Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an ‘‘open learning’’ model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.

1. ANALYSING CUSTOMER EXPERIENCE
FEEDBACK USING TEXT MINING: A
LINGUISTICS-BASED APPROACH
Car park and transfer case study at an airport
Francisco Villarroel
Dr Charalampos Theodoulidis
Dr Jamie Burton
Prof Thorsten Gruber
Dr Mohamed Zaki
21 March 2014

2. • Customer Feedback and Value co-creation
• (Text Mining and its applications)
• Car park and transfer case study at an airport
• Results
• Managerial Implications
• Further Research
Content

3. “Customer feedback process” plays a key role in ensuring that information
from complaints, compliments, market research and other sources are
systematically collected, analysed, and disseminated in ways that will drive
service improvements” (Lovelock and Wirtz, 2007).
Customer Feedback
“Service Quality (SQ) is a marketing stream that considers customer feedback
as an opportunity for assessing customer (di)satisfaction.
SQ can be “measured through the difference between customer
expectations and their real experience with the service” (Parasuraman et
al.1985).

4. Customer
Feedback
Process
Implicit
Determined Actions
(i.e. eye tracking, reading time,
number of scrolling, etc.)
Explicit
Platforms
(e.g. surveys, e-mails, online
review, blog, etc.)

5. • Many companies analyse explicit feedback using quantitative
methods because of simplicity in analysis
• Evaluating an entire service of quantitative measures will result in
an
• incomplete understanding of customer experience (Macdonald
et al. 2011; Vargo et al. 2007)
• only superficial information about the entire customer
experience (Caemmerer and Wilson 2010)
• not capture all the resources and activities involved (Gronroos
2012)
Customer Feedback Process

6. Compliments and Complaints NOTES
Compliments
• Affects positively front line employees
• Promotes positive WOM across
customers
• Provide information about core
competences of the company
Complaints
• Valuable information about what should
be improved.
• Delight in the case of good service
recovery.
• Maintain long term value from
dissatisfied customers.
• Represent areas that does not need
improvements.
• Lack of Originality in their content.
• Receive less attention from customer
managers.
• Can damage self confidence on front
line employees.
• Can generate negative WOM
Luthans, (2002); Kraft and Martin, (2001); Soderlund, (1998); Gruber, (2010); Chebat et al (2005); Buttle and Burton, (2002)

7. What’s Value co-creation?:
• It’s the process of interactions between
the customer and the company’s service
proposition.
• It’s a form of understanding the customer
and the firm as sum of resources
which constantly interact in order to generate
value (Vargo and Lush, 2004)
• These interactions occurs across a
service process, through different
activities which start from the contact of
the customer with the firm until the end of the
service (Payne et al 2008)
Co-creation
http://freshome.com/2008/08/05/ikea-catalog-2009-now-available-online-here
http://www.brsglobal.com/tag/ikea/

8. • Process of analyzing collections of textual materials in order to
capture key concepts & themes & uncover hidden trends.
• 80% of firms information is stored in text format.(Ur-Rahman and
Harding 2011)
• The approaches covered in literature:
• Linguistic approach: consider the natural language characteristics of the text in
the documents (e.g., syntax, grammar)
• Non-linguistic approach: view documents as a series of characters, words,
sentences, paragraphs. Counting the number of times specific words appear in a document
Text Mining

9. 1. Automate process of customer feedback analysis
through a text mining model.
Objectives
3. Evaluate the potential of customer compliments and
complaints for improving their service experience.
2. Determine what are the most important resources and
activities for the customer when using this service.

10. Research Process
1. Understand the customer
feedback process
2. Collect a sample of customer
compliments and complaints.
3. Development and test of a text
mining model
4. Present the results to the
participant company and evaluate

11. Customer Feedback Process
• Daily online survey is sent to customers who parked
their car 2 days before.
• In the Survey, open question asking:
“What is the single most important factor you
feel we can improve upon to enhance your car park
experience”
• The company receives approximately 1000 comments
per week, 50,000 responses annually on average

12. 1. Each comment is classified into just one category (despite
often including more than one compliment, complaint, or
suggestion)
1. Positive or negative sentiments are individual categories, with no
relationship to a specific element of the service
2. The classification of comments by means of manual annotation is
not consistent (approximately 2 weeks to generate a report)
Current Practice

13. Proposed Linguistic- based Text Mining
Sample 100 comments
Population 1092 comments

14. Sample Process
Pre-
processing
tasks
Library of
Concepts
Categorization
of Concepts
Pattern
Development
Model
Results and
Model
Refinement
Sample of 100 comments:
• Extract the sentences with more
valuable information for The M.A.
Comment
Car
Park
Ratin
g
Single improvement factor
Barrier did not recognise
my pre-booked credit card -
had to press buzzer but
person very helpful. Bus
going out was fine - after
waiting 15mins for bus on
return we walked - very
poor
E 5
Barrier did not recognise my pre-
booked credit card
press buzzer but person very
helpful
bus going out was fine
waiting 15mins for bus
very poor

15. Text Mining Process
Pre-
processing
tasks
Library of
Concepts
Categorization
of Concepts
Pattern
Development
Model
Results and
Model
Refinement
Sample 100 comments:
• Extraction of the main concepts by sentence
• Categorization of the concepts into 4 main
Groups: Resource Company, Resource
Customer, Activities, and Attributes
Resource
company
Resource
Customer
Activity 1 Activity 2 Opinion C & C
Barrier credit card did not recognise Pre-booked Complaint
Buzzer/
Person
press very helpful Compliment
bus going out fine Compliment
bus 15 mins waiting Complaint
very poor Complaint

16. Text Mining Process
Pre-
processing
tasks
Library of
Concepts
Categorization
of Concepts
Pattern
Development
Model
Results and
Model
Refinement
Sample 100 comments:
• Extraction of the most common sentences
patterns for compliments and complaints
• Barrier did not recognize my pre-booked credit card
• …had to press buzzer but person very helpful
• Waiting 15 mins for bus
CR Act CuR
CR CR ATP
CRAct CuR
“Complain about entrance”
“Compliment staff”
“Bus Complaint”

17. Car park-transfer service process
Based on Payne et al (2008); Vargo and Lush (2004)

18. Text Mining Process
Pre-
processing
tasks
Library of
Concepts
Categorization
of Concepts
Pattern
Development
Model
Evaluation
and
Refinement
• The model has in total :
• 694 patterns arose from these
comments
• 47 Subcategories of parking and
transfer service process
• 678 concepts mapped to these
subcategories
• 92% overall accuracy

19. Right Predictions
86%
14%
Compliments vs. Complaints
Complaints Compliments Implications:
• Considering that the questions was
asking about suggestions or complaints
it was interesting to find complimenting
customers.

20. Complaints
Most of the complaints were
when the customers were inside
the Car Park trying to park their
car.
87
54
298
111
Booking Arriving Car
Park
Parking Car Bus Service
Complaints through the Service

21. Compliments
8%
22%
70%
Compliments
Bus Service Staff GeneralIn the case of Bus Service
Most of Compliments were
Related with the bus driver
Helpfulness and Friendliness
of staff was found valuable for
Customers
For general compliments it would
be possible to sub-divide into new
categories

22. Overall Results
Service Process Right Predictions WrongPredictions Total Accurancy
Booking 87 3 90 97%
-Bookinggeneral 23 1 24 96%
-Price 64 2 66 97%
Arriving Car Park 54 1 55 98%
Parking Car 298 38 336 89%
-Space 71 3 74 96%
-Staff 25 4 29 86%
-Facilities 9 0 9 100%
-Directions 111 3 114 97%
-Others Car Park 65 12 77 84%
-Others Customer Resources 17 16 33 52%
Bus Service 111 3 114 97%
TOTAL 550 45 595 92%
Complaints
Service Process Right Predictions WrongPredictions Total Accurancy
General 62 7 69 90%
Bus 7 1 8 88%
Staff 20 2 22 91%
TOTAL 89 10 99 90%
Compliments

23. • Use of this model helps close the gaps in the service process
from a customer-centric perspective.
• Concepts such as service blueprinting might be updated
and improved through text mining.
• Addressing gaps in customer-centric service blueprint
could enable organizations to modify service offerings
• How changes in service offerings affect service encounters
• How activities and resources are affected.
• The importance of development of text mining patterns could
aid in developing better predictive models
Implications

24. • The proposed text mining model is domain specific
• The proposed model requires work to improve data capture and
accuracy and to be tested for another dataset.
• However, the approach could be tested and adapted to other
domains.
• Further research could investigate how information gathers from text
mining can be integrated in company information systems
Limitations and Further Research