How can we measure the way companies apologize in Twitter? What can these measurements tell us and why is this important? Illustrated with examples from HSBC and BT Care.

1. Measuring customer care talk in
Twitter
Dr Ruth Page, Professor Jeremy Levesley
University of Leicester
rep22@le.ac.uk, @ruthtweetpage
jl1@le.ac.uk
www.le.ac.uk

2. Overview
• Linguistic methods for quantitative analysis
– Semantic Differential
– Corpus linguistics
– Discourse Analysis
• Why might this be useful?
– Identifying distinctive patterns in communication
– Customer care training
• How metrics can be turned into indices

3. Data sets
Page data
• Data – 177,735 tweets
• 100 publically available
accounts
– 40 companies
– 30 celebrities
– 30 ‘ordinary’ accounts
• Gathered in 2010 and 2012
– Hashtags (Page 2012)
– Apologies (Page 2014)
Precise data
• BT Care
– 4014 tweets
– 69,976 words
• HSBC UK Help
– 3882 tweets
– 78,375 words

4. Methods: Scraping
• Data capture (Page)
– Bespoke python code that worked with the
Twitter API to scrape all public posts from named
accounts
– Automatically sorted tweets
• Updates
• Addressed messages (starting with @username)
• Retweets
• Converted files to plain text

5. Methods: Sampling
• travel:
– @bluejet, @luxorlv, @southwestair, @british_airways, @londonmidland,
@connectbyhertz, @carnivalcruise
• entertainment:
– @directv, @marvel, @travelchannel, @tvguide
• food:
– @sainsburys, @waitrose, @tastidlite, @popeyeschicken, @starbucks,
@dunkindonuts, @wholefoods, @uktesco, @dunkindonuts
• technology:
– @emccorp, @itunesmusic, @dellcares, @costcomcares
• finance:
– @hoover, @hrblock, @zappos, @wachovia, @intuit
• sport:
– @chargers, @chicagobulls
• retail:
– @selfridges, @americanapparel, @karenmillen, @reiss, @marksandspencer,
@rubbermaid, @johnlewisretail.

6. Frequency distribution
Cumulative
frequency
Frequency
End-to-begin
cumulative
frequency

7. Frequency distribution

8. Semantic differential
• Semantic differential consists in three value:
– Evaluation (Good – Bad)
– Activity (Active – Passive)
– Potency (Strong – Weak)
• For each word from the 1,000 most frequently
used words these three values are measured.
• Each value belongs to interval [-4.6, 4.6]

9. Visualization
• Right figure depicts the
distribution of tweets in the
space of the first three
principal components
calculated for the first 150
words
• We can see dense cone and
small cluster outside the
cone

10. Visualization
• We calculate two subsets of the first 150
words which realize an 80% covering of
tweets.
• We calculate three subsets of the first 150
words which realize a 70% covering of tweets.

11. Visualization
• Right figures depict the
distribution of tweets in
the space of the first
three principal
components calculated
for the first 80% covering
• We can see three clusters

12. layered structure
• Right figures depict the
distribution of tweets in
the space of the first
three principal
components calculated
for the first 70% covering
• We can see Layered
structure

13. Visualization
• Right figures depict the
distribution of tweets in the
space of the first three
principal components
calculated for the second
70% covering
• We can see layered structure

14. Question 1
• What kinds of messages do different groups of
Twitter members post to their accounts?
• Methods
– Quantifying the number of each type of post

15. INSIGHT: Distribution of tweet types (2010)
All groups favoured updates, with celebrities most of all
Twitter is an environment for ‘broadcasting’ one-to-many messages
‘Conversational’ one-to-one messages were less

16. INSIGHT: Distribution of tweet types (2012)
Corporate tweeting behaviour changed and becomes more
‘conversational’
What’s distinctive about the corporate addressed messages?

17. Using Corpus-based methods
• Corpus – a definition
– Collection of representative texts
– Machine readable form
• Concordancing tools
– Antconc (Laurence 2014) - Freeware
– Wordsmith Tools, Wmatrix – Proprietary
• Search and sort lexical strings
• Compare with other corpora

18. Corpus linguistics: Basic steps
Start by examining….
1.Frequency of words
2.Keywords in Context
(KWIC)
3.Collocations
Clusters of words that
repeatedly occur together

19. Is the frequency pattern specific to
this dataset?
Keyness list
• ‘Keyness’
– Statistical over-use of words
– I compared the corporate
addressed messages with all
tweets in my dataset
• INSIGHT:
– The items in the keyness list
cluster together and are
typically found in apologies

20. HSBC, BT Care and ‘Page’ data
• INSIGHT
• HSBC UK Help and BT
Care apologise even
more than the
companies in my
dataset!

21. Why are apologies so important?
• Twitter is a public
environment where
customers can complain
• Damage to the
company’s reputation
• Apologies need to
rebuild reputation and
re-establish rapport
between company and
customer

22. Example: KWIC list for HSBC’s sorry

23. Methods: Discourse Analysis
• Manually extracted all
examples of apologies
from data (c.1200 egs)
• Coded manually in Excel
• Features identified by
other researchers
interested in apologies
• Other communicative
features
• Formulae which indicate the apology
• Problem restated in the apology
• Explanation or account
• Offer of repair
• Greeting
• Naming
• Additional questions or instructions
• Emoticons and conversational
features (discourse markers)

24. Do companies repeat the
problem or not?
Companies tend to avoid repeating the
problem in their apology.
This enables them to preserve their
reputation, but it can appear
impersonal.
BT Care typically uses ‘vague language’
to avoid restating the problem
HSBC UK Help typically restates the
problem

25. Do companies explain
why the problem
occurred?
Companies do not often explain why a
problem occurred. But when they do,
it typically downplays their role in the
offence that prompted the complaint.
The effect can be to mitigate damage
to reputation

26. Do companies make an
‘offer of repair’?
Offering recompense to the customer
can be a way to rebuild reputation and
re-establish rapport with the
customer.
It’s not always appropriate though,
and depends on the company in
question.

27. Personalising the message
• Signatures
– 37% of apologies by
companies
– 0 of apologies by
ordinary accounts
– 100% by HSBC Help UK
– 0 by BT Care
• Name of customer
– 19% of apologies by
companies
– 11% of apologies by
ordinary accounts
– 69% by HSCB Help UK
– 0 by BT Care

28. ‘Rapport’ talk: Greetings
and Emoticons HSBC BT Care
Hi 2399 2815
Hello 230 14
Good afternoon 344 0
Good evening 306 0
Good morning 357 14
3636 2844
INSIGHT
The style of an apology can be more or
less formal. More conversational
features like greetings and signals of
emotional response like emoticons can
be used to project rapport with the
customer.
In my data, 19% of companies and
none of the ordinary accounts used
greetings. Six percent of the
companies and 25% of the ordinary
accounts used emoticons.
HSBC uses ‘rapport’ features more
often than BT Care (figures given per
million words)
HSBC BT Care
:) 2118 343
:( 77 29
;-) 89 0
:-) 115 57
2399 429

29. Does the customer need
to respond again?
Companies may not be able to
respond completely to the complaint
in Twitter. They may need more
information or ask a third party to
respond.
This is risky as it means that the
communication chain can break down
leading to greater customer
dissatisfaction.

30. Comparison of questions on the
HSBC Help and BT Care accounts
HSBC UK Help
• 31% of all company tweets
contained a punctuated
question
• 15% of the questions
checked if the customer had
been in touch
• 10% asked if further help
was needed
BT Care
• 44% of all company tweets
contained a punctuated
question
• No questions asked if the
customer had been in touch
or needed further help

31. Summary of BT Care and HSBC
HSBC UK Help
• Risk reputation by restating
the problem
• Build rapport by
– Personalising their tweets by
always signing off and
frequently use the customer’s
name
– Use greetings and emoticons
• Use follow up questions to
check customers’ needs
BT Care
• Protect reputation by rarely
restating the problem and
use explanations to defer
blame
• Limited rapport
– Rarely if ever sign off and
never use the customer’s
names
– Rarely use conversational
features
• Never use follow up
questions to close the
apology

32. Application for clients
• What makes an apology good PR?
– Different factors
– Not all linguistic (e.g. timeliness)
– Different people will value different aspects
• Successful customer care is not mechanistic
• But analysis can identify areas of need and
then training can be developed to improve
practice