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.

32

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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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

55

10.2 ListofPlots

Plot1

Plot2

Plot3

56

Plot4

TRITA -MAT-K 2016:20

ISRN -KTH/MAT/K--16/20--SE

www.kth.se