Disussion on servie bundling

0 500 1000 1500 2000 2500 3000 3500 4000 4500

0 20 40 60 80 100 120

Total profits

Round

ISP1 ISP2

(a)

0 500 1000 1500 2000 2500 3000 3500

0 20 40 60 80 100 120

Total profits

Round

ISP1 ISP2

(b)

0 20 40 60 80 100 120 140

0 20 40 60 80 100 120

Total profits

Round

ISP1 ISP2

()

Figure 2.8: Total individual prots for 2 ISPs (

L = 0.05, 0.30, 1.0

^{), 1 inumbent, 1 entrant,}

deterministi loyalty

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

0 20 40 60 80 100 120

Total profits

Round Threshold=0.1

Threshold=0.2 Threshold=0.3 Threshold=0.4

Figure2.9: Overall umulative prots of 2ISPs at dierent loyaltylevels

beingloyal to them,sine stronger loyalty results inhigher overallprots. Thisresultis inline

withreent empirialsurveys onuser loyalty [13℄.

harder toswithspeiserviesovertime. Thisusuallyinludesa mandatoryloyaltyperiodin

ontrat, inreased swithing ostsand vanishing disounts (if a single servieis swithed to a

dierent provider).

Being suh an important and omplex topi,there is abroad literatureon bundling. Bakos

and Brynjolfsson [47℄ introdue theonept of the preditive valueof bundling,that itis easier

forasellertopredithowaonsumerwillvalueaolletionofgoodsthanitistovalueanygood

individually. Inthisaseaseller anextratmore revenue fromabundle, andsheismorelikely

to innovate. Moreover, at theoptimal prie, moreonsumers will nd thebundle worthbuying

thanwouldhaveboughtthesamegoodssoldseparately. Authorsonludethatformostphysial

goods, the potential impat of large-sale aggregation is limited, however, these eets an be

deisive for the suess or failure of information goods. In [48℄ it is shown through a Hotelling

model thata pair ofrms an prot fromoering abundled disount to thedetriment ofrms

who do not bundle and onsumers whose preferenes are farther removed from the bundled

brands. Authors of [49℄study the impatof multiprodutnonlinear priingon prot, onsumer

surplus and welfare ina duopoly. Their main result is thatbundling an raiseprot but harm

onsumer surplus ompared to linear priing if there is substantial heterogeneity in onsumer

demand. Gilbrideetal. disusses[50℄theframingeets(howtheprieofthebundleispresented

to ustomers) inmixed prie bundling. Theynd that the integratedframe results inthemost

ustomershoosing the bundle. Considering tripleplay, authors of[51℄ derive thattheoutome

oftripleplayompetitionislikelytodependonthespeedofthedevelopmentofnewtehnologies

and theadaptation of the regulatory environment. In the short run, telephone ompanies will

enjoyanadvantageattributabletoswithingosts. However,thisadvantagewillerodeasyounger

subsribers swith to telephony on the Internet. In [52℄, authors propose a model of dening

themost protable bundles andtheir assoiated priesinthe teleommuniation market. They

onstrut andanalyze atwo-level game usingtheframework of Bayesian gametheory.

As evidened by the broadness of related work and a ertain lak of onsensus among

re-searhers, bundlingisalivelyeldforresearh. That said,a omprehensive investigationonthe

interplay of user loyalty and bundling is a timely, relevant and most ambitious (even foused

only on the ommuniation servies market) topi. While we onsider this investigation as a

very important future work for us, it annot t into the boundaries of this work. Nonetheless,

here we give a glimpse on thepossible extensions for both our game-theoretial and simulation

frameworkthatmaybringus losertothethoroughunderstandingofbundling anduserloyalty.

Regarding our analysis, a simple form of bundling an be inorporated to our framework.

Bundled servies an be pereived as a single super-servie with a prie

p _B

^{. In this ase the}

prie ompetitiongamebetween ISPsmodelstheuserbuying substitutableserviebundles. Let

us onsidera triple play senario, whenonsumers an buythree dierent servies: broadband

eah userisharaterized bya vetor

v

^{,whih onsistsoftherespetiveustomer's valuationof}

individual servies,hene

v = (v _I , v _T , v _P )

^{, where}

∀ j, 0 ≤ v(j) ≤ 1

^{. For analytial} tratability, we make the assumption thatevery userhasthe same valuationvetor.

Furthermore, we believe thata dual-reservation prie senarioarises naturally here, aswell.

We dene the reservation priesfor singleservies (

α ₁ = (α ^I ₁ , α ^T ₁ , α ^P ₁ )

^{for swithers and}

α ₂ = (α ^I ₂ , α ^T ₂ , α ^P ₂ )

^{for loyals). Now, we an determine the}reservation priesfor bundles asfollows:

α ^B ₁ = v × α ₁ α ^B ₂ = v × α ₂

where

α ^B ₁

⁽

α ^B ₂

^)isthereservationprieforswithers (loyals). Furtheron,theanalysisinSetion 2.2.4 also holdsfor this extendedmodel.

Theabove model doesnot apture some important aspets ofthe interplay ofbundling and

loyalty. First,byassumingonstant servievaluations arossusers,we losetheheterogeneityof

userdemandfordierentservies. Seond,temporalevolutionofthemarketannotbeanalyzed

asa one-shotgame. Third,network providers oerboth dierent bundles andsingle serviesat

thesametime. Addingalltheseextrafeaturesmakesthemodelveryomplex,henewepropose

simulation-based evaluation.

Whiledeveloping suha toolremains animportant future work forus, we arguethat itan

be done by extending our existing simulator presented in Setion 2.2.6. Heterogeneous servie

valuations an be introdued among users, and integrated into the user and deision model.

Simulationsarerepetitive,sotemporalharateristisof marketevolution anbeobserved.

Ad-ministrative ostsof swithing serviesisinherently present intheprie-dierenebasedmodel;

swithingostsproportionaltothenumberofswithedserviesanbeeasilyadded. Last,

opera-torsoeringsingleservies,anddierentbundles anbemodeled;inthisaseusers wouldmake

an integrated deisionarossallownedanddesiredservies. To sumitup,thesimulator should

alsomodelthevaluationsandserviesubsriptionsofindividualusers. Withthehelpofsuhan

improved tool, wean analyzehowustomersofheterogeneous loyaltyintentions would onvert

topurhasingserviepakagesindierentsenarios,andadditionally,howtheironversionaet

their loyaltylevel.

Another possible diretion for future work regarding bundling is onstruting multi-level

games (extended games), where users solve an optimization problem at a lower level (deiding

upon their beststrategy for purhasing the servies/bundles they need), then servieproviders

determine theiroered bundles,settheir priesandompeteagainsteahotheratthetoplevel.

A gameof this type an beanalyzed throughthe onept ofsub-game perfet equilibrium[9℄.

Note, that suh extended analyti models are still not suient to apture every aspet of

priing,demandandROIC(ReturnonInvestedCapital)forbundledteleomserviesofamajor

a full,operatingmodel issometimes more artthan siene, greatlyrelying on empirial market

studies and experiene. Nevertheless, our work an provide valuable input to suh a full model

withrespetto thesensitivityof onsumerdemand to priingstrategies.

In document B daeUiveiyfTe h (Pldal 49-52)