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In partnership with:
A new Customer Experience MeasurementModel A Meta Analytical Review of Findingsover the period 2002 to 2009
Presented by Prof Adr Schreuder
MD of Consulta Research & Extra-ordinary Professor
of Marketing Research University of Pretoria,South Africa
19th Annual Frontiers in Service Conference 201010-13 June 2010 - Karlstad, Sweden
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis Research Results & Discussion
Dangers of Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
Slide 2
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Background & Rationale for Research
Slide 3
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Terminology Confusion
Slide 4
Source: Created by Adr Schreuder reference:
< http://www.wordle.net/show/wrdl/1954142/Customer_experience >
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CUSTOMER SATISFACTION
A Historic Overview
Slide 5
TQM of EdwardsDeming - ZeroDefect, Six Sigma
Relationship
Quality Era
(1995)
CRM
Customer
Experience Era
(2003)
CEM
Service Quality
Era (1984)
SERVQUAL
Product Quality
Era (1950s)
TQM
The Nordic approach (Grnroos 1984: Technical/FunctionalModel, Lethinen & Lethinen 1988 : Technical, Corporate,Interactive)
The North American Debate (PZB 1985: SERVQUAL (Gap-basedmeasure, Familiar five quality dimensions, Cronin & Taylor 1992:SERVPERF - Performance only measure, Brown Churchill & Peter
1993: Better/worse than expected scale, Teas 1993: EvaluatedPerformance Model = gap between perceived performance &ideal amount of feature)
Jagdish Sheth introduced Relationship Management in mid 90s
Growth of CRM-systems and popularity
NPS introduced by Reichheld in 2003 CEM era is born
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Customer satisfaction:
Contrasting academic and consumers interpretations
Satisfaction defined Derived from Latin satis = enough & facere (faction) = to do/to
make
Early interpretation and use of the word mostly focused on some sort
of release from wrong doing - later release from uncertainty
At least two basic approached in defining the concept: CS viewed as an outcome of a consumption activity
CS viewed as a process
Most widely adopted description = evaluation between what wasreceived and what was expected
Slide 6
Source: Parker, C & Mathews, B.P. Marketing Intelligence & Planning, 19/1 2001 (pp 38-44)
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Customer satisfaction:
CS viewed as an outcome - Focus on the nature (not cause) of
satisfaction: Emotion - satisfaction is the surprise element of product
acquisition and/or consumption experiences, or an affectiveresponse to a specific consumption experience
Fulfilment - motivation theories state that either people are
driven by the desire to satisfy their needs or achieving specificgoals.
State - Olivers (1989) framework of four satisfaction states,where satisfaction is related to reinforcement and arousal. Low arousal = satisfaction-as-contentment
High arousal = satisfaction as surprise (positive / delight ornegative / shock)
Positive reinforcement = satisfaction-as-pleasure
Negative reinforcement = satisfaction-as-relief
Slide 7
Source: Parker, C & Mathews, B.P. Marketing Intelligence & Planning, 19/1 2001 (pp 38-44)
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Customer satisfaction:
CS viewed as a process Concentrate on the antecedents to satisfaction rather than
satisfaction itself. (Origins in discrepancy theory - (Porter, 1961)and Contrast Theory (Cardozo, 1965);
Most common interpretation = a feeling which results from aprocess of evaluating what was received against that expected, thepurchase decision itself and/or the fulfillment of needs/wants.
Most well-known descendent of the discrepancy theories is the
expectation disconfirmation paradigm (Oliver, 1977, 1981).
Slide 8
Source: Parker, C & Mathews, B.P. Marketing Intelligence & Planning, 19/1 2001 (pp 38-44)
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Customer Experience the new CustomerSatisfaction?
Yet despite the recognition of the importance of customerexperience by practitioners, the academic marketing literature
investigating this topic has been limited.
Publications on customer experience are mainly found in
practitioner-oriented journals or management books tend to
focus more on managerial actions and outcomes The literature in marketing, retailing and service management
historically has NOT considered customer experience as aseparate construct. Instead researchers have focused onmeasuring customer satisfaction and service quality.
Source: Verhoef, Peter C., Katherine N. Lemon, A. Parasuraman, Anne Roggeveen, Michael Tsiros and Leonard A.Schlesinger (2009), Customer Experience Creation: Determinants, Dynamics and Management Strategies, Journal of
Retailing, 85 (1), 3141.
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Customer Experience the new CustomerSatisfaction?
One reason for the apparently weak observed link betweensatisfaction and future behaviour may lie in the role ofemotions
Previously studies emphasised cognitive aspects of satisfaction growing body of evidence that affective measures of satisfaction(which incorporate emotions) may be a better predictor of
behaviour As a cognitive measure, satisfaction is more likely to be distorted
over time than a measure that incorporates an affectivecomponent (emotions are more deep-seated & more stable overtime)
Satisfaction should thus include a combination of an evaluative(cognitive) and emotion-based (affective) response to a serviceencounter
Source: Koenig-Lewis, N. and Palmer, A. "Experiential values over time a comparison of measures of satisfactionand emotion," Journal of Marketing Management (24:1-2), 2008, pp. 69-85.
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Construct definition of Customer Experience
The customer experience construct is holistic in nature andinvolves the customerscognitive, affective, emotional, social and
physical responses to the retailer.
This experience is created by: controllable elements - service interface, retail atmosphere,
assortment, price,
uncontrollable elements - influence of others, purpose of shopping
Customer experience encompasses the total experience, includingthe search, purchase, consumption, and after-sale phases of theexperience, and may involve multiple retail channels.
Three major focus areas: cognitive evaluations (i.e., functional values)
affective (emotional) responses
social and physical components
Slide 11
Source: Verhoef, Peter C., Katherine N. Lemon, A. Parasuraman, Anne Roggeveen, Michael Tsiros and Leonard A.Schlesinger (2009), Customer Experience Creation: Determinants, Dynamics and Management Strategies,Journal of
Retailing, 85 (1), 3141.
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Putting Customer Experience into Perspective
The term Customer Experience Management
is used within the broader context ofCustomer Relationship Management (CRM) clearly seen in the view of Kirkby, Wecksell& Janowski (2003) when they say: CEM is
part of customer relationship management
(CRM) and the natural extension of buildingbrand awareness.
Where brand gives the promise, CEM is the
physical delivery of that promise and is vital
in an economy where a brand is increasinglybuilt on value delivered rather than product
features.
Slide 12
Illustration Copyright Consulta 2010
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Putting Customer Experience in Perspective
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis Research Results & Discussion Dangers of Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
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Previous Research & Literature Review
Collection of previous research and literature regardingCustomer Experience measurement are presented anddiscussed under the following topics: Multi-attribute measures such as:
SERVQUAL,
ASCI &
Others Effort Score & ERIC
Net Measures such as: The Net Promoter Score from Fred Reichheld & Bain Company
Secure Customer Index from Burke
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Customer satisfaction and companyprofitability: The Service-Profit Chain
Slide 16
External
ServiceValue
Profitability
Internal
ServiceQuality
Employee
Satisfaction
EmployeeRetention
EmployeeProductivity
Customer
Satisfaction
RevenueGrowth
Customer
Loyalty
3Rs (>Market Share) Retention,
Repeat Business Referrals
Service designed & delivered to meet targeted customers
needs
Service Concept:Results for Customer
Workplace Design
Job Design
Employee Selection & Development (skills& empowerment drives good feelings
towards the firm)
Employee Rewards & Recognition
Tools for Serving Customers
Operating Strategy &
Service Delivery System
Adapted from: Heskett, Jones, Loveman, Sasser & Schlesinger (HBR 1994, HBR July/Aug 2008, p.120)
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The GAP never mentioned
Slide 17
CEM
TheMissingGap
Expectations
Perceptions
Delivery
Interface
Managementunderstanding of
expectations
Marketing &
Communication
ExperienceStandards
Gap 1
Gap 2
Gap 3
Gap 4
Gap 5
CEM = delivering what ourcustomers expect us to and
a little bit more ,
making them feel great atevery moment of truth,
Adapted from original Gaps-
Model of Parasuraman,
Zeithaml & BerryIllustration Copyright Consulta 2010
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CEMS
tra
tegy
Conceptual Model of CustomerExperience Creation
Slide 18
Source: Verhoef, Peter C., Katherine N. Lemon, A. Parasuraman, Anne Roggeveen, Michael Tsiros and Leonard A. Schlesinger (2009),Customer Experience Creation: Determinants, Dynamics and Management Strategies, Journal of Retailing, 85 (1), 3141.
Social Environment:Reference group, tribes, co-destruction, service staff
Service Interface:Service person, technology, co-creation/customisation
Retail Atmosphere:Design, scents, temperature, ambient noise, music
Assortment:
Variety, uniqueness, quality
Price:Loyalty programs, promotions, rewards
Customer experiences in alternativechannels
Retail Brand
CUSTOMER EXPERIENCE (t 1)
Situational
Moderators:Type of store, location,
culture, economic climate,season, competition
ConsumerModerators:
Goals: experientialTask orientation, socio-
demographics, consumerattitudes (price sensitivity,
involvement)
Customer Experience(t):
Cognitive, affective, social,physical
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Effort Score worth the effort?
Slide 19
Pred
ictive
Pow
er*of
Repurchase
High
Low
Low HighPredictive Power* for Increased
Spend
Power* - Linear regression coefficients regressed against Likelihood
to Repurchase & Increase Spend
Research conducted by Customer Contact Council of theCorporate Executive Board
NPSCouncil ConclusionInadequate measure in theservice channel:
Question inherently positive(only likelihood to recommend not criticize)
Captures company-levelsentiment (incl brand, product,pricing)
EffortCouncil Conclusion
Better suited for servicechannel. Better financialpredictor & best indicator ofloyalty
CSAT Council ConclusionPopular, widely used BUT notsufficient in predicting
financial outcomes de-emphasize its use in strategicdecisions
Comments: Directly contrasting scientific
proof ofACSI (American), SCSI(Sweden)
No scientific foundation Irresponsible to recommend
members against Effort-score purely developed in
Contact centre environment No published proof of scientific
reliability & validity
Scale is reverse scored SouthAfrican research shows low
reliability & poor predictiveproperties to the contrary
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ERIC Empathy Rating Index
Slide 20
Source: Lywood, J., Stone, M. and Ekinci, Y. "Customer experience and profitability: An application of the
empathy rating index (ERIC) in UK call centres," Journal of Database Marketing & Customer Strategy
Management (16), 2009, pp. 207-214. & Lywood, J., Stone, M. and Hackett, D. Eric Methodology Whitepaper
2005 < http://www.empathy.co.uk/ >
The ERIC instrument consists of 29 empathy questions measured
on a 10-point rating scale and 11 call process questions that arerelated to how the calls are processed
The trained researchers (mystery callers) then make 40
unscripted(?) calls over three weeks to each company and
complete an online questionnaire
The study sample was limited to 28 companies in which ROCE andERIC ratings were both available.
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ERIC Testing the claims
Slide 21
Comments: No proven scientific grounding
Non rated Journal, 6 rated referencesused Questionable statistics & sample No longitudinal data or reference to
time Methodology basically mystery caller Psychometric properties of scale no
scientific grounding Mixed construct in scale (15 constructs
across 33 statements Of 5 attributes only one (Empathy) is
an interval scale, all other Yes/no or
numerical (number of calls) Claimed at 2008 CS Conference = False
claim
Source: Lywood, J., Stone, M. and Ekinci, Y. "Customer experience and profitability: An application of theempathy rating index (ERIC) in UK call centres,"Journal of Database Marketing & Customer StrategyManagement (16), 2009, pp. 207-214. & Lywood, J., Stone, M. and Hackett, D. Eric Methodology Whitepaper2005 < http://www.empathy.co.uk/ >
Claimed at 2008 CS Conference:At Last a proven link between a service
related measure and profitability
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Net Promoter Score single net measure
A simple recommend question measured on 0 to 10 scale oflikelihood to recommend
How likely is it that you would recommend (brand or company X)
to a friend or colleague?
Net Promoter score is calculated by taking the percentage ofpromoters (9-10 rating; extremely likely) and the percentage ofdetractors (0-6 rating; extremely unlikely)
NPS = % of Promoters minus % of Detractors
Companies with scores above 75% have world-class loyalty andword-of-mouth, which will correlate with a firms growth1
Slide 22
1Reichheld, F. (2003). The One Number You Need to Grow. Harvard Business Review, Dec 2003
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Net Promoter Score single net measure
Slide 23
NPS adopted by executives: Swift to survey Simple to understand and
communicate Top-of-house dashboard metric
Reichheld (2003): NPS is a moreaccurate predictor of sales growththan the elaborate AmericanConsumer Satisfaction Index1
General Electrics CEO: This is thebest customer satisfaction metric
Ive seen
Positive Negative
1Reichheld, F. (2003). The One Number You Need to Grow. Harvard Business Review, Dec 20032Keiningham, T. et al. (2007). The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet,
Managing Service Quality 17(4), 361-384.3Morgan, N. & Rego, L. (2006). The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance. Marketing Science 25(5), Sep Oct.
Little scientific research linkingrecommend intentions to actualintentions2
Morgan and Rego (2006) assessedsix different metrics over a sevenyear period and found: recentprescriptions to focus customer
feedback systems & metrics solely
on customers recommendation
intentions and behaviours aremisguided3
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Testing the Net Promoter Scoreclaims Contrary to Reichhelds assertions, the results indicate
that recommend intention alone will not suffice as asingle predictor of customers future loyaltybehaviour.
Use of a multiple indicator instead of a single
predictor model performs better in predictingcustomer recommendations and retention.
Thus far, however, there have been no peer-reviewed,scientific investigations examining the relationshipbetween recommend intention and customerbehaviours (outside of customer referral/complainingbehavior).
Slide 24
Source: Keiningham, T., Cooil, B., Aksoy, L., Andreassen, T. and Weiner, J. "The value of different customer satisfaction andloyalty metrics in predicting customer retention, recommendation, and share-of-wallet," Managing Service Quality (17:4),2007, pp. 361-384
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Testing the Net Promoter Score claims
FINDING: The assertion that recommend intention alone willsuffice as a predictor of customers future loyaltybehavior
(Reichheld NPS), however, is not supported. We reach this
conclusion based upon three primary findings.
First, bivariate correlations of all the attitudinal variables
and customer behaviours investigatedtended to be modest. Second, when examining the three primary behaviours
associated with customer loyalty (retention, share of
wallet, and recommendations) recommend intention was
generallynot the best predictorfor each of these variables.
Third, multivariate models universallyoutperformedmodelsthat use only recommend intention
Slide 25
Source: Keiningham, T., Cooil, B., Aksoy, L., Andreassen, T. and Weiner, J. "The value of different customersatisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet," ManagingService Quality (17:4), 2007, pp. 361-384
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Secure Customer Index as Net measure
The Secure Customer Index probes three attributes1: the secure customers were very satisfied,
had a likelihood to definitely continue using the service,
and had a likelihood of definitely recommending the service toothers
Customers grouped into subgroups or loyalty segments
Direct linkage to financial & market performance was
calculated
Slide 26
1Brandt, D. (1996). Customer Satisfaction Indexing, Conference Paper presented at American Marketing Association, USA
Secure Favourable Vulnerable At Risk
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Secure Customer Index (SCI) as net measure
Today the new improved SCI is Burke Incorporateds proprietarymodelling approach
Five dimensions to assist validity and predictions of future share ofwallet:
Burke has studied data which directly links and also projects a
correlation between customer satisfaction, loyalty, and value tofinancial performance
Through projection and direct linkage, they can calculate whichpart of the marketing mix will bring the largest ROI
Slide 27
EarnedLoyalty
Likelihoodto
Recommend
Likelihoodto
Repurchase
OverallSatisfaction
PreferredCompany
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Slide 28
Customer Experience A deep ecological paradigm shift(Fritjof Capra The Web of Life, 1996)
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Slide 29
Key Drivers of Loyalty
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Slide 30
Outcomes of Improved Customer Experience
Outcomes of Customer
Experience
Customer-RelatedOutcomes
Efficiency-RelatedOutcomes
Employee-RelatedOutcomes
Overall Performance-Related Outcomes
Behavioral
Intentions
CustomerBehaviours
CustomerCommitment
RepurchaseIntentions
Price Perceptions &Willingness to pay
Customer Loyalty &Repurchase Behaviour
Word-of-Mouth &Complaining Behaviour
FinancialPerformance
NonfinancialPerformance
Source: Luo, X & Homburg, C. April 2007 Neglected Outcomes ofCustomer Satisfaction. Journal of Marketing, Vol 71, Apr 2007 (0 133-149)
Behavioral Intentions are determined byhow the drivers of Customer Satisfactionare managed
this is the essence of CustomerExperience Management
CustomerDefection
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis Research Results & Discussion Dangers of Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
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Research Question
The popularity of the Net Promoter Score has
highlighted the use of net measures in customerexperience measurement
Considering the preceding literature review anddiscussion regarding different net measures, it is
obvious that no single measure can be usedsuccessfully in measuring the complex constructsof customer experience, customer satisfactionand customer loyalty
This presentation will explore a quantitativemodel that integrates the best-of-both-worldsthrough a combined metrics of net measures anda multi-attribute measure of customerexperience
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Research Objectives
Slide 33
Explore the use and application of Net Measures in themeasurement of Customer Experience
Compare Net measures in terms of reliability, validity,predictive ability and practical application
Position Net Measures within the body of knowledge ofmulti-attribute Customer Experience Measurementtheory and practise
The purpose of this study is toinvestigate the following threeobjectives:
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Research Design & Data Collection
Meta-analysis on data collected over a time frame of more than 5
years, covering more than 1.5 million customer interviews acrossSouth Africa
Survey results have been consolidated from enterprise wideproprietary customer satisfaction surveys across a range of clients
For the purpose of this presentation (and reliability) the data is
limited to results from surveys in the financial services industry inSouthern Africa
Respondent selection for each of the surveys under considerationwas quota-based from client contact lists on proportional stratifiedsample designs
At the time of the interview, the respondent was a currentcustomer of the financial service provider being evaluated, andfilter-controlled for having a recent interaction at a specificchannel (enterprise-wide metrics across channels across segments)
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Research Design & Data Collection
Survey data was collected via telephonic, web-based and face-to-face interviews
To ensure minimal non-sampling error, all interviews were subjectto strict quality assurance processes, and advanced technology wasused to capture data
No ethical issues are relevant to the study since most of thefindings will be reported at meta-data levels without identifyingany specific sponsoring company (to protect confidentiality andproprietary measures)
A strict ESOMAR code-of-conduct was followed in all data
collection. The respondents were made aware of the institutionssponsoring the survey and for what purposes the information wouldbe used
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis
Research Results & Discussion Dangers of Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
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Research Methodology & Instruments
Prof Adr Schreuder developed a conceptual cause-and-effect model illustrated as an integratedcustomer experience measurement
Developed through years of academic researchcombined with extensive experience regarding
Customer Satisfaction measurement across multipleindustries
Basis for measurement is a structural model ofcustomer satisfaction that incorporates theimportant constructs of satisfaction that willidentify underlying service or product deficiencies(or strengths) and a proprietary algorithm forintegrating net measures into this multi-attributemodel
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Slide 38
The CONSULTA Integrated Customer ExperienceMeasurement Model
FAILUREFAILURE DELIGHTDELIGHT
FAILUREFAILURE DELIGHTDELIGHT
FAILUREF AI LU RE D EL IG HTDELIGHT
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Slide 39
The Conceptual Model Flow
Copyright Consulta Research - 2010
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Slide 40
Principle Calculation of Modeled Scores
FAILUREFAILUREDELIGHTDELIGHT
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Slide 41
Instrument Development Process
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Slide 42
Model Development Process
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Use an Enterprise-wide Model A Retail Bankingexample
Slide 43
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Present CE Metrics in Dashboards
Slide 44Slide 44
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Research Methodology & Instruments
Slide 45
For this reason the customer experience index score is notreported in isolation as a single number, but merely as the net
result of multiple items, each of which contains detail results and
offers valuable strategic information into the management ofcustomer delight, loyalty, propensity to shift, service recovery,
corrective improvement measures and consequence management
It is important to be able to delve deeper into the results toenable the receiver to delve deeper than satisfaction
The integrated customer experience measurement, although
resulting in a final index score, acknowledges the fact that a singlevalue for an index might hide more that it reveals
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Research Methodology & Instruments
Research Instruments:
Same basic layout including sections corresponding to thecomponents contained in the conceptual model for customersatisfaction measurement
First section measures specific channels value proposition with
a range of custom designed service attributes - incorporates
both customer perception and customer expectation by usingconfirmation-disconfirmation scale
Specific questions on product quality, service quality,relationship quality & pricing as contributingfactors/components of customer satisfaction
Slide 46
0 1 2 4 53 9 10876
Much worse than expected Much better than expected
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Meta-data and Analysis
For each of the surveys the statistical analysis (using the statistical
software package STATISTICA) included: reliability and factor analysis;
structural equation modelling;
multiple regression analysis
The result, for each of the surveys, was a unique structural (cause-
and-effect) model of customer satisfaction that considers all theimportant drivers of satisfaction
Final data set used for meta-analysis contained each of thecomponents defined on next slide
Included 704 separate customer satisfaction studies forming part
of the enterprise wide measurement of customer experience, foreach of the financial institutions - each with a sample of at least100 respondents and more
Slide 47
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Meta-data and Analysis
Slide 48
Metric Description
Weighted serviceattribute average score A weighted average of the (unique channel) serviceattributes measured in terms of customer expectation
Service problems % Proportion of respondents who indicated that theyexperienced a service problem within a certain time period.This is different from the proportion of respondents
complaining (formally or informally) as measured in ACSI
Problem recovery % Proportion of respondents who indicated that their serviceproblem was recovered to their satisfaction
Overall delight % Proportion of respondents who gave a 9 or 10 rating out of
10 for overall satisfaction. This is much more strict than thetypical Top 2 Box metric calculated on a 5 point verbal scale
or the equivalent top four boxes on the ten-point ACSI
scale
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Meta-data and Analysis
Slide 49
Metric Description
Overall failure % Proportion of respondents who gave a 0 or 1 rating out of10 for overall satisfaction
Average score (overallsatisfaction)
A simple average of overall satisfaction rated on a scalefrom 0 to 10
Customer satisfaction
index score
Index score (out of 100) is a function of the following key
elements: Underlying structural model Basic calculation principle of being rewarded for
positive ratings and being penalised for negativeratings corresponding to the concept of a net measure
Net Promoter Score Calculated according to the original definition of Reichheld(2003) the Net Promoter Score equals the % of promotersminus the % of detractors
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis
Research Results & Discussion Dangers of Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
Slide 50
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The Dangers of Reporting Net Measures inIsolation
Danger/weakness inreporting any netmeasure (in isolation):two measurementshaving exactly thesame value for the netmeasure can in facthave a range ofdifferent valuesassigned to thecomponents of the net
measure
Slide 51
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The Dangers of Reporting Net Measures inIsolation
Recommendation not onlyapplicable to netmeasures, but to othersimple statistical
measures (e.g. the samplemean) as well
A variety of differentrespondent values canalso yield the same resultfor the specific statisticalmeasure and typicaldistribution detailand/or graphs providemore insight into theresults
Slide 52
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Satisfaction Measures as Predictors of the NPS
As is to be expected, service problems and failure ratings show anegative correlation with customer satisfaction and NPS, whiledelight ratings show a positive correlation. Service problemrecovery shows a very low, but positive, correlation with the NPS NOTE poor R2
Slide 53 Sample Base: 1.5million respondents
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Satisfaction Measures as Predictors of the NPS
Slide 54
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Satisfaction Measures as Predictors of the NPS
Individually, as independent variables in modelling the Customer
Loyalty, the graphs and correlation coefficients clearly show thatthe integrated index score with an R2 of 0.73 seems to be the bestpredictor of the Net Promoter Score
Slide 55 Sample Base: 1.5million respondents
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Satisfaction Measures as Predictor
However, we do notrecommend either the NPS orcustomer satisfaction index score in isolation as thebest and sufficient measurement to evaluate businessperformance, but agree with Schneider et al. that
using a variety of measures rather than simply onemeasure would better capture the complexityunderlying customer satisfaction and customer
behaviours
Slide 56Schneider, D.; Berent, M.; Thomas, R. & Krosnick, J. (2008). Measuring Customer Satisfaction and Loyalty: Improving the Net-PromoterScore. Poster presented at the Annual Meeting of the American Association for Public Opinion Research, New Orleans, Louisiana
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Integrated Satisfaction Measure as Predictor
The net measure(s) in itselfcan provide a top line measurement to trackperformanceor even be effectively used as a top-of-house executive
indicator
Analysing the detail of all the different metrics constituting the customersatisfaction index score and NPS will assist greatly in the need for rootcause analyses and strategic/tactical direction
The quantitative data analysis of these measures is further enriched byqualitative questions similar to the whys asked by GE, including
verbatim descriptions of service problems that were experienced,suggestions on improving service delivery, etc.
Slide 57
Normality of Customer Experience
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Normality of Customer ExperienceModelled Score
Due to more complex nature of its calculation, efforts to examinestatistical properties of net measures using a mathematicalapproach can be tedious and difficult
Computer-intensive simulation methods such as the bootstrapprovide a solution
The bootstrap method was applied to replicate 1 000 bootstrapsamples for each of four different studies each bootstrap sampleconsisted of 380 respondents chosen randomly (with replacement)from the survey data
This provided 1 000 simulated index scores, which can be plottedas histograms and normal probability plots
The accuracy of the simulations increase as the number of bootstrap replications
increase; 500 or more simulations are sufficient to reduce variability and provide
accurate results
Slide 58
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Normality of Customer Experience Modelled Score
Slide 59
Variable: VoC1, Distribution: Normal
Chi-Square test = 8.67399, df = 9 (adjusted) , p = 0.46790
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Category (upper limi ts)
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RelativeFrequency(%)
Variable: VoC2, Distribution: Normal
Chi-Square test = 5.06307, df = 7 (adjusted) , p = 0.65227
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Category (upper limi ts)
0
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RelativeFre
quency(%)
Normal Probabili ty Plot of Vo C1 (4 VoCs for normality g raphs 4v*1000c)
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Observed Val ue
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ExpectedNormalValue
VoC1: SW-W = 0.998084051, p = 0.3196
Normal Probabili ty Plot of Vo C2 (4 VoCs for normality graphs 4v*1000c)
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Observed Val ue
-4
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ExpectedNormalValue
VoC2: SW-W = 0.998708772 = 0.6945
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Normality of Customer Experience Modelled Score
Slide 60
Variable: VoC3, Distribution: Normal
Chi-Square test = 8.15932, df = 7 (adjusted) , p = 0.31876
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Category (upper limits)
0
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RelativeFrequency(%)
Variable: VoC4, Distribution: Normal
Chi-Square test = 6.36535, df = 7 (adjusted) , p = 0.49779
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Category (upper limits)
0
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RelativeFreq
uency(%)
. , .
Normal Probabili ty Plot of VoC3 (4 VoCs for normality graphs 4v*1000c)
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Observed Val ue
-4
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-1
0
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ExpectedNormalValue
VoC3: SW-W = 0.998033823, p = 0.2971
Normal Probabili ty Plot of VoC4 (4 VoCs for normality graphs 4v*1000c)
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Observed Val ue
-4
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-2
-1
0
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ExpectedN
ormalValue
VoC4: SW-W = 0.998200047, p = 0.3767
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Normality of Customer Experience Modelled Score
For all four studies, both the chi-square test and Shapiro-Wilk testdid NOT reject normality of the customer satisfaction index score,which holds the benefit of statistical inference of the index score(e.g. calculating confidence intervals and performing hypothesistesting)
Although these results are based on only four studies, representinga small portion of the wide range of underlying models used todescribe the results of the various studies, we believe that withadditional research we will be able to establish similar results forthe whole range of studies under consideration, and consequentlyestablish normality for the customer satisfaction index score in
general
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Index
Background & Rationale for Research Previous Research and Literature Review
Research Question & Objectives
Research Methodology & Data Analysis
Research Results & Discussion Reporting Net Measures in Isolation
Satisfaction Measures as Predictors of NPS
Normality of Customer Experience Modelled Score Conclusion
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Conclusion
Without denying the fact that net measures has a role to play, theuse of net measures as standalone questions has been shown tohave some disadvantages
Reporting net measures in context, supported by the multipleitems it contains, provides the opportunity to analyse the detail ofall the different metrics constituting the net measure
This assist in the need for root cause analyses andstrategic/tactical direction, while the net measure in itself canprovide a top line measurement to track performance or even beeffectively used as a top-of-house executive indicator
The quantitative data analysis of these measures can further beenriched by qualitative questions, including verbatim descriptionsof service problems that were experienced, suggestions onimproving service delivery, etc.
Slide 63
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Conclusion
Using longitudinal meta-data analysis of more than 1.5million customer satisfaction measurement interviews,we have presented reliable correlations between theNet Promoter Score and an Integrated CustomerSatisfaction Index score, as well as establishingstatistical properties of these measures
The Customer Satisfaction Index score can be classifiedas a combined multi-attribute and net measureapproach, since it incorporates the net effect of
failure and delight ratings, as well as serviceproblems and the recovery thereof
Slide 64
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Conclusion
Understanding that customers, as human beings, arecomplex by nature and accepting that the
measurement of customer satisfaction involves the
measurement of a complex construct, the use of anintegrated measure of multiple-item & net measureshas the advantage of providing insight into
underlying drivers of customer satisfaction, whilealso offering a simple top-of-house dashboard
metric that is simple to communicate.