistestwhereoneusesanFstatistictodecidewhethertorejectanullornot.
The test includesbothanF statisticandanalphaquantileof theFdistribution. If theF statistic is
smallerthanthealphaquantile,thenullhypothesisshouldberejected.(Lang2015)
As one study a hypothesis, it is useful to compute a confidence interval. Asmentioned in section
2.1.3.underHeteroskedacity, the standarddeviationsof theestimator𝛽 is thesquare rootof the
diagonal elements in the covariance matrix. A confidence interval at risk level alpha for 𝛽% is:
𝛽% = 𝐹Y(1, 𝑛 − 𝑘 − 1) ∙ 𝑆𝑆(𝛽%),
𝑛 = 𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑜𝑏𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛𝑠, 𝑘 = 𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑐𝑜𝑣𝑎𝑟𝑖𝑎𝑡𝑒𝑠
Thealphaquantileisdenotedas𝐹Y(1, 𝑛 − 𝑘 − 1).Ithas𝑛 − 𝑘 − 1denominatordegreesoffreedom
andonenumeratordegreesoffreedom.
TheFstatisticisavaluereceivedfromaregression.ThepurposeofanF-testistoexaminewhethera
groupofvariablesarejointlysignificant.TheFstatisticforthehypothesis𝛽% = 𝛽9isderivedfrom
𝐹 =(𝛽%9 − 𝛽%9)>
𝑉𝑎𝑟(𝛽%)
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Tofurtherevaluatewhetherthenullistoberejected,astudyofthep-valueisappropriate.Thep-
valueforthehypothesisis𝑃𝑟(𝐹(1, 𝑛 − 𝑘 − 1) > 𝐹).Arestrictionisgivenbyalpha;ifthep-valueis
greaterthanalpha,thenullcannotberejected.
Whendecidingifaresultissignificant,thecombinationofanFstatisticandthep-valueiscrucial.If
onlythesignificanceoftheFstatisticisstudied,theresultmightbecontradictory.Thisisduetothe
factthattheFstatisticisthejointeffectofallvariables.IfFissignificant,theimplicationisnotthat
allvariablesaresignificant.(Andale2016)
Criticism
ThehypothesistestperformedbyanalysingtheFstatisticandthep-valueisinefficient.Partofthe
resultsofmentionedteststhatarenotaccurate.Ifthep-valueindicatesthatthehypothesismaybe
rejectedwith95%certainty,thereisa5%chancethatitwouldbewisetonotrejectit(TheTrustees
ofPrincetonUniversity2007).
Due to described imperfection, it is advisable to strengthen the result with an effect size of the
investigatedcoefficient,seesection2.2.7.(NakagawaandCuthill2007,LevineandHullet2002)
ETA-squared
Studies of the effect covariates have on the result may be determined by computing partial eta
squared,alsocalledeffectsize.Ifaregressionisrunonafullmodelandonewishtoseetheimpact
ofremovingonevariable,theeffectsizemaybeexpressedasfollows:
η> =|ê∗|> − |ê|>
|ê∗|>
In the described function, |ê|>represents the sum of residuals for the full model whilst |ê∗|>
represents the sumof residuals for the reducedone. Theeta squared is calculated separately for
each factor and a high value indicates that the concerned covariate has a large effect on the
responsevariable.(Lang2015)
AkaikeInformationCriterion
Whencomparingdifferentmodelsandinvestigatingwhichcovariatestoinclude,acommonmethod
is the Akaike Information Criterion test (AIC). The method calculates an estimation for the
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“informationlost”whenapplyingacertaincomposedmodeltoananalysisandthismodelisnotthe
theoreticallyperfectone:
𝐴𝐼𝐶 = 𝑛 ∙ ln |ê|> + 2𝑘,𝑛 = numberofobservations, 𝑘 = numberofcoefficients
HencethelowerAIC-value,thebetter.Tocompareafullmodelwithareducedone,idestamodel
whereoneorseveralofthevariablesareremoved,anexaminationofthedifferencesbetweenthe
twoAIC-valuesisperformed.Inthispaperthis∆𝐴𝐼𝐶-valueiscalculatedbysubtractingtheAIC-value
forthereducedmodelfromtheAIC-valueforthefullmodel:
∆𝐴𝐼𝐶 = 𝐴𝐼𝐶yz{{|}~�{ − 𝐴𝐼𝐶��~z��~|}~�{
Hence,if∆𝐴𝐼𝐶islargerthanzero,themodelshouldbereduced.If∆𝐴𝐼𝐶issmallerthanzero,thefull
modelshouldbekept.(Lang2015)
The purpose of the method is not to test a null hypothesis; it rather displays the model that
minimisestheestimated“informationloss”.(SnipesandTaylor2014)
GoodnessofFit
ThemeasureGoodnessofFit is theamountofvariationexplainedbythecovariates. It isgenerally
calledtheCoefficientofDetermination:𝑅>,andisusedtoanalysethelinearapproximationfroman
OLS-estimation. The coefficient is referred to as themeasureofGoodnessof Fit since it indicates
howwellthelinearestimationfitsintothegivenobservations.Hence,thelarger𝑅>,thebetter.
The explanation partof𝑅> is computed as the difference between the sum of residuals of a full
model|ê|>andtheonefromaregressionrunwithonlytheintercept,|ê∗∗|>.Therelativesizeofthe
mentioneddifferenceiswhattheexpression𝑅>represents:
𝑅> =|ê∗∗|> − |ê|>
|ê∗∗|>
(Lang2015)
16
Anadjustment for degrees of freedom in 𝑅> is a method that may be used to increase the
understandingasanewcovariateisincludedinthemodel.Thevaluereceivedafterthementioned
correctionisdenotedAdjusted𝑅>.Thismeasurementiscalculatedasfollows:
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑𝑅> = 1 −𝑆𝑆���%~z�{𝑛 − 𝑘
𝑆𝑆�}��{𝑛 − 1
𝑆𝑆 = standarderror, 𝑛 = numberofobservations, 𝑘 = numberofcovariates
(GraphPad2016)
Itcomparesthedescriptivepowerofmodelsincludingdifferentvariables.TheAdjusted𝑅> isoften
usedtostudyifareducedmodelismoreeffectivethanthefullone.(Investopedia2016)
2.2. AnalysisofMarketStrategy
2.2.1. SWOT
TheSWOTanalysisisintroducedinthe1950sbytwoHarvardBusinessSchoolPolicyUnitprofessors,
George Albert Smith Jr and C Roland Christiensen (Friesner 2016). It is a tool often used for
constructingacompany’smarketstrategy(Finlay2000).Theanalysis isdividedintoastudyoffour
important factors of a company: Strengths,Weaknesses,Opportunities and Threats. (Skärvad and
Olsson2013).Thestrengthsandweaknessesareobserved inan internalperspective,whereas the
opportunitiesandthreatsareexternalfactors(Sjöberg2016).
When analysing the internal part, it is common to examine the resources of a company. These
provide a good view of the current state inside an organisation (Fallon Taylor 2016). Financial,
human,physicalandimmaterialresourcesareoftenthefactorsbeingstudiedtohelpbuildthisview.
Financial resources represent money and money placement whilst human resources are the co-
workers’abilities,skillsandknowledge.Examplesofphysicalresourcesarefacilitiesandinventories
andimmaterialresourcesstandforbrand,goodwilletcetera.(SkärvadandOlsson2013)
Theexternalpartoftheanalysisisdifferent,itmainlyrepresentsfactorsacompanycannotcontrol.
Thiscouldbewhetherthenationaleconomyisstrongorweak,howthemarkettrendsdevelopalong
withnewtechnology,politicalregulations,fundingfromdonorsetcetera.Otherexternalfactorsare
17
easierforthecompanytoregulate,thismaybewhichtargetgroupisreachedandtherelationship
withsuppliers,partnersandcustomers.(FallonTaylor2016)
2.2.2. 4P’s
The4P’sinmarketing,alsocalledmarketingmix,isoriginallyintroducedbyJeromeMcCarthyinthe
1960s (Acutt and Kuo 2015). Themodel contains four factors, Price, Product, Place (Distribution
channel)andPromotion.Pricemaybeanalysedbystudyingwhichfactorsare influencingtheprice
setting,whichmethodsareusedwhendeterminingthepricesandpricedifferentiation.Examining
Product isperformedbyevaluatingtheclassificationoftheproduct,thebrandandtheproductlife
cycle. Place includes determining which channels to use when selling and marketing concerning
product or service and if middlemen are to employed. Promotion studies the sort of selling and
communication performed in a company – if it is made personally, which kind of commercial is
establishedetcetera.(SkärvadandOlsson2013)
2.2.3. PENCILS
PENCILS is amodelwithin thePromotion factor in the4P’softenused to summarizea company’s
publicitystrategy.ItisanabbreviationforPublications,Events,News,CommunityRelations,Identity
Media, Lobbying and Social Investments. The model is constructed by the famous marketing
professorPhilipKotlerwhilecomparing theneed foradvertisingandneed forPublicRelations.He
arguesthatadvertisinghasbeenoverdoneandPublicRelationsunderdone,thatpeoplearefedup
withads.(Kotler2005)Thefactorsinthemodelisfurtherexplainedbelow.
Publications deals with all documents the company issues, exempli gratia brochures or financial
statements.Eventsincludesallhappeningsacompanyattends,bothin-houseandexternal,suchas
sportseventsandcustomerdinners.Newsexplainsorganisationnews,co-workernewsandallother
developmentand innovationoccurring inacompany.Community relations canbesponsorshipsof
organisationspromotingwelfareorsomethingelsesupportingasociety,forexamplealocalfootball
team.Identitymediapresentsthebrand,profileandidentityofacompanyinformsofsymbolsand
media such as uniforms, letters and signs. Lobbying indicates a company influencing political,
financial or other decisionmakers in order to benefit the company. The last factor, called Social
investments,mainlyconcernsinvolvementinimportantissuesinsocietyandimprovingthegoodwill
andcorporateculture.(SkärvadandOlsson2013)
18
3. Methodology
Thissectionisdividedintotwoparts,firstaquantitativeresearchwhereamathematicalanalysisis
performed.Second,aqualitativeresearchincludingastrategicmeetingandaninterview.
3.1. LiteratureStudy
To obtain a deep understanding of multiple regression analysis and market strategy models a
literaturestudyisperformed.Thepre-studyismainlybasedonthebooksFöretagsekonomi100and
Elements of Regression Analysis, laying a foundation for the entire thesis. By the means of this,
furtherevaluationof the topics is doneusing severalother reliablebooks, articles andwebpages,
seesection9foracompletelist.
3.2. QuantitativeResearch–MultipleRegressionAnalysis
3.2.1. MainModel
The regression analysis aims primarily to produce a finalmodel for the averageoccupancy of the
threefacilities,independentlyofpricecategory.Asfromnow,thisisreferredtoasthemainmodel.
VariableSelection
Intheregressionanalysistheinitiatingactionischoosingwhichvariablesthatistobeincludedinthe
originalmodel.Firstly,theresponsevariable isdescribedandmotivated.Bythemeansofthis, the
explanatory variables can be obtained. In this section a definition to every variable is contained.
Considerationtoendogeneityistakenwhenchoosingeveryvariable.
Occupancy
StayAt’sOccupancyisnaturallychosenastheresponsevariableinthemainmodel.Theoccupancyis
definedasnumberofsoldroomspermonthdividedwithnumberofavailableroomspermonth.This
makesthemodeladaptabletodatafromallthreehotelfacilities;Bromma,KistaandLund.
RelativePrice
ThePricevariableforthemainmodelischosenasaratio:averageroomratedividedwithaverage
roomrateforStayAt’smaincompetitiveset,seesection1.1.
19
OccupancyCompetitiveSet
This variable is expressed in percent and is computed as the average number of sold rooms per
month relative to the averagenumber of available roomspermonth for the competitive set, see
section1.1.
Stockholm
Stockholm is adummyvariable takingon the value1 if the concernedhotel is StayAtBrommaor
StayAtKistaand0ifitislocatedinLund.
Season
TheSeason-parameterhas threevalues,one for lowseason,one formediumseasonandone for
highseason.ThedefinitionforSeasoninthissectionisnottheclimatologicalseasons.Itisthehigh-
and low- seasonsdefinedby thehotelmanagement (Schwalm2016). January, July andDecember
aredefinedaslowseasons,takingonthequalitativevalue1.February,JuneandAugustaremiddle
seasonswithdefinedqualitativevalue2.March,April,May,September,OctoberandNovemberare
classifiedashighseasonandobtainqualitativevalue3.
Weather
Weatherisavariableexplainingtheaveragemonthlytemperatureinthegeographicalareainwhich
thehotelislocated.
NearbyEvents
The Nearby Events variable is assembled as the number of days per month where events are
present.Thedefinitionofaneventisconcerts,exhibitions,marathons,variousbusinesseventsand
festivals. The duration of the events varies; therefore, the above mentioned summary of days
describesthevariable.
Economy
TheEconomyvariableisdefinedbytheSwedishInstituteofEconomicResearch(Konjukturinstitutet)
barometer indicator of themood of Swedish economy. These values are produced by indications
fromhouseholdsandcompaniesandarestandardisedwithmeanvalue100andstandarddeviation
10.(Konjukturinstitutet2016)
20
MonthsonMarket
Thisvariableisdefinedasnumberofmonthsfromthedatethefacilitiesopened.Note:itisthetime
fromwhenthefacilityisinaugurated;itisnotwhenthere-establishingofthehotelisperformedin
2010.Seesection1.1forfurtherinformation.
NPS
The Net Promoter Score variable is a measurement the hotel uses to evaluate the customer
satisfaction.Thescoreisonascalebetween-100and100,apositiveNPSisonegreaterthanzero.
AnexcellentNPSisascoregreaterthan50.(SatmetrixSystems2016)
MarketShares
TheMarket Share variable is a relation between StayAt and the competitive set’s revenue per
availableroom.Thestatedrevenueistheaverageratemultipliedwithoccupancy.
CollectionofData
This paper studies an average of the three facilities instead of them separately to delimit the
research.HistoricaldataforallvariablesexceptforWeatherandEconomy isreceivedfromStayAt.
TheweatherdataisobtainedfromSMHIandtheinformationonweak/strongeconomyinSwedenis
collected from the Swedish Institute of Economic Research. All data is collected from January 1st
2011toJanuary31st2016,and isdividedmonthly.Thereasonfornotgatheringdata fromearlier
dates is the reformationof the company inApril 2010,when the current concern is founded, see
section 1.1. The resultswill bemore accurate and relevant for StayAt if all data is obtained from
afterthisdatesinceextensivereformationsweremade.
OriginalMainModel
Theregressionequationandagraphicalviewofthevariablesaredisplayedbelow.
𝑦 = 𝛽9 + 𝑥4𝛽4+. . . +𝑥49𝛽49
21
MainModel
𝑦 Occupancy(%)
𝑥4 RelativePrice(%)
𝑥> Season
𝑥� Weather
𝑥� NearbyEvents
𝑥� Economy
𝑥� MonthsonMarket
𝑥� OccupancyCompetitiveSet(%)
𝑥� NPS
𝑥� Stockholm
𝑥49 MarketShares(%)
TestingtheVariablesandReducingtheModel
Whentestingthemodel,thecomputerprogramRisused.
QuantileQuantile-plotandVarianceInflationFactor
Theinitialstepistocontrolifthemodelisapprovedbythelinearregressionassumptions.Anormal
QQ-plotofthestandardisedresidualsiscreatedinRandtheresultindicateswhethertheerrorsof
the model are normally distributed, ergo if the model is approved by the normality- and
homoscedasticityassumptions.ToapprovethemulticollinearityassumptionaVIF-testisperformed
andanexaminationifanyvariableshouldberemovedismade.
BetaEstimates,p-values,ETA-squaredandConfidenceIntervals
Estimates of the betas, p-values, ETA-squared and confidence intervals for the regression
coefficients is accumulated in R by themeans of an F-test. These generates a comprehension of
whichvariablecoefficientsshouldbeincludedinthezerohypothesis𝐻9,idestwhichcovariatesthat
maybeinsignificant.Therisklevelchosenforalltestsis0,05andthelimitforETA-squaredisavalue
greaterthan0,02.
AkaikeInformationCriterionandAdjustedR2
To examine which covariates should be removed an AIC-test is performed and adjusted R2 is
calculatedforbothfullandreducedmodels.
22
3.2.2. CategoryModels
Toaid theanalysisofStayAt’smarket strategy,anexaminationofdifferences in factors impacting
occupancy depending on price category is pursued. This indicates that there are three additional
regressionmodelsandresponsevariables:one forDaily,one forExtendedandone forLongTerm
stays. These are referred to as category models. The category models will only be used for
determiningthedifferencesdependingonpricecategory,notforregression inferences.Therefore,
finalmodelsarenotnecessary.
VariableSelection
Occupancy
TheresponsevariablesinthecategorymodelsaretheOccupancyforrespectivelengthofstay.Itis
calculatedasnumberof sold rooms in respectiveprice categorypermonthdividedwith the total
numberofavailableroomspermonth.
ExplanatoryVariables-CategoryModels
Theexplanatoryvariableschosenforthecategorymodelsarethesameas inthefinalmainmodel
except for two factors. In the category models the number of sold rooms per price category is
investigated and therefore theoccupancy ismuch smaller.Hence, theOccupancyCompetitive Set
variable isnot included in thecategorymodels since theyarenotcomparable.Theother factor is
changingthecovariateRelativePricetoPrice.Thereasonforthisisthatdatafortheseparateprice
categories of the competitors is not available, only an average. This covariate is defined by the
differentpricecategoriesforrespectivelengthofstay.
Thefullcategoryequationsareexpressedasfollows:
𝑦 = 𝛽9 + 𝑥4𝛽4+. . . +𝑥�𝛽�
SeeTable1insection10.1foragraphicalviewofthevariables.
RunningtheRegression
Toexamine themodels, the linear regressionassumptions firsthad tobeapprovedwithQQ-plots
and VIF-tests. Thereby, the beta-estimates, p-values and ETA-squared could be evaluated. As
mentioned, the models are only used to examine differences and therefore there is no need to
performAIC-testsorcalculate𝑅>toremovevariablesfromthemodel.Theinterestinginformation
fromthesemodelsarethedifferencesinbetaestimates,p-valuesandETA-squared.
23
3.3. QualitativeResearch
3.3.1. MeetingwithManagementofStayAt
Toenhancethecomprehensionofthecompany,ameetingwithtwoofthekeymanagersatStayAt
Apartments is held February 2nd 2016. The Deputy CEO,Michael Schwalm, and the Commercial
Manager,NiklasFrisell,presentthecompany’sdevelopmentandupcomingexpansion.Information
about current goals and overall vision is received. By means of this, a discussion about possible
researchquestions is raised.Themeeting lastsabout threehoursand is intentionallyheldearly in
the process of this thesis since it functions as a foundation for the problem formulation. It is
important to lay a stable groundwork for the research question since it impregnates the entire
paper.Whoever possess power of the problem formulation also bear the largest influence of the
resultsastheproblemprivilegeclaims(Gustafsson1989).
3.3.2. InterviewwithDeputyCEOatStayAt
Furtheralongintheprocessofthepaper,atApril14th,atwo-hourinterviewwiththeDeputyCEOof
StayAt is held. The purpose of the session is to obtain detailed information about the company’s
currentbusinessmodelandmarketstrategy.Focusis laidonquestionsconcerningthetwomarket
strategymodels:SWOTand4P’s.
24
4. MathematicalResults
4.1. LinearRegressionAssumptions
4.1.1. QuantileQuantile-plot
Theplotofthestandardisedresidualsisshownbelow.Sincethelineisalmoststraight,idestthereis
a linear relationshipbetween the standardisederrors and the theoretical quantiles, thenormality
assumptionisapproved.
4.1.2. VarianceInflationFactor
Inthissection,theVIFresultsarevisualised.Sevenofthetencovariatesweredirectlyapprovedfor
themulticollinearityassumption.TheVIF-valuesofthevariablesMonthsOnMarket,Stockholmand
OccupancyCompetitiveSetwasclosetofive,whichisthelimitforwhenfurtherinvestigationshould
bedone(MinitabInc.2016).ThefactthatbothMonthsOnMarketandOccupancyCompetitiveSet
wouldcorrelatealittlewithStockholmhavealreadybeenrealisedthough,sincethesetwovariables
arealsobasedonwhichfacilityisexamined;whereitislocated.Despitethisfact,itisconcludedthat
allthreecovariatesshouldbeincludedinthemodelanywaysinceMonthsOnMarketandOccupancy
CompetitiveSetdescribethethreedifferentfacilitieswhileStockholmonlycomparethedifference
betweenStockholmandLund.Inaddition,theremainingtestsperformedprovesthesevariablesto
berelevantwhichfurtherarguesforthecausetokeepthem.
25
OriginalModel VIF
RelativePrice 2.768155
Season 2.532885
Weather 3.002500
NearbyEvents 1.275966
Economy 1.275016
MonthsonMarket 4.742640
OccupancyCompetitiveset 4.674845
NPS 1.923735
Stockholm 4.989523
MarketShares 2.631624
4.2. TestingtheMainModel
4.2.1. EstimatedBetas,p-valuesandETA-squared
The results of the estimated beta-values, p-values and ETA-squared are presented in the table
below.TheindicationofthisistoexamineifNearbyEvents,EconomyandNPSarerelevantforthe
model since theirp-valuesarehigh,ETA-squared lowand theirestimatedcoefficientsareclose to
zero.(Thompson2002)
OriginalModelSummary Estimate Std.Error Eta.sq p.value
(Intercept) 3.464189e-01 0.1023685184 0.10054 0.0009
RelativePrice -6.715507e-01 0.0805634844 0.46844 0.0000
Season 2.928637e-02 0.0093973909 0.07956 0.0021
Weather 2.511372e-03 0.0011569987 0.03499 0.0313
NearbyEvents 4.400136e-04 0.0006102560 0.00336 0.4719
Economy 9.198599e-05 0.0006729116 0.00011 0.8914
MonthsonMarket 8.716381e-04 0.0002561764 0.07841 0.0008
OccupancyCompetitiveset 7.529508e-01 0.0944690071 0.37263 0.0000
NPS 6.716594e-06 0.0004084403 0.00000 0.9869
Stockholm -2.317027e-01 0.0192297613 0.46527 0.0000
MarketShares 4.933680e-01 0.0342054941 0.61971 0.0000
26
4.2.2. ConfidenceIntervals
TheresultsfortheconfidenceintervalsarestatedinTable2insection10.1.Theconfidenceintervals
forthesameregressioncoefficientsmentioned insection4.2.1.allcontainzerohencethenull for
thesecovariatescannotberejected.Theconclusiontoexaminethesecovariatesrelevanceisfurther
supported.
4.3. ReductionoftheModel
TheregressioncoefficientsbeingtestedforzeroaretheonesforNearbyEvents,EconomyandNPS
bytheargumentspresentedabove.
4.3.1. AkaikeInformationCriterion
In this test,∆𝐴𝐼𝐶 iscalculatedforthemodelswhererespectivevariabletestedfor irrelevancehas
beenremoved.The∆𝐴𝐼𝐶isalsocalculatedforthemodelwhereallthreevariablesareremoved.As
seeninthetableall∆𝐴𝐼𝐶valuesarelargerthanzero,implyingthatallvariablesshouldberemoved
fromthemodel.Oneadditionaltestisperformedbeforemakingthisdecisionthough.
Removedvariable NearbyEvents Economy NPS NearbyEvents,EconomyandNPS∆AIC 1,383872 1,979247 1,999695 5,348732
4.3.2. Adjusted𝑹𝟐
In the table below, adjusted𝑅> values for the fullmodels aswell as themodelswith concerned
covariates removed are shown. By means of these results, the implication from 4.2.1. is further
strengthened. All 𝑅>-values increase as the variables are removed. The largest enhancement is
obtainedwhenallthreeofthemisremoved.Therefore,itisdecidedtomovethesecovariatesfrom
themodelinlackofrelevance.
Removedvariable NearbyEvents Economy NPS NearbyEvents,EconomyandNPSAdjusted𝑹𝟐 0.8438 0.8443 0.8443 0.8455
Adjusted𝑹𝟐FullModel 0.8434 0.8434 0.8434 0.8434
The final𝑅>-value is 0,8455,which indicates that theoccupancy is highly explainedby themodel
produced.
27
4.4. FinalMainModel
Inthissectionthefinalmainmodelispresented.SeeTable3insection10.1fortheestimatesofthe
coefficients, standarderrors,ETA-squaredandp-valuesandPlot1 in section10.2 for theQuantile
Quantile-plot.
Occupancy=0.3648-0.6728·RelativePrice+0.0297·Season+0.0026·Weather+0.0009·Months
onMarket+0.7483·OccupancyCompetitiveSet-0.2316·Stockholm+0.4906·MarketShares
4.5. CategoryModel
4.5.1. LinearRegressionAssumptions
QuantileQuantile-plot
Insection10.2 theDaily,ExtendedandLongTermQQ-plotsaredisplayed, referred toasPlot2,3
and4.Ascanbeseeninthese,allmodelsareapprovedofthenormalityassumptionsincethereisa
linearrelationshipbetweenthestandardisedresidualsandthetheoreticalquantiles.
VarianceInflationFactor
As onemay see inTable4, section 10.1, theVIF-values are all low. Therefore,multicollinearity is
dismissedforthecategorymodels.
4.5.2. DifferencesinRegressions
Concludedfromthedisplayedtablebelow,themostdistinctivedifferencescomposedfromrunning
theregressionsarethefollowing:
● FortheDailyOccupancythevariablesLow/HighSeasonforthehotelandMarketSharesare
notasrelevantasitisfortheExtendedandLongTermOccupancy.
● IntheExtendedandLongTermmodelsWeather isirrelevantwhereasintheDaily ithasan
impact.
● The last relevant difference noticed is that thePrice covariate is not as significant for the
LongTermstayasitisfortheDailyandExtended.ThevalueofETA-squaredforthecovariate
inLongTermwashigher than inDaily.Although, the confidence interval showed that the
null hypothesis could not be rejected for the Long Term Price, hence the conclusion is
strengthened.
Primarily,theresultsarebasedonp-valueandETA-squared.Butincasetheseresultsareconflicted,confidenceintervalsaretakenintoaccount,seeTable5insection10.1.
28
Daily Estimate Std.Error Eta.sq p.value
(Intercept) 4.596590e-01 2.517435e-02 0.56497 0.0000
DailyPrice(1-4days) -2.611038e-05 8.080908e-06 0.01270 0.0015
Stockholm -4.792214e-02 1.507301e-02 0.06237 0.0017
Season 6.505140e-03 4.198131e-03 0.01118 0.1231
Weather 3.050437e-03 5.176522e-04 0.16907 0.0000
MonthsOnMarket -2.153127e-03 1.461029e-04 0.53497 0.0000
MarketShares -7.075996e-03 1.724625e-02 0.00072 0.6821
Extended Estimate Std.Error Eta.sq p.value
(Intercept) 0.3043020004 7.378198e-02 0.10985 0.0001
ExtendedPrice(5-29days) -0.0003502485 8.876393e-05 0.12416 0.0001
Stockholm 0.0594250944 1.881643e-02 0.04597 0.0019
Season 0.0628790036 6.543170e-03 0.33764 0.0000
Weather 0.0006900965 7.777748e-04 0.00482 0.3761
MonthsOnMarket -0.0006593034 1.724785e-04 0.04819 0.0002
MarketShares 0.1189444976 3.275107e-02 0.08579 0.0004
LongTerm Estimate Std.Error Eta.sq p.value
(Intercept) -0.1430516865 0.0983190480 0.01964 0.1475
LongTermPrice(>29days) -0.0002872128 0.0001639458 0.02963 0.0815
Stockholm -0.1855890739 0.0216101307 0.27203 0.0000
Season 0.0664863443 0.0066956415 0.33642 0.0000
Weather -0.0015519374 0.0008925823 0.01902 0.0838
MonthsOnMarket 0.0043134249 0.0002294939 0.60072 0.0000
MarketShares 0.1441220846 0.0328317389 0.09591 0.0000
29
5. InferencesfromtheRegressionAnalysis
RelativePrice
SinceOccupancy is chosen as dependent variable, price seems to be one of themost important
influencingvariables.Whenchoosinghotelthisisoneofthemostcommonandconspicuousfactors
toconsider.AccordingtoLang’stheoryThoushaltknowyourdata,pricehasanegativeinfluenceon
occupancy(Lang2015).Therefore,examiningtherelationbetweenpricesseemsmorerelevant.The
measurementbecomesuniversalandstandardized.
TheresultimplicatesthatRelativePricehasanegativeeffectontheoccupancy.Itdemonstratesthat
price is a delicate factor when attracting customers. The coefficient of the covariate proves that
customersareselectiveregardingpricewhenchoosinghotel.
Insection3.1.1.underVariableSelection,asthepricecovariateischosen,theriskofsimultaneityis
takenintoaccount.It isknownfromeconomictheorythatifdemandraises,pricewillraiseaswell
(KrugmanandWells2013).Although,sinceavarietyoftestsshowthatthecovariateisstillseverely
significantitisincludedinthemodel.
Season
Thevariablehasapositiveinfluence,indicatingthattheoccupancyincreasesastheseasonishigh.
The result is predictable but the variable is still relevant due to the extent of the effect on the
occupancy.Consideringtheresult,StayAtmayusetheinformationtoputeffortonthemarketingas
they are approaching low season. Equivalent to the Relative Price variable, awareness of
simultaneityispresentwhenchoosingthiscovariate.Theseasonsaredefinedbytheaveragelevelof
occupancyand therefore there isa riskofendogeneity.Themotivation forkeeping thevariable is
thesmallstandarderrorandhighETA-squared.
Weather
Warmweather contributes positively. Itmay be concluded that the factor is advantageouswhen
computing a market strategy to attract end customers to the hotel. The significance of weather
unleashesstrategyimprovementsastherelevancemayrevealunderstandingoftheguestschoosing
StayAt when visiting Sweden. The data for the variable is expressed in degrees Celsius. Data for
hoursofsunpermonthisnotavailableandthereforeexcludedfromthevariable.Therefore,itmay
notbecompletelytranslatedto“weather”butstillcontainssignificance.
30
MonthsonMarket
The variable has a favourable impact on the demand, implicating that awell-established brand is
beneficial in this market. Another interpretation of this variable is that tourism in Sweden has
increased.Thecovariaterepresentsthepassingoftime,implyingthattheconclusionsaremany.The
maininferenceisthatexperienceisvaluableinthismarketandmaybeusedinfuturestrategywork.
OccupancyCompetitiveSet
The sign of this variable is positive, supposing that increased demand for adjacent competitors is
beneficial for StayAt. An implication is that the branch seasons are synchronised. This inference
strengthens the aim of this study. The need for a strong brand is essential and B2Emarketing is
evidentlyimportantforincreasedpenetrationpowerandoccupancy.
Stockholm
The variablehas a negative influenceon the result.Hence, StayAthavebetter occupancy in Lund
than inStockholm.Theobservationaladvantagetoexploit isevidently the importanceof location.
SinceStayAtareexpandingitisofgreaterinteresttostudythewiderdefinitionoflocation,tostudy
sub-locations separatelywould not be as relevant in this project. Further, sincemost of the used
data is obtained from each individual hotel, multicollinearity would be present if separating the
variablesintosub-locations.TheestablishedvariablemayimplicateotherpropertiesthanStockholm
versusLund,basedoncommonfactorsfortheStockholmhotelswhichdiffersforthefacilityinLund.
An example is that the Stockholmhotels are open 24 hours a day,whilst the Lund facility is not.
AnotherdifferenceistheproximitytoDenmarkandWesternEurope.Thisisthedownsidewiththe
inclusionofadummyvariable.
Despite of abovementioned criticism, the regression result eventuates that the dummy variable
Stockholm, issignificantforthehotel’soccupancy.Thevariablehasaprincipallynegativeeffecton
the occupancy, indicating that the StayAt brand and the general standard of the hotel may be
superior in Lund. Another implication is that the Stockholm hotels might be exposed to a more
competitivemarket. A further explanationmay be that the location of the hotel in Lund ismore
centralandattractive.
MarketShares
Thepositivecoefficientdemonstratesthatan increaseofthehotel’spenetrationpowerrelativeto
the competitive set will generate an increase of the occupancy as well. This indicates that the
31
greaterthecompanyhasbecome,thegreateritsoccupancywillbe.Itleadstoawell-knownbrand
which attractsmore customers. Themain conclusion of the variableMonth onMarket is thereby
strengthened.
NearbyEvents
TheNearbyEventvariableisreducedfromthemodel;itdidnothaveasignificantinfluenceonthe
hoteloccupancy.Itisnaturaltoassumethatadesirableeventwouldinfluencetheoccupancydueto
thewiderrangeofpeoplearrivingtoconcernedarea.Thereasonforthepoorrelevanceisprobably
duetoincompatibilityofthedata.Theinformationforthevariableiscompiledondatasorteddayby
day,andthensummarisedtoamonthlyvalue.Thehotelsareoccasionallyfullybookedasaneventis
present but that do not affect themonthly occupancy enough to be considerable. If the variable
instead would consider only events with longer durations, the occupancy may be influenced
significantly.
Economy
The economic situation in Sweden does not show enough relevance to be significant. An initial
assumption is that theeconomy inSwedenhasan impactsince itmay influence incomingtourists
and internationalconsultants.The interpretationoftherejection is thatthedatadoesnotcovera
timeperiodlongenoughtoperceiveafluctuationintheeconomy.
NPS
Thecustomersatisfaction isrejectedfromthemodel. Intuitively,onemayassumethattheguests’
judgementofthehotel isrelevantsincetheywouldrecommendittoothers.The interpretationof
theresult isthatthedatais incoherent.Thevariableshowsanaveragefortheentireorganisation,
whichdoesnotgenerateafairinterpretationoftheseparatehotels.
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6. DiscussionandMarketStrategyAnalysis
Inthischaptertwomainmodelsareused:SWOTanalysisand4P’s.TheSWOTanalysisgeneratesan
overallviewofthecurrentmarketsituationforthecompany.The4P’sexaminemorethoroughlythe
practical capacities of the company which may be developed. Further, within the 4P’s-method
concreteanalyticalunderstandingisfoundusingPENCILS(SkärvadandOlsson2013).Allmodelsare
influenced with the results and inferences from the mathematical analysis to strengthened the
arguments.
6.1. SWOT
Strengths
WhenobservingStayAt’sstrengthsthemostobviousfactoristheircustomerrelationships.Theyare
established on the market towards companies employing international consultants and have
excellentcontactwith these.Theirability tobeperceptive, communicatewithdifferentaudiences
andadapttothecustomers'requests isdefinitelyastrength.This ispartofthe ideawithcontract
arrangements(Frisell2016).Thecompanyhasalemmacalledworkandlifebalance.Sincemostof
theircustomersare inStockholmorLund forwork,andarenewtoeither thecountryor thecity,
StayAt raise the importanceofbeingable to liveanormal lifeandbalancing free timewithwork.
This is proved to be appreciated among the guests and is a very valuable resource. The heart of
StayAt are the Extended and Long Term residences but there is capacity for the Short Term
accommodationaswell,whichthecompanyiseagertotakeadvantageof.Thisisfurtherexamined
inOpportunities.
Another strength of the company is the desire to do well and the genuineness among the co-
workers,astheDeputyCEOexplains.Accordingtohim,thereisincredibledriveandurgeamongthe
staff.Byprovidingallco-workerswithcontinuousfeedbackontheirworkandtherebyencouraging
them to increase their independence, the company may make further use of this potential. The
hotel’s well thought out geographical positions is another valuable acquisition. Readmore about
locationinsection6.2.
Weaknesses
Not being large enough is aweakness for StayAt today, this is further discussed inOpportunities.
AnotherfactortoconsideristheinsufficiencyinmediatingwhoStayAtare,whattheydo,whyand
how; idestarticulatingtheirbrandwithintheB2Efield.Thereis internalongoingworkto improve
thistoday,theStayAtAcademy.TheAcademyisaworkshopforco-workersatthecompanywhere
33
visionandbrandarefilledwithconcretevalueandstories.Visualisingthebrandtowardsthepublic
isnotaswell-developed,andthisisespeciallyessentialinanexpansionphase.
To furtherdiscussexpansions,onemayenlightenacase studyof IKEAwhere it ismentioned that
scaleandsizecanbeseenasadownside.Thelargertheorganisationgets,theharderitistocontrol
the standards and qualities (Business Case Studies 2016). This is a perspective the companymay
want tokeep inmindwhilegrowing. One lastweakness identified is thatStayAtdoesnotexploit
theirNPS-systemcompletely.Afollow-upisnotperformedwiththepeoplegivingfeedback.Having
a continuous dialoguewith customers generates additional value since it brings forth themissing
partsinaproductorservice.
Opportunities
Asmentionedinsection1.1,thehotel’smeanoccupancyisabout77%andifStayAtcanreachoutto
abroadercustomersegment,alargepotentiallieshere.Thereiscapacitywithinthecompany,which
isbroughtupinStrengths,andtheDeputyCEOdeclaresthemarketdemandexist.Thisopportunity
ismainlybasedontheexistingfacilitiesandconceptsbutthereliesmanyinthefutureexpansionas
well,bothconcerningofferingsandgeographicallocations.
As stated, there is further capacitywithin StayAt and they have a thrive for expanding (Schwalm
2016)buttheirbranchhasadifficulttimedeveloping.ThebranchreferredtohereistheExtended
andLongTermStayHotels.Becauseofthistheyhavenotyetbeenwelcomedintothehotelsegment
andtotheforumswheretheyneedtobeheardandacknowledged.Itisessentialforthemtoshow
their Unique Selling Points (USP) to relevant decisionmakers in order to expand, see section 6.3
Lobbying.
Threats
ProblemsandbarriersinthelabourmarketisoneofthethreatstoStayAtandtheentirebranch,for
example if thegovernmentsharpentherules forworkingvisas.Creatingthesekindofobstacles is
usuallyunfavourableforthebusinessworldandthereforealsoforthenationintheend.Anexample
of thismatter iswhen Spotify threatened tomove to theUS because of political barriers, one of
which being the shortage of residents in Stockholm. This would have been costly for the entire
country because of losses in tax incomes, occupations, innovation and human resources (Ek and
Lorentzon).
34
WhenanalysingStayAt’sthreatsitisclearthatcompetitorsisnotthemostessentialone.Sincethe
marketforExtendedandLongTermresidencesisstillrelativelysmall,itisratherbeneficialforStayAt
ifothersweretoenterthefield.Thisindicatesthatgrowthofcurrentcompetitorsandentranceof
newonesisbothathreatandanopportunity.Onemaycomparethisconclusionwithacasestudyof
McDonald’sentering theChinesemarket.Thethreat ispresentdueto theseverecompetitionbut
opportunities arises as more consumers embrace the new concept, enabling others to succeed
withinthefield.(GlennandCastle2011)InStayAt’scase, increaseofcompetitorswouldcausethe
branchtogrowandevolve influencebutstillexposethecompanytopressure.AstheDeputyCEO
expresses it, competitors are rather seen as inspiration and motivation. (Schwalm 2016) This is
furthersupportedbythecoefficientofthevariableOccupancyCompetitiveSetinthemathematical
part of this paper,which is positive. That indicates that themore the occupancy of a competitor
increases,themoreitincreasesforStayAt.
6.2. 4P’s
Price
StayAtdonotusea lowcost strategy (SkärvadandOlsson2013), instead theyprioritise to create
greatvalueforthegueststoaslightlyhigherprice.Thepricestrategyisconstitutedinthemanner;
thelongerthestay,thelowerthepriceispernight.TheintentionoftheDeputyCEOishoweverto
change this strategy.His vision is to implementavaluecreating systembypersonalising theprice
(Normann2001).StayAthasthepotentialtoadjusttheirproductforeachindividualcustomerand
consequentlyuseapricestrategyfollowingthedemandoftheindependentguest.
A result from the regression analysis indicates that the price varies significantly for the separate
lengthsof stay, see section4.5. The implication is that thepriceelasticity is greater forDaily and
ExtendedthanforLongtermaccommodation.
An internal question onemight raise is if it is beneficial to decrease the prices for theDaily and
ExtendedcategoryandincreaseitfortheLongTerm.Asmentioned,avaluecreatingsystemistobe
synergised in the organisation and the price is included in the reformation. As the system is
implemented, itmaybeofgreat importancetoregardthattheDailyandExtended ratesarequite
sensitive whilst the Long Termmight bemore flexible for inclusion of packages and increases in
price.Onemust recall that themargin forDaily is the lowestof the threecategoriesand that the
Dailycustomerstypicallypreferalowprice.Thisdilemmaraisesfurtherinvestigation.
35
As stated in section 1.2, further marketing and establishment in the B2E sector is needed. The
customers infocusherearemainlytheshorttermonessincetheendusersareusuallyrentingfor
vacationor temporary stays (Schwalm2016).A lowerprice for this categorywill be an additional
support for breaking into thismarket. On the contrary, the profit of the organisation is naturally
prioritised – especially during an expansion phase (Berk and DeMarzo 2014). The conclusion one
may draw is to keep theDaily price as low as possible and let the guests decide for themselves
whethertoaddbreakfastandotherservices,whichtodayareincluded.Naturally,itisimportantto
regardthatthemarginshouldstaypositivewhilechangingtheprice,seesection1.1.
Thecosts forExtendedandLongTermcategoriesare less than for theDaily (seesection1.1),and
thereforeamoreadjustablepricingstrategycanbeconducted.Asmentioned,theregressionshows
that the price elasticity is lower for the Long Term category, therefore it may be advisable to
primarilyletthisvariablebethemostflexiblewhilepersonalisingthestrategy.
Place
“Weowntherelations”iswhattheDeputyCEOreplieswhenaskedabouttheirB2Bmarketing.The
hotels current strategy is creating strong customer bonds on a business level, the brand is
established via personalised marketing and mainly spread mouth to mouth. Concerning the
outsourceddistributionchannels,StayAthaslesscontrol.Theyactonalargeandunfamiliarmarket
wheretheyhave littlepenetrationpowertoday(Schwalm2016).Thewellestablishedbrandshave
anadvantagehereduetotheirdirectmarketingtowardstheendcustomer.Whatpeopleseewhen
looking for a hotel using the channels available is a picture, geographical location and price. The
StayAtbrandisnotstrongenoughinthemarkettostickoutinthisarea.Itisdifficulttoshowtheir
exceptionalbrandsinceitisinternallyembedded.
Geographically, the hotels are strategically located in proximity to industrial areas. For StayAt’s
primary clientbase this is especially invitingdue to the closeness towork. For theB2E customers
though,the locationneedstobearguedfor.Thehotelsarecentrally locatedbutperchancenot in
the most pleasant areas. Due to mentioned fact, the marketing might enlighten factors such as
proximity to metro, attractions and nature. This is something StayAt can use the distribution
channels for by highlighting the beneficial aspects more consciously. The collaborations with
distribution channels are costly and therefore it ismeaningful to exploit the relation asmuch as
possible.
36
TheStayAtwebsiteisaplatformusedmostlybythefrequentindependenttravellers(Schwalm2016)
and the opportunities here are nearby endless. If the website is thoroughly promoted, the hotel
mightpenetratethemarketsegmenttheyareexcludedfromtoday.Simplicitycanhelpcustomers
understandtheStayAtbrand,furtherexplainedinsection6.3.
Product
StayAt’sproductistheiraccommodation;itiswhattheycallthehardware.Although,asmentioned
insection1.1,theexperienceofthestay–thesoftware–isthefocus.Thehotelisnotonlyofferinga
place to sleep but a place to live and make use of their spare time. This development is a
requirementinordertodifferentiatefromcompetitors.
Fromtheregressionanalysisitisdeducedthatthenumberofmonthsonthemarkethasapositive
influenceontheoccupancy.ThisindicatesthatStayAt’sproductgrowsinvalueproportionallytoits
time at the market. The result strengthens the differentiation to focus on the software; the
hardwareisrathervaluedhighifitisrecentlyreconstructed.Tofurtherenlightenasimilarcontrast,
thiscanbecomparedtoatechnologicalproduct,whichdecreaseinvaluethelongerithasbeenon
themarket(PorterandHeppelmann2014).
ForStayAt, it isnowimportanttocreateanalignment intheirproducts, idesttoconstructaclear
design of their accommodation and facilities. A standardization of the apartments and lobbies is
necessaryinordertobuildabrandanddefinethecompany’sessence.
Promotion
The B2Bmarket communication of StayAt is strongly connectedwith the brand. Due to the fixed
supply, they have a push-strategywhich is combinedwith a personalised selling process (Skärvad
andOlsson).Thestrategycanthoughbeseenasacrossingbetweenpushandpullsincetheselling
often is pursued through a dialogue. The selling process is designed such that main sales are
managed in the salesdepartment.Anotherplatform for sales is the receptionwhere the selling is
donemouth tomouth. The implication is that StayAt’s currentmarketing is solely advertised via
personalisedcommunicationandmeetings.
The result from the regression analysis of the category models implicates that the high and low
seasonsforthehotelhavenorelevancefortheDailyguests.MarketSharesindicatesthesameand
theinterpretationofthis isthatDailycustomersdonotaveragelyconsiderthese.Onthecontrary,
37
the mentioned factors can be used when advertising towards the Long Term segment, which
generallymainly consists of business customers according to the Deputy CEO. Onemay consider
puttinggreatereffortonmarketingtowardsthesecustomersduringlowseason.Further,itwouldbe
beneficialtoemphasisetheextentofStayAt’smarketpenetration.
TheWeathervariableissignificantfortheDailycustomersaccordingtothecategoryresultsfromthe
regression.Theadvantageonecantakefromthis istoconstantlyhaveanupdatedweatherreport
onthewebsite.Thereportmayincludeconnecting ideasofwhattodoatcertaintemperaturesto
attractendcustomers.
6.3. PENCILS
The Deputy CEO explains that 80% of their accommodation comes from B2B and 10% from
distributionchannels,idesttheendusers.Hisanswertowhatthegreatestlackintheirmarketingis
today is: Penetration. This statement insinuates that the main focus of this section lies within
expanding the communication with the B2E field. To investigate how to reach out to a wider
customerbaseandhowtoselltheroomstheydonottoday,adetailedanalysisofStayAt’smarket
opportunitiesisstudiedwithPENCILS.
Publications
WhenstudyingStayAt’s currentpublicationstrategy it is clear that itmaybe improved.Awebsite
andFacebookpagesareusedtosomeextent,andfinancialstatementsaremadepublicbutnothing
elseisperformed.
The company is greater andmore active than themarket is aware of, see Social Investments for
furtherdiscussion.Thismaybeanimportantfactortowhytheydonotobtainthegreatsuccessin
their marketing towards the end users, the private individuals. First of all, the website can be
developed in severalmatters. Today, there is no general descriptionof the company, its business
model,vision,missionandgoalsdespitethefactthattheyhaveathoroughlyarticulatedsuch.These
factorsarepresentedtotheircontractedcustomerswhensellingbutitneverquitereachestheend
users.Therefore, thismaybedevelopedsothat it isavailableandeasilyaccessedforallcustomer
segments,andthebrandisbetterarticulatedintheB2Esector.Inaddition,thewebsitecouldpost
every event, news happening and action made at the company in order to keep a continuous
update.
38
TheFacebookpagesaredividedbylocations.Thismaycauseanimpressionthatthethreefacilities
arenotcompletelyconnected,whichweakensthebrandsinceanalignmentbetweenthehotels is
thegoal.AttheirFacebookpagetheymayalsopostdailyupdatesofupcomingeventsandplans.
Further, there is definitely room for promotion on additional platforms than used today. Today,
socialmedia is a significant part of company'smarketing strategy (Kaplan 2011).Well established
brandssuchasVolvo,NikeandCoca-Colahavediscoveredthiswayofexploiting.Already in2007,
themobilemarketing revenue totalsUS$2.773million (MishraandGupta2012). If StayAtwere to
followtheseexamplesabreakthroughmaycometotheB2Emarket.Ithasproventobeeffectiveto
keepanactiveprofileateverydigitalmedia.InadditiontomentionedwebsiteandFacebookpage,
importantevents andnewsat the companymaybepostedon Instagram, LinkedInandviaE-mail
Newsletters.Itisimportanttocontinuallykeeptheseupdatesequivalentandsynchronised.
Events
Weeklyeventsareperformedinthereceptionlobbiestopromotefamiliarityamongthecustomers.
Theeventsaredirectedtoallpeoplestayingatthehotel.Thenewsisspreadmouthtomouthand
byputting informationnotes insidethefacilities,nopublicadvertising isperformed.Thisresults in
attendance ofmostly Long Term guests, id estmainly the B2B customers. Themarketingmay be
developedsinceeventscouldbeagoodtoolformarketingandattractingnewcustomers.
Evidentlythehotels’overallfocusistoconsciouslyelevatetheirLongTermguests,mainlyconsisting
of Asian consultants, and the same strategy influences their events. A cricket team is initiated in
ordertobuildasenseofcommunityandtheenquiriesforthiscomefromtheAsianguests.(Mikrut
2015)TheDeputyCEOexpressesaneedformorediversityamongtheguests.Thehoteliscurrently
situated in a lock in effect (Kuhn 1962) created by mainly targeting customers from Asia. If the
economicgrowthinthisgeographicalareadecreases,thehotelscustomerbasewillalsobeseverely
reduced(Regeringskansliet2016).Thereareroomforfurtherevents inthehotelbudget(Schwalm
2016),whichmaybeusedinordertoincreasethediversity.
Anexampleof this is arranging for theguests to visit nearby fairs, to gobowlingor sing karaoke,
nothingextravagant.Theeventsheldoutsidethehotelfacilitiescouldentailasmallerfeesothatit
wouldnotbealargefinancialburden.Anotherexamplemaybetoorganisenon-prestigiouscreative
competitionssuchaspublicationofanInstagrampicturetaggingStayAt.This isgreatpublicitytoa
largediversityforthecompanyaswellasafunhappeningandanopportunitytowinfortheguests.
39
News
In thebeginningof 2016 a newplayroomat thehotel in Kista is inaugurated, thenews is spread
mouth by mouth to in-house guests. The Deputy CEO expresses that emailing information to
individualcorecustomersoccurbutitiscommonlydelayedordisregarded.
Asinnovativeprocessesareimplementedandcompleted,therearenoupdatestowardsthepublic
totellthenews.Thereareplatformsavailable,well-functioninganddesignedinamodernmanner.
ThesearenotusedandthequestionWhyisinstantlyraised.AsmentionedunderPublications,ifthe
hotelwere topublishupdatesof theirongoingplansand finishedgoals,awiderunderstandingof
thehotelvaluemightbeincreasedbythepublic.
CommunityRelations
At the time of the interview the only existing sponsoring is to the cricket community in Sweden.
Other initiatives executed are scholarships regarding innovationswithin hospitality services at the
UniversityofLund.AnexpresseddesireofStayAt’sistodevelopthisareainthenearfuture.
StayAt’scompetitor,ScandicHotel,hasaseparatesectionontheirwebsitededicatedtosponsoring
(Scandic2016).Theimpressionisseriousnessandcommitmenttothesociety.IfStayAtimplements
thesametechnique,anideaistoclearlystatethepurposeofthesponsoringdecisionsandtomake
surethattheircorevaluesarereflectedintheirchoice.HiltonHotelisalsoagoodexampleregarding
sponsoring, at their website they offer an opportunity to apply for financial support (Hilton
Worldwide 2016). This suggests an economic confidence and an impression of security and
innovation.
IdentityMedia
TodayStayAtuseGoogleAdWordstomediatetheiridentity,theirmainhitscomefromthewebsite
(Frisell2016).Asmentionedinsection1.1,theintentionistoilluminatetheexperienceofthestay
andnotthehardware.Further,toenlightentheirestablishmentanextensionofthelogotypemaybe
introducedwhere it ispresentedhow long theStayAtbrandhasexistedon themarket. Since the
variableMonthsonMarketplayedasignificantroleintheOccupancyequation,thisseemstobean
appropriateaction.
40
Lobbying
There is one main actor that businesses in the hotel branch may exploit to influence decision
makers.ThisorganisationiscalledVisita(Nandorf2016).StayAthasadelimitedvotewithVisitadue
to the lackof penetrationpower.Asmentioned in section6.1, thebranchhasnot yet developed
enoughtobecomeanimportantactorintherelevantforums.
One cannotputenough importance to the issueof StayAt’sbrandwithin theB2E field. Thereare
sufficientpathsthatcanbetaken,VisitaisnottheonlyopportunityStayAthasregardinglobbying.
Anapproach isthecommercialand industrial life inSweden,ortodirectlyapproachtheenduser.
Thedilemmaistofindanentrancetocreatesignificantinfluence.
SocialInvestments
ForStayAt,socialinvestmentsaremainlymanagedinacloseperspectivebytakinggoodcareofthe
gueststheyreceive.Asmentionedinsection1.1,mostofStayAt’scustomersare internationaland
newtothecountry.Therefore,creatingahomelyenvironmentforthemisasubstantialpartofthe
company’s social investments. This promotes diversity and amulticultural society. Further, StayAt
work on Corporate Social Responsibility (CSR). An example of this is their involvement in an
accommodation project with UNHCR dedicated the current refugees in Europe. This was not a
plannedprocess,onlyanaction thecompanysawnecessary. (Schwalm2016)Whenengaging ina
matter this important and up to date, it is essential that the company goes public with it, see
Publications.Itiscrucialpartlybecauseitmayevokeotherstoactthesameway,andpartlybecause
thegoodwillofStayAtisstrengthened.AdditionalCSRworkmaybeexecutedinordertoalignwith
corevalues.Anexampleisengaginginsupportofhomelessandrefugees.
41
7. Recommendations
DevelopStrategy
In section 6.2 under Price, the conclusion of the discussion is not determined in this study. The
decision needs to be evaluated by themanagement of StayAt. The dilemma raised iswhether to
enlightentheresultoftheregressionanalysisanddecreasetheirDailyandExtendedratestoattract
concernedcustomersorifthemarginsaretobeprioritised.Asuggestedapproachistoconsiderthe
low price elasticity for the Long Term segment and increase the concerned rate. This would
compensateforthedecreaseinprofitwhenloweringtheDailyandExtendedrate.
Whenanalysing thePublicRelationsofStayAt’s insection6.3Lobbying, thediscussioncircuits the
influencethecompanyhasinthehotelbranch.ItmaybeadvisableforStayAttoredirecttheirmain
focus from Visita to another actor within their field of business to get their voices heard.Which
actorstoconsiderisnotincludedduetothefeasibilityofthisthesisbuttherecommendationisto
rethinkthestrategyregardingengagementindifferentunions.
Digitalisation
AcrucialopportunityforStayAtisfurtherusingdigitalisation.Oneadviceistomakeadditionaluseof
thedigitalchannelsusedtoday,idestFacebookandthewebsite.Further,itwouldbebeneficialto
establishthecompanyatmoremediassuchasInstagram,LinkedInandsendingE-mailNewsletters
toattractB2Ecustomers.
VisualiseBrand
StayAtmayprofitsignificantlyfromvisualisingtheirwellformulatedbrandtotheB2Efield.Inorder
for thepublic to access their core values and incentive, businessmodel, vision,missionandgoals
shouldbeprintedonthewebsite.Also,ahistoricalbackgroundmaybepresentedheretocapture
the essence of StayAt. This will create a platform where their USP is thoroughly illustrated. To
furtherbuildaprofoundbrand, StayAtmayextend their logotypewith “Established in2010”. The
Facebookpages shouldbe transformed intoone common for StayAtand further alignment in the
physicalfacilitiesisnecessarytoclarifythetrademark.
42
Publications
The main recommendation is to make all essential information available to the end customer.
Everythingthatisaccomplishedandimplementedisadvisedtobepublishedonthedigitalchannels.
The importance lies in the synchronisationandequivalenceof theupdates,on should consider to
havearegulationpolicyregardingwhattopublishtomakeitprofessional.
Tofurther includetheendcustomer intheorganisation,anewslettermaybe implementedwhere
upcoming events, weather reports and happenings in the proximity is mentioned. If the
recommendation is operated, a thought is to include connecting ideas of what to do at certain
moments.Timingsarefurthermentionedbelow.
To trigger end customers to participate in organised events, a recommendation is to formulate
publicitydrivencompetitionsornon-prestigiousevents. Examplesof sucharediscussed in section
6.3.TheideaistoletthecustomersspreadthewordaboutStayAtusingsocialmedia.
CSRinitiatives
Sponsoring and enter partnership with additional actors may be a considerable effort to make.
Whenchoosingthese,thecompanymaywanttoprioritisetheactorsreflectingStayAt’scorevalues.
To further develop, the companymay consider introducing additional CSR engagement related to
their customer base and vision. These could be supporting street children in Asia or help
coordinatingrefugees.
ExploitResources
StayAthasgreatcompetencewithin thecompany,a recommendation is therefore tomakeuseof
this. To let the employees interact andbe included in discussions to raise innovative incitements.
Anotherunexploitedresourceisthecustomersatisfaction;itisrecommendedtotakeadvantageof
thedataprovided.Ensureallopinionsarestudiedandanalysewhethertomakechangestoadjust
fortheresults.Theproximitytomalls,cinemasandsimilarcommonspacesshouldbeenlightened.A
recommendation is tocreatecooperationwith localentrepreneurs togetdiscountedoffers to the
guests.
43
Timing
Topreventthreatsandenhanceopportunitiesitshouldbeconsideredwhentotakecertainactions
intermsofmarketing.Inthiscase,severalofthevariablesfromtheregressionanalysismaybeused.
Enlightening activities during different weathers and putting greater effort onmarketing towards
businesses during low season are two examples of this. Another important aspect is to consider
wheretotaketheseactions,notonlyonwhichplatformsbutalsoifitshouldbepromotedinternally
andexternally.
44
8. Criticism
Criticism is raised towards the model 4P’s, it is stated as old fashioned and conservative. The
marketing professor Robert Lauterborn and his cowriters Don E. Schultz and Stanley I.
Tannenbaum’s introduce 4C’s as a compliment to rather put focus on the client instead of the
product.Thenotion is thoroughlydescribed in thebookTheNewMarketingParadigm: Integrated
Marketing Communications. The 4C’s are Consumer, Cost, Convenience, Communication. The
decision is made to still use the 4P’s due to reliable sources still recommending the method.
(Lauterborn,SchultzandTannenbaum1994).Anawarenessofthecriticismispresentastheanalysis
isperformed,resultinginaflexibleusageofthemodel.Choosing4P’smainlybasesonthedepthof
the model, it provides companies with a complete understanding of their products and how to
marketthem.
Concerning the SWOT analysis, criticism is raised towards its objectiveness. Opinions brought up
meanthattheanalysisismainlybasedonsubjectiveobservations.Onthecontrary,itissaidthatthis
isinsignificantsincetheprocessofthisstudyismoreimportantthanitsresults.Thisisalsoapplied
tothecaseofthisthesissinceitsweightisliedonthe4P’sandtheSWOTactsmainlyasafoundation
forthis.(TheEconomist2009)Inthispaper,SWOTischosenbecauseofthewidthofthemodel, it
includes both internal and external perspectives. Compared to Porter’s Five Forces for instance,
which only examines external factors (Porter 1979), this is presumably the SWOTmodel’s largest
advantage.TofurtherargueforusingSWOTanalysisanddismissingPorter’sFiveForcesspecifically,
the Five Forces are known to bemore applicable within product oriented companies and in not
valuecreatingones.(Schilling2013)
Criticism regarding theSWOTbeing toogeneric is raisedbya varietyofprofessors, theanalyse is
saidtobelongandcostlybutnotcompellingorvaluable(Martin2014).Although,thegenericviewis
interesting in this thesis, and so the model being too wide is rather a positive factor when
investigating the occupancy for StayAt. This assumption is strengthened by a wide range of
practitioners working with strategic analyses. The tool is referred to as a key to obtain a
comprehensiveoverviewoftheorganisationconcepts(Dietrich2015).
Lastly, an objective criticism is raised towards the recommendations in section 7. StayAt may
advisably consider the time investment needed in the strategy implementation. The innovative
approach will bring both structural reformations and changes in priorities. When observing the
StayAtbrandasaproduct, it isrealisedthatthenewmarketingstrategymayinitiallybetedious.If
45
the company obtain early adopters who spread their idea, the customer base will increase
continuously resulting in an improved occupancy. To visualise the process, an adaptor category
model is shown in the figure below. (Rogers 1983) The conclusion of this is that the postulated
recommendationswillnotyieldresultsimmediatelybutareintendedtoleadStayAttowardstheB2E
fieldandanimprovedoccupancy.
46
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Frisell,Niklas;CommercialManageratStayAtHotelApartAB.2016-02-02
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10. Appendix10.1. ListofTablesTable1
CategoryModels
𝒚 Occupancy(%)
𝒙𝟏 Price(kr)
𝑥> Season
𝒙𝟑 Weather
𝒙𝟒 MonthsonMarket
𝒙𝟓 Stockholm
𝒙𝟔 MarketShares(%)
Table2
OriginalModelConfidenceInterval Lower Upper
(Intercept) 0.1443585306 0.5484791750
RelativePrice(%) -0.8305711269 -0.5125303099
Season 0.0107373097 0.0478354306
Weather 0.0002276278 0.0047951166
NearbyEvents -0.0007645415 0.0016445686
Economy -0.0012362421 0.0014202141
MonthsonMarket 0.0003659837 0.0013772924
OccupancyCompetitiveset(%) 0.5664829077 0.9394186169
NPS -0.0007994841 0.0008129173
Stockholm -0.2696594282 -0.1937460162
MarketShares(%) 0.4258513730 0.5608845506
53
Table3
Estimate Std.Error Eta.sq p.value
(Intercept) 0.3647656436 0.0724276724 0.18163 0.0000
RelativePrice -0.6728414058 0.0799056463 0.47737 0.0000
Season 0.0297254576 0.0092595095 0.08334 0.0016
Weather 0.0026269342 0.0011185288 0.04258 0.0200
MonthsonMarket 0.0008641373 0.0001982089 0.11957 0.0000
OccupancyCompetitiveSet 0.7483414056 0.0868951642 0.42404 0.0000
Stockholm -0.2316473813 0.0165331613 0.52383 0.0000
MarketShares 0.4906227701 0.0341160037 0.62892 0.0000
Table4
Covariate VIF(>29) VIF(5-29) VIF(1-4)
Price 1.832196 1.186678 1.223315
Season 1.073856 1.205689 1.243634
Weather 1.034801 1.032982 1.027913
MonthsonMarket 3.169241 2.777040 2.786976
Stockholm 3.596394 3.609388 3.635024
MarketShares 1.664403 1.617652 1.587795
54
Table5Daily lower upper
(Intercept) 4.099766e-01 5.093414e-01
DailyPrice(1-4days) -4.205833e-05 -1.016243e-05
Stockholm -7.766923e-02 -1.817504e-02
Season -1.780016e-03 1.479030e-02
Weather 2.028833e-03 4.072042e-03
MonthsOnMarket -2.441466e-03 -1.864788e-03
MarketShares -4.111206e-02 2.696007e-02
Extended lower upper
(Intercept) 0.1586907349 0.4499132659
ExtendedPrice(5-29days) -0.0005254272 -0.0001750698
Stockholm 0.0222902141 0.0965599748
Season 0.0499658334 0.0757921739
Weather -0.0008448688 0.0022250619
MonthsOnMarket -0.0009996956 -0.0003189111
MarketShares 0.0543091321 0.1835798631
LongTerm lower upper
(Intercept) -0.3370877087 5.098434e-02
LongTermPrice(>29days) -0.0006107655 3.633996e-05
Stockholm -0.2282374100 -1.429407e-01
Season 0.0532722656 7.970042e-02
Weather -0.0033134793 2.096046e-04
MonthsOnMarket 0.0038605108 4.766339e-03
MarketShares 0.0793275188 2.089167e-01