new frontiers (and new findings) on emotion in customer ... · angry – rude customer. polite...

New Frontiers (and New Findings) on

Emotion in Customer Service

Professor Anat Rafaeli

Slides and papers available at http://Anat.Rafaeli.Net

Service Loaded with Emotions!


Logic: Employee Emotions (Smile, Apology)

Essential for Sales and Service; Emotions are easy, do not require effort;

Emotional Labor:Jobs Require Employees to

Display Expected Emotions

Rafaeli & Sutton (1985), Academy of Management Review

Formal Expectation: Pleasantness, Empathy to ALL Customers

Available Research:Based on Self-Report Limited External Validity

Limited Operational Connections

Smile and Be Nice Rule

Rafaeli & Sutton (1987), Research in Organizational Behavior

Rafaeli (1989):

A Struggle for Control

A Social Context toService Interactions


Henkle, Rafaeli et al (2016):

When Marketplace Interactions

BecomeSocial Interactions

Physical Set Up

Multiple Managers

Multiple Causes of Stress

The Service Context

Pratt & Rafaeli (2001), Symbols and organizational relationships, Research in OB

What Do Queues Feel Like?

Rafaeli & Munichor (2007), Journal of Applied Psychology

Lots of Customer Anger in Service: What is the Influence?

What are the Implications?


Service through interactive technology and social networks;

Extensive NON-OBTRUSIVE data.

New tools and paradigms!

New Platforms Create Great Opportunities

Rafaeli & Altman (2016), Journal of Service Research

No News in Terms ofEmotion Labor Rules!


Written Interactions(through Tweet or Chat)Customers

Do customer emotion displays influence employees?

New Service Context

Experimental Research Paradigm

Angry Rude Customer Polite CustomerIts such a nightmare to reach you! Your service is just horrible. Update my home phone to 03-7526654. George Ashley

My home phone number changed. Need to update it please. My number is 03-7526654. Thank you very much, George Ashley.

I am sick and tired of your lousy service. Move me to the weekend deal. Password is "Friends". Josh.

Hi please note my request to move me to the weekend deal. My password is "Friends". Thank you, Josh.

Rafaeli et al. (2012), Journal of Applied Psychology

Customer Anger Reduces Employee Accuracy

Rafaeli et al. (2012), Journal of Applied Psychology
Chart1angryneutralDisplayed emotion of customerPercent of requests handled correctly0.4550.5651excellentcorrecteeper_vip_exper_vip_regangry0.467941angry0.455angry3.52angry0.40.49angry4.7neutral0.549429neutral0.565neutral2.77neutral0.530.55neutral5.8angryneutral1Displayed emotion of customerPercent of requests handled correctly and quickly2Displayed emotion of customerPercent of requests handled correctly3Displayed emotion of customerEmotional exhuastionEffect of customer emotion on employee emotional exhuastionneutralneutralvipregularDisplayed emotion of customerPercent of requests handled correctly and quicklyInteraction of VIP and displayed emotion on PerformanceDisplayed emotion of customerNumber of correct answersEffect of customer emotion on cognitive performance (problem solbing test)

Customer Anger Increases Employee Fatigue (Burnout)

Chart1angryneutralDisplayed emotion of customerEmotional exhuastion3.522.771excellentcorrecteeper_vip_exper_vip_regangry0.467941angry0.455angry3.52angry0.40.49angry4.7neutral0.549429neutral0.565neutral2.77neutral0.530.55neutral5.8angryneutral1Displayed emotion of customerPercent of requests handled correctly and quicklyEffect of customer emotion on performance(Quality and quantity measure)2Displayed emotion of customerPercent of requests handled correctlyEffects of customer emotion on performance (quality measure)3Displayed emotion of customerEmotional exhuastionneutralneutralvipregularDisplayed emotion of customerPercent of requests handled correctly and quicklyInteraction of VIP and displayed emotion on PerformanceDisplayed emotion of customerNumber of correct answersEffect of customer emotion on cognitive performance (problem solbing test)

Cumulative Effects on Accuracy of Performance

Chart10123Number of Angry CallsMemory Performance65.564.444.14Sheet10615.5624.4434.14Sheet1Number of Angry CallsMemory PerformanceSheet2Sheet3

Habituation Challenges


Field Research: Service Through Text

Chat with McCafee call center started @ 13:23:07. Full transcript can be found at :https://community.mcafee.com/thread/28171

SYSTEM: Welcome to McCafee. How may I help you?Customer (13:37:46):Hi. I purchaed a disk and it doesnt work. Employee (13:41:33):Please let me know the locations you purchased the CD.Customer (13:41:46):Ive been waiting for this info for 20 mins.Employee (13:42:00):You can contact the McAfee Sales team at +91 80 6656 9000 to renew the McAfee software.Customer (13:42:17):Come on.Customer (13:42:29):Don't pass the bill.Customer (13:42:46):Youre not answering my question.Customer (13:43:05):Can I chat with your supervisor?Employee (13:43:22):I apologize for the inconvenience.Customer (13:43:33):Can I have a chat with your supervisor?

@BestBuySupport

Large scale data sets Actual customer and

employee behaviors Non-obtrusive measures

Rafaeli & Altman (2016), Journal of Service Research
https://community.mcafee.com/thread/28171

Multiple Terms for Customer Negative Emotion

Anger Abuse Bullying Deviance Contempt Irritation ... Rudeness

Focus: (High Arousal) Negative Emotions


Emotion in Tweet Service

@VanDusenEthan

@BestBuySupport

EmployeeApologyEmpathy

CustomerAnger

Frustration

@BestBuy too bad your site keeps saying my email is invalid. You just lost a $300 dishwasher sale.

@VanDusenEthan I'm sorry you were unable to purchase. Indeed it is annoying.


Emotion in Tweet Service

Data Set 1:N = 305 service conversations

Customer Satisfaction

Data Set 2: N = 305

StudyHerzig, J., et al (2016). Predicting Customer Satisfaction Using Affective Features. UMAP

Mturk Ratings Emotions expressed by customers

Emotional labor strategies of employees

Customer Emotions

Employee Emotional Strategies

? Customer Satisfaction

Mturk 1 Workers

Mturk 2 Workers

5 Raters per conversation; Agreement of raters;

ICC2=0.73 0.94

Results

Customer Emotions Employee Emotional Strategies

EmpathyApology

ThankingBeing cheerful

AngerFrustrationDisappointment

HappinessGratitude

Negative Emotions

Positive Emotions

Support

Positivity

CFA confirmed model superiority over 2-factor model2[21df] = 106.83, p = .001, CFI = 0.98, TLI = 0.96, RMSEA = 0.07


Effects of Employee Emotion Strategies on Customer Satisfaction

Employee Expressing Positivity

Employee Expressing

Support


+-


Role of Customer Emotion?


Employee Apology

Customer Negative Emotions

-.42*** (.13)-.45*** (.12)

Only Long Enough ConversationsN = 168 conversationsAdj. R2 = .52***

b (SE)

Up to 3 customers at a time!

Natural Language Processing

Dictionary of words defined as emotional (e.g., LIWC)

Emoticons and CAPS

Added rules (amplifier, negation e.g., Very, Not)

Negative PositivePrecision 0.72 0.87Recall 0.24 0.57

Automated Emotion Detection


14%7%

79%

37,189 CustomerMessages

37%

18%

41%

4%

7,147 Full Chats

Positive Negative

No-Emotion Mixed

Emotion in Chats(two weeks; airline sales and service)

14%8%

78%

10,035,32 Customer Messages

38%

10%14%

38%

677,936 Full Chats

Positive Negative

No-Emotion Mixed

Emotion in Chats (11 weeks; telecommunication)

One week data;14,700 conversations.Sample 10 time points in each; (messages at 0%, 11%, 22% ); calculate emotion at each point;Average 10 sample points across all company conversations;

Unfolding of Emotion

No emotion

Negative

Positive


Emotion patterns of satisfied and dissatisfied customers;Tele-comm company chats;Data of one month26703 conversations with NPS = 109382 conversations with NPS = 0

Emotion Dynamics in Different NPS Scores


Note:All are from customer 1.All are from customer 2.All are from the same Employee.

with no represent earlier sent messages.

12:00 12:01 12:02 12:04 12:05 12:08

Serv

ice

Tim

e =

RT (C

1)

1 m

in

Serv

ice

Tim

e C1

2

min

Serv

ice

Tim

e C1

3

min

Resp

onse

Tim

e C1

6

min

Employee Response Time (RT) vs. Service Time (ST)


7,147 chats 65,536 messages

37,174 customer messages 28,362 employee messages

Number of messages per chat (M= 12.3, SD=8.3)

Removed system messages Employees simultaneously serve

multiple customers

SYSTEM: Welcome to McCafee. How may I help you?Customer (13:37:46):Hi. I purchaed a disk and it doesnt work. Employee (13:41:33):Please let me know the locations you purchased the CD.Customer (13:41:46):I have been waiting for this information for 20 mins.Employee (13:42:00):You can contact the McAfee Sales team at +91 80 6656 9000 to renew the McAfee software.Customer (13:42:17):Come on.Customer (13:42:29):Don't pass the bill.Customer (13:42:46):Youre not answering my question.Customer (13:43:05):Can I chat with your supervisor?Employee (13:43:22):I apologize for the inconvenience.Customer (13:43:33):Can I chat with your supervisor?

Airline Data (December 2015)

Mere presence of customer POSITIVE emotion correlated with lower employee Response Time (RT)(30 seconds less for each employee message! (b= -29.57, SE=6.77, p0.05).

Results Chat Level Analysis


Empl

oyee

RT

(log1

p(se

c))

Log1p (Emotion intensity)

Positive Negative

Analyses control number of words per message (customer and employee), time waiting for service, and service/sales

HLM(chats within employee):n=7,147

R2 = 40.08%

b= -0.36 (SE=0.10, p

Empl

oyee

RT

(log1

p(se

c))

Log1p (Mean Number of customer words)

No Emotion Positive Negative

Analyses control number of words per message (customer and employee), time waiting for service and service/sales

Emotion Moderates Effect of Workload (#customer words) on Employee RT

HLM(chats within employee):n=7,147

R2 = 40.08%

b= -0.36 (SE=0.10, p

Airline data and analyses (20,355 chats from January 2016):Time based analysis (using T1 customer behavior to predictT2 employee behavior.

T1 T2

Study 2

Customer

Study 1

Employee

Problems and issues: What causes what?


1 2 3 4 5 6 7

1 2 3 4 5 6 7 8

Customer Emotion (Positive and Negative)Customer number of words

Customer Response Time.

Employee Service Time to focal customer

T1 T2

January 2016 Airline DataChats with at least 6 customer messagesN= 6,013 (from total of 20,355 chats)Random point in chat with at least 4 customer messages before and 2 employee messages after44 Employees

Control Variables

Control Variables

Rand

om b

reak

poin

t

Study 2

Employee

Customer

Herzig, J., et al (2016). Predicting Customer Satisfaction Using Affective Features. UMAP

Customer T1 positive emotion REDUCES employee T2 service time AND response time (b= -0.78, SE=0.20 ,p0.05).

Results

Study

Understanding EmployeeUNSCHEDULED Breaks

(up to 15 minutes)

Likelihood (1/0)

Length


Employee Status During the Day

0.0

2.0

4.0

6.0

8.0

10.0

07:30 08:30 09:30 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30

Time (1 min. resolution)

Aver

age

num

ber o

f cas

es

Total Break Online

Aver

age

num

ber o

f em

ploy

ees


First customer

assignment to employee

8:00 8:01 12:00 12:01 12:02 12:03 12:04 12:05 2:06 12:07 12:08 12:09 12:10 12:11 12:12 12:13 12:14

Customer 11 (C11)

Customer 12 (C12)

Customer 13 (C13)

C12 Assignment

to employee

C13 Assignment

to employee

C11Assignment

to employee

Beginningof shift

Customer Emotion and Employee UNSCHEDULED Breaks

Time interval

Two intervals:12 minutes (length of average chat, predicting likelihood of break); 30 minutes (prior to break taken, predicting length of break)

First customer


8:00 8:01 12:00 12:01 12:02 12:03 12:04 12:05 2:06 12:07 12:08 12:09 12:10 12:11 12:12 12:13 12:14

Customer 11 (C11)

Customer 12 (C12)

Customer 13 (C13)

C12 Assignment

to employee

C13 Assignment

to employee

C11Assignment

to employee

Beginningof shift

Start of break

Employee activated a

break

Workload

# chats handled# words employee wrote# words employee readMean # concurrent chats Cronbachs = 0.75/0.84

(Predicting likelihood and length)


First customer


8:00 8:01 12:00 12:01 12:02 12:03 12:04 12:05 2:06 12:07 12:08 12:09 12:10 12:11 12:12 12:13 12:14

Customer 11 (C11)

Customer 12 (C12)

Customer 13 (C13)

C12 Assignment

to employee

C13 Assignment

to employee

C11Assignment

to employee

Beginningof shift

Start of break

Employee activated a

break

Customer Positive Emotion

Customer Negative Emotion


Workload and Customer Positive Emotion Increase Likelihood Employee

Takes Break

Employee workload and customer positive emotion

increase likelihood employee will take a break

**p

Customer Positive Emotion REDUCES Length of Breaks

With higher customer positive emotions,

high workload leads to shorter breaks

Lengthof Employee

BreakWorkload

Customer

Leng

th o

f Bre

ak (s

econ

ds)

Workload

Low Medium High

*p

Customer Negative Emotion INCREASES Length of Breaks

*p

What Have We Learned?


Customer Positive Emotion: REDUCES employee Response Time; Stronger effects of LOW intensity then HIGH intensity REDUCES effects of work load on length of employee breaks;Customer Negative Emotion LOW Intensity - NO effects HIGH intensity - INCREASES employee Response Time; In General

REDUCES effects of workload on employee Response Time INCREASES effects of workload on length of unscheduled

breaks

Summary of Findings

Many open questions


Affect based routing could increase contact centers efficiency

Including emotion to employee tasks, and fair division of labor

New opportunities for research

Where are we going?


Daniel AltmanShelly AshtarGalia Bar

David SpivakMonika Westphal


Dr. Galit Yom-Tov

New Frontiers (and New Findings) on Emotion in Customer ServiceService Loaded with Emotions!Emotional Labor:Jobs Require Employees to Display Expected EmotionsSlide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Multiple Terms for Customer Negative EmotionSlide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Emotion in Chats(two weeks; airline sales and service)Emotion in Chats (11 weeks; telecommunication)One week data;14,700 conversations.Sample 10 time points in each; (messages at 0%, 11%, 22% ); calculate emotion at each point; Average 10 sample points across all company conversations;Emotion patterns of satisfied and dissatisfied customers; Tele-comm company chats;Data of one month26703 conversations with NPS = 109382 conversations with NPS = 0Employee Response Time (RT) vs. Service Time (ST)Airline Data (December 2015)Results Chat Level AnalysisValence of Customer Emotion Moderates Effects on Employee Response TimeEmotion Moderates Effect of Workload (#customer words) on Employee RTSlide Number 37Slide Number 38Slide Number 39Slide Number 40Slide Number 41Slide Number 42Slide Number 43Slide Number 44Slide Number 48Slide Number 49Slide Number 50Slide Number 52Slide Number 53Slide Number 54Slide Number 55Slide Number 56Slide Number 57

new frontiers (and new findings) on emotion in customer ... · angry – rude customer. polite...

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