| faculty of economics and business department of marketing what you do and how you tell it: it...
TRANSCRIPT
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faculty of economicsand business
department of marketing
What you do and how you tell it: it matters!
Insights on the impact of service quality and message content on firms’ success
KUMPEM Forum Retail ConferenceIstanbul, May 14-15, 2015
Maarten J. Gijsenberg University of Groningen
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faculty of economicsand business
department of marketing
Losses loom longer than gains
The Impact of Service Crises on Perceived Service Quality over Time
Maarten J. Gijsenberg – University of Groningen
Harald J. van Heerde – Massey University
Peter C. Verhoef – University of Groningen
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faculty of economicsand business
department of marketing
Mass service crises
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faculty of economicsand business
department of marketing
Mass service crises› Characteristics of mass service crises
Strong and sustained drops in operational service performance Affecting many customers at the same time
- Production and consumption: same time- All consumers using the service are affected
› Similar to, but different from, product-harm crises Products are defective, causing harm to users, often leading to costly
product recalls (e.g. Van Heerde, Helsen, and Dekimpe 2007)
Negative impact often limited to subset of customers- Production and consumption: different times
Defective products can be recalled before consumption
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faculty of economicsand business
department of marketing
Services performance & satisfaction› Service performance is important driver of customer satisfaction
Satisfaction formation according to Expectancy-(dis)confirmation paradigm(Bolton and Drew 1991; Oliver 1977; 1980; Szymanski and Henard 2001)
Negative experiences have strong effect on satisfaction (Anderson and Sullivan 1993)
› Service failures Limited attention in literature, mainly in service recovery literature
(e.g., Smith, Bolton and Wagner 1999)
Focus on individual-customer level service failure
› Mainly short-term focus Limited longitudinal research on customer satisfaction
(e.g., Mittal, Kumar, and Tsiros 1999; van Doorn and Verhoef 2008)
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faculty of economicsand business
department of marketing
Objectives› What are the short- and long-term effects of objective service
performance changes on perceived service quality?
› Do losses in objective service performance not only loom larger than gains, but do they also loom longer?
› Do these effects depend on the trend in objective service performance?
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faculty of economicsand business
department of marketing
Dynamic effects› Service restoration
Excellent recovery can lead to higher satisfaction than before crisis (Smith and Bolton 1998)
Negative experiences have stronger effects than positive experiences(e.g., Antonides, Verhoef and Van Aalst 2002; Inman, Dyer and Jia, 1997)
Service restoration may not be strong enough to attain pre-crisis levels of satisfaction- Losses may loom longer than gains
› Trend in service performance may affect customers’ mindsets “What have you done for me lately” heuristics (Smith and Bolton 1998)
- Recent performance affects expectations- Contrast and assimilation effects (Bolton 1998)
Prior beliefs also directly impact expectations (Boulding, Kalra and Staelin 1999)
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faculty of economicsand business
department of marketing
Data› Large European logistics service company
› Monthly data for seven years January 2006-October 2012
Objective Service Performance- % successful connections
Perceiced Service Quality- Answer scale: 10 = excellent, 9 = very good, 8 = good, 7 = more than
sufficient/satisfactory, 6 = sufficient/satisfactory, 5 = inadequate, 4 = very inadequate, 3 = bad, 2 = very bad, 1 = could not be worse
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faculty of economicsand business
department of marketing
OSP & PSQ
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faculty of economicsand business
department of marketing
OSP & PSQ
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faculty of economicsand business
department of marketing
Parameter estimate Standard error p-value
PSQ equation
Constant -.060 .412 .884 .011* .006 .091
-.027** .011 .016.002 .110 .983-.000 .007 .951.023** .010 .033
-.308** .110 .007-.010* .005 .058-.019** .008 .020
R² .568 AIC -3.474 BIC -3.203
OSP equation
Constant 44.487** 8.475 .000
9.063** 3.219 .006.515** .092 .000
R² .423 AIC 3.755 BIC 3.845
Model results
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faculty of economicsand business
department of marketing
› IRF over all possible histories
Improved service performance- No long-term effect
Long-term effects
Decrease in service performance- Negative long-term effect
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faculty of economicsand business
department of marketing
Impact of performance history› 3 scenarios
Business as usual- Relatively constant (up-down or down-up)
Sustained gains- Upward trend in performance
Sustained losses- Downward trend in performance
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faculty of economicsand business
department of marketing
Business as usual
Improved service performance- Positive short-term effect, no
long-term effect
Decrease in service performance- Negative short- & long-term effects
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Conclusion: losses loom longer than gains, as before
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faculty of economicsand business
department of marketing
Sustained gains
Improved service performance- Positive short- & long-term effects
- Explanation: customer delight
Decrease in service performance- Negative short-& long-term effects
- Explanation: Extreme negative expectancy disconfirmation
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faculty of economicsand business
department of marketing
Sustained losses
Improved service performance- Positive short-term effect, but
negative long-term effect- Explanation: less predictability,
more risk, stronger effect of negative experiences
Decrease in service performance- Negative short-term effect, but no
long-term effect- Explanation: confirming expectations
of bad and even ever worse service
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faculty of economicsand business
department of marketing
Implications › Service recovery needs to more than overcome the service
failure to keep long-term customer satisfaction constant The bar for future performance is raised
› Be mindful about the trend in performance Upward shocks only have favorable long-term consequences during
upward trends Downward shocks have strong negative long-term consequences
during upward trends and stable situations Steady as it goes (up or down) is better for long-term satisfaction than
up-down or down-up scenarios as the latter create more “risk” for consumers
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faculty of economicsand business
department of marketing
Probably the best message in the world
The impact of consistency and overlap in advertising content on brands’ success
Mike Friedman - UC Louvain
Maarten J. Gijsenberg - University of Groningen
Nicolas Kervyn - UC Louvain
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faculty of economicsand business
department of marketing
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faculty of economicsand business
department of marketing
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faculty of economicsand business
department of marketing
Background› Huge amounts of money invested in advertising
› Good insights about returns to adspend, but what about content? Much anecdotal evidence Much experimental evidence on “soft” outcomes
- Mainly on overlap- Some on variation
No longitudinal evidence, no evidence on “hard” outcomes
› Should brands try to be consistent in their message over time?
› Should brands try to be different from competitors?
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faculty of economicsand business
department of marketing
Consistency› Strong brands are built by consistent long-term communication
support (Keller 2008)
› Consistency in advertising content expected to have positive effect Mere exposure effects (Zajonc 1968)
Prior exposure to same stimuli elicits positive affect towards the stimuli (Janiszewski and Meyvis 2001)
Creating and reinforcing nodes and associations in consumers memory (associative memory models: Anderson 1983; Wyer and Srull 1986; Keller 1993)
- More easily retreived and activated (e.g. Albrecht and Myers 1995; 1998; Wyer 2004; Luna 2005)
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faculty of economicsand business
department of marketing
Overlap› The extent to which the content of the advertising message is
similar to messages by other brands
› Successful brands take unique position in consumers’ minds Clear positioning (e.g. Aaker 1996; Keller 2008)
Clear communication of unique benefits (e.g. Aaker 1996; Keller 2008)
› Overlap in advertising content expected to have negative effect Distinctive information is easier to retrieve (e.g. Craik 1979; Eysenck 1979)
Competitive interference and brand confusion- Unconnected memory traces that resemble each other will get activated
simultaneously (e.g. Keller 1987; 1991; Poiesz and Verhallen, 1989)
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faculty of economicsand business
department of marketing
Overlap› Is overlap always bad?
Not necessarily!- Brands should create “deep” awareness (Keller 2008)
- Strong links to product category- High top-of-mind awareness
› Effects may consequently depend on type of content Different types of nodes in the ad memory trace (cfr. Hutchinson and Moore 1984)
- Category-related: e.g. how and when to use the product- More overlap likely beneficial: clear category link
- Product-related: e.g. unique product features / benefits- More overlap likely detrimental: no unique product features / benefits
- Brand-related: e.g. brand values- More overlap likely detrimental: no unique brand positioning
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faculty of economicsand business
department of marketing
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Message Consistency
Category factors+
Product factors+
Brand factors+
Message Overlap
Category factors+
Product factors-
Brand factors-
Marketing controls
Relative adspend+
Relative price-
Time controls
Seasonality
Trend
Brand market share
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faculty of economicsand business
department of marketing
Data› United Kingdom
Chocolate
› 2008p2 – 2012p3: >4 years of data, 4-week periods Transcripts of all print and tv advertising messages per brand Volume sales, price and advertising spending per brand
› Focus on most active advertisers 66 brands in the category
- Many of them very infrequent advertisers and low-share brands Initial choice: top-10 most active advertisers Only advertising spending available for 7 of these top-10 brands
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faculty of economicsand business
department of marketing
General descriptives
Market Share # Messages Yearly Adspend(*1000)
Sample 7 brands 30.8% 336 £26,986Mean 4.4% 48 £3,855
Stdev 4.3% 26.5 £2,977 Max 13.3% 88 £10,410
Min .1% 18 £1,407 Category 66 brands 100% 839
Mean 1.5% 12.7 Stdev 2.9% 18.6 Max 17.9% 88
Min .0% 0
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faculty of economicsand business
department of marketing
Text analytics› Focus on the following factors
Category factors- Usage context of the product category: social psychological processes
Product factors- Biological processes
Brand factors - Personal concerns
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faculty of economicsand business
department of marketing
Category: consistency & overlap
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Category factors Hyperparameter Z-score
Consistency Short run 0.480 *** 2.330
Long run 0.645 *** 2.735
Overlap Short run 0.220 *** 3.423
Long run 0.317 *** 3.236
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
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faculty of economicsand business
department of marketing
Product: avoid overlap
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Product factors Hyperparameter Z-score
Consistency Short run -0.100 -0.949
Long run -0.059 -0.449
Overlap Short run -0.199 *** -4.184
Long run -0.191 *** -3.220
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
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faculty of economicsand business
department of marketing
Brand: consistency
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* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
Brand-related factors Hyperparameter Z-score
Consistency Short run 0.147 * 1.631
Long run 0.240 ** 2.149
Overlap Short run 0.058 1.444
Long run 0.092 1.476
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faculty of economicsand business
department of marketing
Managerial implications› It pays off to clearly link the product/service to the category, and
to resemble your competitors in that sense Not just once, but in a sustained way
› When positioning the product/service, it is important to be clearly different than competitors What makes the product/service so unique?
- Look into those characteristics that do matter to customers, and stress unique features
› When positioning the brand, it is important to be consistent over time What is the brand identity/image?
- What are the personal concerns of customers the brand appeals to?
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faculty of economicsand business
department of marketing
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faculty of economicsand business
department of marketing
› Contact Information dr. ir. M.J. Gijsenberg
Assistant Professor of Marketing
Department of Marketing Faculty of Economics and Business University of Groningen PO Box 800 9700 AV Groningen The Netherlands
Tel +31 50 363 8249
E-mail [email protected]
www.rug.nl/staff/m.j.gijsenberg
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faculty of economicsand business
department of marketing
Appendix 1
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faculty of economicsand business
department of marketing
Asymmetries in PSQ evolution› Asymmetry Tests (e.g. Deleersnyder et al. 2004; Lamey et al. 2007 Randles et al. 1980)
Deepness asymmetry (-.036; p<.05) - Perceived Service Quality shows stronger decreases than recovery
Steepness asymmetry (-.049; p<.05)- Perceived Service Quality shows faster decreases than recovery
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time
PSQ
time
PSQ
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faculty of economicsand business
department of marketing
Econometric model› Starting point: Bivariate Structural Var model (Pauwels 2004)
› Allowing for asymmetric effects
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faculty of economicsand business
department of marketing
Econometric model› Final model: Double-Asymmetric Structural Vector AutoRegressive
(DASVAR) model
Asymmetric effects of decline vs improvement in service performance Asymmetric number of lags L1 and L2 across equations
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faculty of economicsand business
department of marketing
Econometric model› Long-term effects
Impulse-Response Functions Importance of performance history
- Effect of one-time shock depends on history prior to the shock- Role of asymmetric effects (cfr. Killian and Vigfusson 2011)
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faculty of economicsand business
department of marketing
Model fit
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MAPE Asymmetric Lags Symmetric Lags
Asymmetric Losses-Gains
Focal Model:DASVAR
Benchmark 2:Asymmetric Effect SVAR
In-sampleOut-of-sample
.454%
.489%In-sampleOut-of-sample
.472%
.543%
Symmetric Losses-Gains
Benchmark 1:Asymmetric Lag SVAR
Benchmark 3:Symmetric SVAR
In-sampleOut-of-sample
.473%
.552%In-sampleOut-of-sample
.474%
.553%
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faculty of economicsand business
department of marketing
Predictive performance
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faculty of economicsand business
department of marketing
Conclusions › Losses not only loom larger but also longer than gains
Deepening of existing knowledge on prospect theory
› Important “moderating” role of service performance history Might occur due to different mindsets of customers Reinforcement of prior beliefs
› DASVAR model Goes beyond traditional (S)VAR models by
- Including asymmetric number of lags across equations- Including asymmetric effects of service performance losses vs gains
- Allows for IRFs conditioned on performance history Is superior to models not allowing for these asymmetries
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faculty of economicsand business
department of marketing
Appendix 2
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faculty of economicsand business
department of marketing
Issue
› To be consistent and/or to be different: that is the question
› This study: Quantifies consistency and overlap in advertising messages Quantifies the effect of consistency and overlap in advertising
messages on brands’ performance Investigates whether effects are different for different types of
advertising content Investigates whether we find differences between short- and long-
term effects
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faculty of economicsand business
department of marketing
Methodology › 5 steps
Extract information using text analytics Define consistency and overlap Basic model: error correction model Endogeneity Individual and overall insights
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faculty of economicsand business
department of marketing
Text analytics› Linguistic Inquiry and Word Count (LIWC2007) (Pennebaker et al. 2007)
Classifying text content of the messages according to predefined libraries
Main focus: - Linguistic processes- Psychological processes- Personal concerns
Result:- Scores on 1-100 scale on different categories of words.
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faculty of economicsand business
department of marketing
Consistency and overlap
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faculty of economicsand business
department of marketing
Basic model› Error correction model at the brand level
Estimate per brand, separately for each factor
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faculty of economicsand business
department of marketing
Endogeneity› Control for possible endogeneity of relative adstock and relative
price
Simultaneous estimation with full variance-covariance matrix
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∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡=𝛾𝑎𝑑𝑣 ,0+𝛾𝑎𝑑𝑣 ,1∗∆𝑅𝑒𝑙𝐴𝑑𝑠𝑡𝑡 −1+𝛾𝑎𝑑𝑣 ,2∗∆ h𝑀𝑆 𝑎𝑟𝑒𝑡−1+𝜈𝑎𝑑𝑣 ,𝑡
∆𝑅𝑒𝑙𝑃𝑟𝑖𝑐𝑒𝑡=𝛾𝑝𝑟𝑖 ,0+𝛾𝑝𝑟𝑖 ,1∗∆𝑅𝑒𝑙𝑃𝑅𝑖𝑐𝑒𝑡−1+𝛾𝑝𝑟𝑖 , 2∗∆ h𝑀𝑆 𝑎𝑟𝑒𝑡 −1+𝜈𝑝𝑟𝑖 , 𝑡
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faculty of economicsand business
department of marketing
Individual & overall insights› Basic estimation for individual brands
Allowing for differences among brands (heterogeneity) Per brand, separate estimate for each factor Per brand, combine estimates for control variables across estimations
› Combine individual-brand estimates into overall insights Meta-analytic approach
- Uncertainty-weighted average parameter estimate- Significance: Added-Z method
(e.g. Rosenthal, 1991; Van Heerde et al., 2013; Gijsenberg, 2014)
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faculty of economicsand business
department of marketing
Main model: control variables
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Main equation Hyperparameter Z-score
Intercept 10.555 °°° 5.596
x Sinus 0.075 °°° 3.651
x Cosinus -0.227 °°° -8.109
x Trend -0.007 °°° -4.648
x NoAdv -1.099 * -1.641
∆RelPrice -2.033 *** -6.979
∆RelAdstock -0.041 -1.029
LagRelPrice -1.793 *** -4.722
LagRelAdstock 0.039 0.949
Adjustment -0.986 *** -12.082
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
β0β1β2β3β4
𝛼1𝑠𝑟
𝛼2𝑠𝑟
𝛼1𝑙𝑟
𝛼2𝑙𝑟
Π
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faculty of economicsand business
department of marketing
Side equations
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Advertising equation Hyperparameter Z-score
Intercept 0.001 0.046
Lag∆RelAdstock -0.071 -1.355
Lag∆MShare 0.180 °°° 2.032
* p < 0.10, one-sided; ** p < 0.05, one sided; *** p < 0.01, one-sided ; ° p < 0.10, two-sided; °° p < 0.05, two sided; °°° p < 0.01, two-sided
Price equation Hyperparameter Z-score
Intercept 0.000 0.063
Lag∆RelPrice -0.359 °°° -5.467
Lag∆MShare 0.009 0.613
𝛾𝑎𝑑𝑣 , 0
𝛾𝑎𝑑𝑣 , 1
𝛾𝑎𝑑𝑣 , 2
𝛾𝑝𝑟𝑖 ,0
𝛾𝑝𝑟𝑖 ,1
𝛾𝑝𝑟𝑖 ,2