In today’s ICT marketplace almost all networks provide a comparably high degree of objectively measurable quality. As a result, quality alone is no longer sufficient to distinguish a service provider from the competition and ensure customer loyalty. Instead, customer experience awareness has emerged as one of the most important business enablers for service providers, by helping them understand the opinions, needs and motivations of users. New enabling technologies for data analytics in the customer experience awareness field can provide richly detailed and actionable insights for business optimization.

1. CUSTOMER EXPERIENCE AWARENESS ✱
SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEW 1
ERICSSON
TECHNOLOGY
t
C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | # 0 8 ∙ 2 0 1 6
INSIGHTSFROM
CUSTOMEREXPERIENCE
AWARENESS

2. 2 SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016 3
CUSTOMER EXPERIENCE AWARENESS ✱✱ CUSTOMER EXPERIENCE AWARENESS
mightnotalwaysfeelsatisfied–evenwhenexperiencing
goodservice.Understandingwhythisisthecaseis
essentialtodevelopingcustomerexperienceawareness
andgainingtheinsightsrequiredtomaketheright
decisionstoactivelymanagetheuser’sperception.
Thereareseveralwaystogoaboutdeveloping
ahigherlevelofcustomerexperienceawareness,
includingtraining,goalsettingandoptimizationofthe
organizationalstructure.Inmanycases,acustomer
experiencemanagement(cem)systemwillalsoplaya
keyrole.
Acem systemisabusinessassurancetoolthat
monitorsandactivelycontrolstheimpactthatthe
user’sperceptionofaproduct,brandorservicehason
thebusinessresultofaserviceprovider.Thecentral
figureincem thinkingistheindividualuser:ahuman
beingwithpersonalopinionsbasedonsubjective
perceptions,whoisembeddedwithinaparticular
socialenvironment.Theuser’sperceptionisbased
ontheirindividualexpectations.Themoods,feelings
andspecificcontextofeveryuserplayamajorrolein
developingtheiropinionsandattitudes,andultimately
determiningtheiractionsandbehaviors.Justasusers
canneverbeobjectiveinformingtheirperceptionof
theirserviceproviders,theyarefrequentlyinconsistent
intheiractions.
A good cems ystem requires a holistic
understanding that goes all the way down to an
individual level, with a broad range of contextual
information about the user taken into consideration.
The resulting insights become actionable if they are
combined with business processes at the individual
user’s level that make it possible to personalize
their experience.
Thepathtoactionableinsights
Acem systemprovidesinsightsthatcanbeusedas
thebasisfordecisionmakingandactiontakingthat
willhelpoptimizebusinessresults.Theseinsights
aretypicallyexpressedinscoresandindicators
thatquantifyaparticularaspectofusersandtheir
experience.Forexample,theNetPromoterScore
(nps)quantifiesinasinglenumbertheuser’sgeneral
willingnesstopromotetheserviceprovider,which
isanindirectexpressionoftheirlevelofsatisfaction
andloyalty.Thenps measuresuserperceptionofthe
overallperformanceoftheserviceprovider,andhas
becomeaveryusefultoolforraisingawarenessof
customerexperiencewithinanorganization.
cem systemsarespecificallydesignedforparticular
businessoptimizationusecases,generatingavarietyof
use-case-specificscoresandindicatorsastheirprimary
output.Innetworkoperations,forexample,any
substandarduserexperienceisdetectedautomatically
withlowlatencyfromperformancemetricsand
broughttotheattentionofsupporttechniciansfor
furtheranalysisandrapidresponse.Thiscontributes
tooverallbusinessoptimization,asproblemsare
solvedquickly,hencelimitingtheireffectsaswellasthe
numberofuserswhoareexposedtothem.
Everybusinessprocessoroptimizationuse
casethatwouldbenefitfromcustomerexperience
JÖRG NIEMÖLLER,
NINA WASHINGTON,
GEORGE
SARMONIKAS
In today’s ict marketplace almost all networks provide a high degree of
objectively measurable quality. As a result, quality alone is no longer sufficient
to distinguish a service provider from the competition and ensure customer
loyalty. Instead, customer experience awareness has emerged as one of
the most important business enablers for service providers, by helping them
understand the opinions, needs and motivations of users. New enabling
technologies for data analytics in the customer experience awareness field
can provide richly detailed and actionable insights for business optimization.
t h e i n t e r n e t o f t h i n g s (IoT) offers
users many great opportunities and simplifies
many facets of life, but for some, its all-
encompassing nature can be overwhelming
and alienating. Service providers that
recognize this risk have the opportunity to
differentiate and create environments where
each individual user feels comfortable and can
rely on their intuition. Doing so successfully,
however, requires a high degree of customer
experience awareness.
■Inthenotsodistantpast,objectiveQoSwasthe
centralconcernforserviceproviders.Theideawas
thatahighdegreeofQoSenabledbyanexcellent
infrastructurewouldleadtoapositivecustomer
experiencewithahighdegreeofsatisfactionand
loyalty.Thiswasreflectedinbusinessmetricssuchas
churnratesandusers’propensitytocallcustomercare.
Whilethatlogicstillholdstruetoacertainextent,
therearemanyfactorsthatitfailstotakeintoaccounton
anindividualuserlevel.Thefactis,individualusersare
nevertrulyobjective,andasubjectiveindividualuser
THE MOODS, FEELINGS
AND SPECIFIC CONTEXT OF
EVERY USER PLAY A MAJOR
ROLE IN DEVELOPING THEIR
OPINIONS AND ATTITUDES,
AND ULTIMATELY
DETERMINING THEIR ACTIONS
AND BEHAVIORS
Terms and abbreviations
bss — business support systems | cem — customer experience management | eli — experience level index |
iot — Internet of Things | mos — Mean opinion score | nps — Net Promoter Score | oss — operations support
systems | s-kpi — service kpi
Customer
ExperienceAWARENESS
GENERATING ACTIONABLE INSIGHTS FROM

3. ✱ CUSTOMER EXPERIENCE AWARENESS CUSTOMER EXPERIENCE AWARENESS ✱
4 SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016 5
ofthemostchallengingtasksduetothecomplexity
oftheuserasanindividual.Understandingtheuser
asanindividualmeansgaininganaccurateideaof
theirlevelofsatisfactionaswellastheirexpectation,
behavior,loyaltyandintention.Thesefactorsare
determinedandinfluencedbypersonalpropertiessuch
assentiment,perception,experienceandneed.The
linksbetweenthesepropertiesarecomplex,andcan
varysignificantlydependingonpersonalcontext,such
asthesocialenvironmentandtimingofexperiences.
Furthermore,experiencesthataretotallyunrelatedto
theserviceprovidercanhaveamajorimpact,because
theyinfluencemanyfactorsfromgeneralpriority
settingtomomentarymoods.
Itispossibletomeasurethesefactorsbyasking
theusertocompleteasurvey.Gettingconsistent
andaccurateanswersrequiressmartwaysofasking
questions,however.Forexample,nps surveysask
usersiftheywouldrecommendtheirserviceprovider.
Thisisaproxyquestionusedtoindirectlymeasure
satisfactionandloyaltybytriggeringmoreaccurate
responsesthanaskingusersdirectlyabouttheirlevel
ofsatisfaction.
Survey-basedmethodsfailcompletelyinusecases
thatrequireagileactiontobetaken,however,because
lowlatencyintermsofinsightavailabilityandoutreach
totheentireuserbaseareprerequisitesfortaking
personalizationactions.Consequently,whileperfectly
suitedtoclarifyingcustomerexperienceperformance
atanorganizationallevel,thenps isofnousein
personalizingthetreatmentofusers.
Theindividual’sindicationoftheirlevelof
satisfactionprovidesavaluableinsightformanyuse
casesinmarketingandserviceoperations.Ithelpsin
theselectionofrecipientsinmarketingcampaigns,
forexample,andcansupportproductplanning.The
individualinsightlevelallowsforthecustomization
ofserviceofferstailoredtosingleusers.Lowlatency
andfrequentupdatesmakeitpossibletoadaptthe
personalizedofferdynamicallyaccordingtochangesin
theindividualuser’sneeds.
Ericssonhasintroducedthesli asasatisfaction
scorethatmeetsthetechnicalpropertiesofusecases
thatrequireagileaction[1].Reachedusingapredictive
model,itisapersonalscoreavailableforeveryuser.It
isupdatedfrequently,andwithlowlatencyafterauser
activity.Experience-relateds-kpisthatoriginatefrom
thenetworkprobeinfrastructureareusedasinput.
Psychologicalfactorsrelatingtosubjectiveperception
formthebasisforinterpretingtheobjectively
measuredexperienceexpressedbythekips,and
indicateasubjectivelevelofsatisfaction.Thefollowing
psychologicaleffectshavebeenidentified:
〉〉Perceptionisindividual,sodifferentmodelsareneeded
fordifferentusers.
〉〉Anegativeexperiencehasmoreimpactthanapositive
oneontheoveralllevelofusersatisfaction.
〉〉Surprisingexperiencesaremoresignificantthanless
surprisingones.
〉〉Theuserforgetsexperiencesastimepasses.
〉〉Themoresignificanttheexperience,thelongeritwill
berelevant.
〉〉Thecontextofeachexperienceeventmayaffectthe
user’sperception.
Thesli modelisapsychology-basedhypothesis
thatrepresentsthesefactorsmathematically.The
modelistrainedusingsurvey-basedreferencedata.
Inthisway,itiscalibratedtohowserviceandnetwork
experiencesareperceivedbyaparticularuserbase.
Thecombinationofpsychologicalresearchwith
state-of-the-artmachinelearningalgorithmsisthe
centralinnovationthatmakesitpossibletomaster
thecomplexityofindividualuserperception.The
insightsprovidedbythesli aredesignedfordirect
consumptionindecision-makingandaction-taking
Figure 1
Properties of
scores
UNDERSTANDING THE
USER AS AN INDIVIDUAL MEANS
GAINING AN ACCURATE IDEA
OF THEIR LEVEL OF
SATISFACTION AS WELL AS
THEIR EXPECTATION,
BEHAVIOR, LOYALTY AND
INTENTION
awarenesswillhaveitsownparticularrequirements
withrespecttoscores.Asidefromthemainsubject
thataparticularscoreexpresses,thefollowing
characteristicsarerelevant:
〉〉 Scope:Doesthescorereflectaninsightattheindividual
userlevel,foragroupofusers,orforanentireorganization?
〉〉 Outreach:Howmanyusersareincluded?
〉〉 Subjectivity:Doestheinsightreflectanobjectivefactora
subjectiveperception?
〉〉 Predictive:Istheinsightdirectlymeasuredortheresultof
apredictivemodel?
〉〉 Latency:Howquicklydoesthescoreneedtoreflect
anexperience?
〉〉 Frequency:Howoftenisanupdateofthescoreneeded?
Theseuse-case-specificrequirementsaredirectly
reflectedinthewayascoreisobtainedand
implemented.Aservicekpi (s-kpi)andsimilarlow-
latency,high-outreachmeasurementsareneededfor
swiftcorrectiveactiontobetaken.Mostofthemare
objectivemetricsmeasuringtechnicalperformance,
andtheymakeitpossibletodistinguishindividual
users.Thecharacteristicsofthekip arereachedwith
considerableeffortintermsoftheefficienthandling
ofareal-timeinputdatastream.Therawdatacomes
fromanextensivedistributedprobenetwork,andis
correlatedandprocessedinnearrealtime.
Surveysandstudiesthatapproachtheuser
directlyandaskforfeedbackhaveacompletely
differenttechnicalprofilefromthes-kpi – that
is,thecharacteristicsofthesescoresarequite
different.Thenps isaprominentexample,asitisa
directmeasurementthattypicallyhashighlatency,
withsignificanttimeintervalsbetweendistinct
measurements.Furthermore,theoutreachisnot
veryhigh,withonlyafewpercentoftheuserbase
includedineverymeasurementactivity.InFigure1,
thecharacteristicsoffourdifferenttypesofscoresare
compared–s-kpi,nps,servicelevelindex(sli)and
meanopinionscore(mos).
Measuringsubjectiveuserperception
Theabilitytounderstandtheuseronanindividual
andpersonallevelhasbecomeoneofthemost
promisingareaswithincem.Nevertheless,itisone
Global per
organization
S-KPI
MOS
NPS
Individual
per customer
Objective
performance
Subjective
perception
Updated in
real time
NPS
S-KPI MOS
SLI
Occasionally
updated
Measured
Predictive
SLI

4. ✱ CUSTOMER EXPERIENCE AWARENESS CUSTOMER EXPERIENCE AWARENESS ✱
6 SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016 7
processes,contributingtoincreasedpersonalizationof
theuserexperience.
Inshort,thesli andthenps bothprovideinsights
intosubjectivesatisfaction,buttheytargetdifferentsets
ofusecasesandthereforetakedifferentapproaches
toreachthetechnicalusecaserequirements.Thesli
introducestheabilitytoactonanindividualuserlevel
andultimatelyimprovethenps,whichisanimportant
indicatorofbusinesssuccess.
Understandingtheexperiencejourney
Inalloftheirinteractionswithaserviceprovider,
usersundergoanexperiencejourney:asequenceof
experienceeventstheyareinvolvedinovertime.These
eventscanbedirectinteractionssuchasvisitingapoint
ofsales,callingcustomercareorsimplyusingaservice.
Buteveneventsthatdonotinvolvetheserviceprovider
directlyareconsideredpartoftheexperiencejourney.
Forexample,theusermightsharetheiropinionofa
servicewithotherusersonsocialmediaplatforms.
Theexperiencejourneyisapowerfulconceptual
toolforcontinuousmonitoringandcategorizationof
experiences.Aprominentandwidelyusedexample
istm Forum’sExperienceLifecycleModel[2],
whichdefines22phasestocategorizetheindividual
experienceeventsinthejourney.Thismodel,
illustratedin Figure2,wasrecentlyupdatedunder
Ericsson’sleadershiptocoverdigitalserviceproviders
acrossdomainsanduserexperienceintheInternetof
SmartEverything.
Therearealwaystwodistinctrolesinthecontext
ofamodelforexperiencejourneys:theobserveduser,
andtheserviceproviderthatisusingthemodelfor
structuredrecordingofobservationswithrichdetails
abouttheuser.
Itisnotablethattheuser’sexperiencewhile
consumingtheserviceisjustoneof22phasesinthe
journey.Itbeginsbeforetheuserbecomesacustomer,
withtherealizationthatthereisaneedforaservice
orthatcurrentservicesarenolongeragoodfit.The
firstfewphasesofthejourneydetermineinterestand
loyalty,andagoodunderstandingofthesecanbe
highlyrelevant,allowingaserviceprovidertoapproach
usersattherighttimewiththerightmessage.The
ExperienceLifecycleModelclearlyillustratesthe
significantparadigmshiftfromnetwork-operation-
centriccem totakingaholisticviewoftheuser.
Eachphaseinthejourneyandeveryexperience
eventisconnectedtoarichsetofcontextualdetails
andmetrics.Inserviceusage,thes-kpisarerelated
totheusageevent.Forcustomercare,respectivekpis
determinethedetailsofaninteractionbetweentheuser
andtheserviceprovider.Alloftheseexperience-event
kpisproviderichinputtoanalytics.Forexample,the
sli modelutilizesservice-usageeventstodetermine
usersatisfaction.
TheExperienceLifecycleModelreveals,however,
thatthesli ismissingexperiencesfromotherphasesof
thejourney,whichalsocontributetousersatisfaction.
Thepsychologicalscoringmodelofthesli isdesigned
toutilizefurtherkpisaslongastheycorrelatewith
usersatisfaction.Thismeansthescoringcaneasily
beextendedtofurtherpartsofthejourneyforhigher
predictionaccuracy.Ericssoncallstheresultingscore
theexperiencelevelindex(eli)[1].
Withrespecttotheuserjourney,interaction
betweentheuserandtheserviceprovidercanoccurin
variousphasesandthroughmanydifferentchannels.
Thediversityofthesetouchpointsmeansausercan
startaninteractionononechannelandthencontinue
lateronadifferentone.Forexample,theusermightfirst
callcustomercaretogetinformationaboutaservice
offer,andlaterresumethisdialogueatapointofsales.
Theoverallexperienceisnotonlydeterminedby
eachindividualinteraction,butbytheentirejourney.
Inthisrespect,continuityandconsistencyacross
theindividualtouchpointsarehighlyimportantfor
ensuringagooduserexperience.
Theconceptofaseamlessandconsistentjourney
experienceisreferredtoas“omni-channel”bytm
Forum[3].Thetechnicalsolutionforreachingit
consistsofaconsolidatedoperationssupportsystems/
businesssupportsystems(oss/bss)backendwith
consistentdataanddetailedhandlingofeachuser’s
communicationhistory.Thisisanotherexamplein
whichdeliveringagooduserexperiencerequiresahigh
degreeofpersonalization.
FromIoTtoInternetofSmartEverything
ThecombinationoftheIoTandtheexponentially
increasingsmartnessofallmannerofthingsisdriving
usintoanewphaseofdigitalizationknownasthe
InternetofSmartEverything.Inthisemergingreality,
theuserispartofaninfrastructurealongwithalarge
numberofphysicalthings,servicesandsocialmedia
platforms.Thingscanbeunderstoodasautonomous
entitieswithbuilt-inintelligencethatrepresentphysical
entitiesinthedigitalcontext.Atthesametime,new
smartservicesarelaunchedeveryday,andsocialmedia
platformsandotherinteractionchannelscontinueto
grow.Familiarofflineservices–publictransportation
andutilitiessuchaselectricityandwater,forexample
–arealsoincreasinglypresentedandmanaged
throughdigitalchannels.Termslike“smartcity,”
Figure 2
Experience
Lifecycle Model
Be aware
Realizeneed
PhasesJourneyphasesJourneydetails
Learn
React
Request
Select
Order/renew
Receive
Use
Reviewuse
Manageprofile
Receivehelp
Receiveresolution
Receiveandverify
Pay
Compensate
Realizechange
Reviewvalue
Reviewoffer
Refer
Feedback
Beloyal
Leave
Interact Choose Consume Manage Settle Review Share Decide
Insights and knowledge
Engaging Using
Experience events
Channels
Evaluating
Subjective perception scoring
〉〉 TheNPSisabusinessperformancebenchmarkfortheserviceproviderthatmeasurescustomerexperience.Itisbased
onsurveysinwhichanumberofusersareaskediftheywouldrecommendtheserviceprovidertofriends,colleagues
andfamily.
〉〉 TheSLIpredictsauser'scurrentlevelofsatisfactionbyinterpretingobservationsabouttheirserviceusageandthe
deliveredservicequality.Ananalyticsmodelevaluatestheobservationsanddeliversascore.TheSLIisfrequently
updatedandavailableforeveryuser.
〉〉 TheELIexpandstheSLImodeltoincludeadditionalphasesoftheuserjourney.

5. ✱ CUSTOMER EXPERIENCE AWARENESS CUSTOMER EXPERIENCE AWARENESS ✱
8 SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016 9
servicequalitydegradationinordertocontainthe
effectsoftheissue.Fortheanalyticsbackend,this
impliestheneedforascalablestreamprocessing
infrastructurethatisabletoprocesshighvolumesof
data.Scalablerulesenginescanbeappliedtofilterout
interestinginsightsfromthestream.In-memorydata
handlingisanotheressentialcomponentinthiscontext.
Inotherusecases,theinsightswillbebasedona
similarlyhugeamountofavailabledata,butlowlatency
isnotrequired.Batchprocessingcomponentsbased
onMapReducetechniques,forexample,constitute
anessentialenablerforthistypeofusecase.Accessto
comprehensivehistoricaldataisoftenimportantfor
generatingtheseinsights.
Prescriptiveanalyticswillsupporttheservice
provider’stechnicalandbusinessexpertsinmaking
theiroperationaldecisions.Thedomainexperts
willbeabletoautomatizetheirdecisionsandaction
processesformoreagilereactionandimplementation
ofchange.Theseexpertsystemsarebasedon
techniquesforknowledgemanagementandartificial
intelligence.Rulesenginesareabletogenerate
straightforwardrecommendationsbasedonprevious
analyticsinsights.Ontologiesandsemanticmodels
givemeaningandcontexttodataandanalyticsresults.
Theymakeitpossible,forexample,toinformthe
recommendationsystemaboutbusiness-levelgoals
andstrategies.Pairingthiswithmachinereasoning
“smartcars”and“smartmeters”refertothisongoing
transformation.
The Internet of Smart Everything constitutes a
vital new dimension in cem. The number of
digital interactions with the user is set to increase
dramatically, resulting in a new level of complexity
in the user journey.
IntheIoTenvironment,usersexperiencea
presentationlayerthroughwhichmoreorlesssmart
thingsandrelatedservicesinteractwiththem.The
thingspresentinformation,requestdecisionsand
learnfromtheinteraction.Agooduserexperienceis
aneffortlessandintuitiveonewithoutunnecessary
interactions.Usersshouldalwaysbeawareofwhat
theycando,howtheycanreachtheirgoalsefficiently,
andwhatconsequencesanactionwillhave.Allof
thisshouldbeenabledwiththerightlevelofrelevant
informationavailableattherighttime.
Itisimportanttorecognizethatusersaredifferent
intheirabilitiestocopewithandacceptthisnew
environment.Thedigitalnativesofthe21stcentury
willinteractmoreintuitivelythanmanyothers.
Personalizationmakesitpossibletocustomizethe
entireexperienceforeachindividual.
cem suppliersfollowIoTdevelopmentsclosely,
andwilllaunchnewcapabilitiestomanagenew
typesofinteractionexperiences.Newandextended
scoreswillhelptocaptureexperiencesand
facilitatepersonalization.Theanalyticsbackend
willincorporatenewkpisthatareamoreaccurate
reflectionofuserexperience.Andnewandimproved
algorithmswillprocessallavailabledataintoever
betterrecommendationsforactiontaking.
Whiletheexperienceofuserswheninteractingand
usingsmartthingsisobviouslyparamount,itisalso
truethatsmartthingsthemselvesactlikeuserswhen
interactingwitheachotherorwithhumanusers.The
smartthingdecidesautonomouslytouseservicesor
tocommunicate.Ithasrequirementsthatneedtobe
satisfiedbytheservicesandinfrastructurethatitutilizes.
Asaresult,thesametoolsusedtodetermineandactively
managetheexperienceofapersoncanbeappliedtothe
experienceofasmartthing.
Theanalyticsinfrastructure
Inordertoprovideahighlevelofcustomerexperience
awarenessinavastandcomplexdigitalworld,acem
systemneedstosupportfourtypesofanalyticsina
flexibleanalyticsbackend:
〉〉 Descriptiveanalyticsdeterminesinformationaboutthe
currentsituationandpresentsitinawaythatmakesit
possibletocapturetheessentialinsighteasilyanddecide
ontheappropriateaction.
〉〉 Diagnosticanalyticsgoesonestepfurther,andfinds
causalitiesinthedata.
〉〉 Predictiveanalyticslearnsfromcurrentandhistorical
referencestodetecttrendsandanticipatesituations
beforetheyoccur,enablingearlycountermeasures
tobetakentomitigateproblemsbeforetheybecome
significant.
〉〉 Prescriptiveanalyticsdirectlyproposesthebestactions
tobetaken.
Alloftheseapproachesdependonaccesstorawdata
comingfromvarioussources.Thecem systemmust
beabletosupportmanydifferentsystemswithagreat
varietyofinterfacesandprotocolsfordataaccess,
togetherwithrespectiveinformationmodels.Typical
sourcesofdataarecustomerrelationshipmanagement
systems,billingandotherbss,networkprobes,IoT
serviceenablementandanytypeofexistingdata
warehouse.Theresultsofusersurveyscanalsobe
includedintheanalysis.
Inearlyprocessing,therawdataisfiltered,
aggregatedandcorrelatedinordertomaximize
relevance.Basicstatisticalmethodsaredeployedand
s-kpisarecalculatedinthisstep,creatingthebasis
formoreelaborateanalytics.Thisphaseincludes
aredundancyandirrelevancereduction,whichis
particularlyimportantforinputdatastreams,where
thesheervolumeofincomingdatathatalsoneeds
processingwithlowlatencyisachallengeinitself.
Thisinfrastructureisidealforusecaseswherethe
systemisexpectedtodeliverthebasisforagileactions
andcountermeasures.Atypicalexampleisnetwork
operation,whichneedstoreactrapidlyondetected
Figure 3 Logistics in a warehouse
Offline learning and
model training
Online learning and
model training
Data sources
Internet of Smart
Everything
Data warehouses
User journey
Operation
Presentation
Decision processes
Network
OSS/BSS systems
Analytics Scores and insights Decision and action
Data
ingestion
Batch
processing
Stream pre-
processing
Stream
processing
Recommendation
generation
Data Insights
CEM analytics core

6. ✱ CUSTOMER EXPERIENCE AWARENESS CUSTOMER EXPERIENCE AWARENESS ✱
10 SEPTEMBER 30, 2016 ✱ ERICSSON TECHNOLOGY REVIEWERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016 11
techniquesleadstorecommendationsthatare
dynamicallyalignedwithbusiness-levelconcerns.
Thefourtypesofanalyticsactasenabling
toolsetsthatallowadatascientisttosetupanalytics
algorithmswiththerighttechnicalproperties
foragivenusecase.Ericsson’sstate-of-the-art
architectureishighlyscalableforthispurposein
termsofprocessinganddata-handlingcapabilities.
Itsparticularstrengthisinlow-latencyprocessing
ofnetwork-basedstreamsofmetrics,theintegration
ofdiversedatasourcesandinformationmodels,and
theincorporationofresultsintosubsequentdecision-
makingandaction-takingprocessesthroughout
diverseoss/bss tools.
Theanalyticsinfrastructureofourcem solutions
hasbecomeacommonbaseforEricsson’sentiredigital
businesssystemsportfolio.Thisisanimportantstep
toreachaconsolidatedofferingwhereaconsistent
experienceforusersacrossseveraltouchpointsalong
theirjourneysisrelativelyeasytoachieve.
Theend-to-endanalyticspathisoutlinedin
Figure3,whichdistinguishesbetweenthestream
processingtrack–wherelowlatencyandreal-time
processingareenabled–andthebatchprocessing
track–whereanalyticsonmorestaticdatais
performed.Rawdataentersthesystemontheleftat
preprocessing,whichinterfaceswithagreatvariety
ofsources.Theincomingdataisimmediatelyusedin
streamprocessingforlowlatencyinsightsandstored
inadistributedin-memorydatabasetomakeiteasily
availableforfurtheranalyticsprocessing.
Traininganalyticsmodelsviamachinelearning,and
usingtheminactualscoringandinsightgeneration,
aretwodistinctfunctionsintheanalyticsworkflow.
Learningandmodeladaptationcyclescan,however,
behighlydynamicprocesseswithcontinuous
adaptationofthewaythatinsightsaregenerated.
Theinsightsgeneratedinanalyticsarekept
availableinadatabaseforsubsequentinternalor
externalusage.Recommendationsystemsusually
operateontheanalyticsinsights.Decisionmakingand
actiontakingaretypicallydistributedthroughoutthe
variousbusinessandoperationallevelmanagement
andplanningsystems.Examplesofbusinesslevel
systemsandprocessesthatprofitfromanalytics
insightsarecampaignandrevenuemanagement,as
wellasinvestmentplanning.
An essential component of future cem solutions
will be recommendation systems that are use-case
aware and able to directly propose an action that
would optimize a user experience. These systems
would, for example, recommend when to start a
marketing campaign for upsell and propose which
users to include, or recommend investments into
infrastructure upgrades to optimize the
user experience.
Theseexamplesshowtheevercloserintegration
ofcem intobusinesslevelprocessesandactivities.
Futurerecommendationsystemswillbeawareof
theserviceprovider’sbusinessgoalsandpreferred
strategiestosupportstaffindecisionmaking.The
resultingchangeinscoreswillthenbeusedtotrack
progressandverifythesuccessoftheactionstaken.
Thecustomer-centricorganization
Manyserviceprovidersfindtheyneedtoadapttheir
businessprocessestomakeuseofthenewinsights
theyreceivefromcem.Theseprocessadaptations
canbeexpectedtoproducesignificantimprovements
inbusinessresults.Theyare,however,onlythefirst
stepintheprocessofbecomingatrulycustomer-
centricorganization.
Acustomer-centricorganizationischaracterizedby
fourexternallyvisiblekeyabilities:
1 Beingattentive.
Norequestsareleftunanswered.Thegoalistobe
perceivedasflexibleandavailable.Theuserfeelsevery
effortisbeingmadetoaccommodatetheirpersonal
needswithacustomizedsolution.
2 Beingproactive.
Theserviceprovideranticipatescustomerconcernsand
addressesthemearlybeforetheybecomeanissue.In
thisrespect,itisimportanttochoosetherighttimeand
channelforinteraction.
3 Beingconsistent.
Foragoodexperience,thedialoguewithcustomers
acrosschannelsneedstobeseamless.Theuserexpects
theserviceprovidertohavearecordofallprevious
contactsothattheydon’thavetorepeatthemselves
orreceiveconflictinginformationfromdifferent
touchpoints.Informationneedstobeconsistent,
flawlessandimmediate.
4 Beingadaptive.
Servicesandproductsareadaptedcontinuouslytomeet
customerneeds.Thecustomerfeelstheirneedsare
fulfilledandexperiencesanychangesasimprovements.
Theseabilitiescrosscutaserviceprovider’sentire
organization,demandingahighdegreeofengagement
fromeveryemployee.Thiscanbeachievedthrough
goalsettingandtraining,butitalsorequirestheright
organizationalstructureandinternalinterfaces.
cem helpsidentifybothshortcomingswithinan
organizationandanynecessarycorrectiveactions.
Furthermore,ceminsightsenableeachunitwithinthe
serviceprovider’sorganizationtomastertheirtasks
withcontinuousawarenessofthecustomerexperience
impact.Customer-experience-relatedbenchmarksare
usedasmajorsuccesscriteriaforaunit.Theycanalso
facilitateinvestmentdecisionsandindicatethereturn
ofinvestmentofactionsandchanges.
Conclusion
Customerexperiencemanagementisthepractice
ofcontinuouslymanagingandimproving
anorganization’scustomertouchpointsand
interactions.Inanincreasinglyvastandcomplex
digitalworld,thereisaclearneedforservice
providerstounderstandthecustomerexperience
–notonlyfromtheperspectiveofnetworkand
serviceperformance,butalso,torecognize
eachuserasasubjectiveindividual.Customer
experienceawarenesshasthepotentialtoactasa
keydifferentiatorintheICTindustry,helpingservice
providerstransformintothekindofcustomer-
centricorganizationsthatcanofferahighlevelof
personalization.Bycombiningtechnology,strategies
andresources,anyserviceproviderhasthechanceto
usecustomerexperienceawarenessthinking–and
cem systemsinparticular–tosignificantlyimprove
customersatisfactionandloyalty.
References:
1. Jörg Niemöller and Nina Washington, Subjective Perception Scoring, at the 19th International
Conference on Innovations in Clouds, Internet and Networks (ICIN 2016), March 2016, Paris, France
2. TM Forum Best Practices, GB-995, 360 Degree view of the Customer R16.0.0, Frameworx Release 16
3. TM Forum Best Practices, GB-994, Omni-Channel Guidebook R16.0.0, Frameworx Release 16

7. ✱ CUSTOMER EXPERIENCE AWARENESS
12 ERICSSON TECHNOLOGY REVIEW ✱ SEPTEMBER 30, 2016
Jörg Niemöller
◆ is a systems manager
for customer experience
management and data
analytics within Digital
Business Systems. He has
established subjective
perception scoring within
Ericsson’s products for cem
and analytics. Niemöller
holds a Ph.D. in computer
science from Tilburg
University, the Netherlands,
and a diploma in electrical
engineering from the
Technical University of
Dortmund, Germany. He
has worked for Ericsson
since 1998 in various
system management
positions and at Ericsson
Research.
Nina Washington
◆ joined Ericsson
Research in 2012. She is
an experienced services
researcher focusing
on psychological
perspectives, studying
user behavior, attitudes
and values. In her current
research she explores goal
setting, communication
and cross-cultural
differences, all in interplay
with technology as an
enabler. She holds an
M.Sc. in psychology from
Lund University, Sweden,
as well as an M.Sc. in
positive psychology from
the University of East
London, uk.
GeorgeSarmonikas
◆ joined Ericsson in
2013 after several years
of working for mobile
operators. He currently
leads the IoT Analytics
and CEM Innovation group
within Digital Business
Systems. Prior to this he
was responsible for product
management of Ericsson’s
CEM and analytics
portfolio, including assets
for subjective experience
scoring. Sarmonikas
holds both an M.Sc. in
communication systems
and an M.Eng. in electronic
engineering and computer
science from Bristol
University, uk, as well as a
diploma in innovation with
artificial intelligence from
Stanford University, us.
theauthors
ISSN 0014-0171
284 23-3291 | Uen
© Ericsson AB 2016
Ericsson
SE-164 83 Stockholm, Sweden
Phone: +46 10 719 0000