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Procedia Economics and Finance 11 (2014) 289 – 305 Available online at www.sciencedirect.com 2212-5671 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies. doi:10.1016/S2212-5671(14)00197-X ScienceDirect Symbiosis Institute of Management Studies Annual Research Conference (SIMSARC13) Enhancing Customer Experience using Business Intelligence Tools with Specific Emphasis on the Indian DTH Industry Prof. Sujata Joshi a *, Arnab Majumdar b , Archit Malhotra c a Assistant Professor, Symbiosis Institute of Telecom Management, Pune bc Student, Symbiosis Institute of Telecom Management, Pune Abstract The Direct to Home industry has emerged as the key driver for the Indian entertainment industry. In October 2011 the Government announced implementation of a phase-wise digitization programme of pay TV services throughout the country. The Indian Direct to Home industry is expected to grow by 50% in 2016. A few challenges faced by the Direct to Home Industry are low Average Revenue per user (ARPU), high customer acquisition costs and high churn rate. DTH Service Providers consider superior service experience as the key differentiator that will help them acquire new customers and manage churn as well. Currently there is no standard available in the India market for DTH service providers to quantify and improve its customer experience. Hence the objective of this paper is to formulate various constructs that helps to understand the impact of different service attributes on customer experience for Direct to home customers using business intelligence tools. Keywords: Customer Experience, KANO’s quadrants, Hub & Spoke, Satisfaction Loyalty Grid., BI tools * Corresponding author. E-mail address:[email protected] © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.

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Page 1: designing Customer Experience

Procedia Economics and Finance 11 ( 2014 ) 289 – 305

Available online at www.sciencedirect.com

2212-5671 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.doi: 10.1016/S2212-5671(14)00197-X

ScienceDirect

Symbiosis Institute of Management Studies Annual Research Conference (SIMSARC13)

Enhancing Customer Experience using Business Intelligence Tools

with Specific Emphasis on the Indian DTH Industry Prof. Sujata Joshia *, Arnab Majumdarb, Archit Malhotrac

aAssistant Professor, Symbiosis Institute of Telecom Management, Pune bcStudent, Symbiosis Institute of Telecom Management, Pune

Abstract

The Direct to Home industry has emerged as the key driver for the Indian entertainment industry. In October 2011 the

Government announced implementation of a phase-wise digitization programme of pay TV services throughout the country. The

Indian Direct to Home industry is expected to grow by 50% in 2016. A few challenges faced by the Direct to Home Industry are

low Average Revenue per user (ARPU), high customer acquisition costs and high churn rate.

DTH Service Providers consider superior service experience as the key differentiator that will help them acquire new customers

and manage churn as well. Currently there is no standard available in the India market for DTH service providers to quantify and

improve its customer experience. Hence the objective of this paper is to formulate various constructs that helps to understand the

impact of different service attributes on customer experience for Direct to home customers using business intelligence tools.

© 2013 The Authors. Published by Elsevier B.V. Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.

Keywords: Customer Experience, KANO’s quadrants, Hub & Spoke, Satisfaction Loyalty Grid., BI tools

* Corresponding author.

E-mail address:[email protected]

© 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Selection and/or peer-review under responsibility of Symbiosis Institute of Management Studies.

Page 2: designing Customer Experience

290 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

1. Introduction

India is the third largest TV market after the US and China with 155 million subscriber households in 2012, TV

signals in India are currently distributed in analogue as well as in digital and terrestrial formats. Most cable

operators in the country are providing analogue TV service while all DTH operators are providing a digital TV

service. The government of India amended the Cable Television Networks (Regulation) Act in October 2011 to

announce implementation of a phase-wise digitization programme of pay TV services throughout the country and

hope for complete digitalization by end of 2014. The main reasons driving the growth of Direct to Home services

are the progress in technology, increased overall value proposition, and simplified yet enhanced consumer's

television viewing experience. Another important catalyst for growth of DTH is customer service since it is a key

differentiator between the DTH players and the unorganized service providers. A few challenges faced by the Direct

to Home Industry are low Average Revenue per user (ARPU), high customer acquisition costs and high churn rate.

DTH Service Providers consider superior service experience as the key differentiator that will help them acquire

new customers and manage churn as well. Currently the rate of churn for the DTH industry is 14-16%. Thus, it is

essential to develop effective methods to retain the existing customers which include two major steps. One is to

manage the customer experience and second is to generate service value for the customers. Customer experience is

defined as the sum of all experiences that a customer has at every touch-point of the customer-company relationship.

It is an intentional effort on the part of the company to develop and maintain good experience which is differentiated

from the competition, consistent at every touch point and most importantly valued by the customer. Currently there

is no standard available in the India market for DTH service providers to quantify and improve its customer

experience. Hence the objective of this paper is to formulate various constructs that help to understand the impact of

different service attributes on customer experience for Direct to home customers using business intelligence tools.

This solution can be used to enhance the quality of service, deliver better service experience to the customers. This

in turn will influence customers’ loyalty for their brands as well as have a positive impact on their intent to spend

and recommend the brand.

2. Methodology

Exploratory interviews were conducted on 440 customers in order to define the important attributes that affect the

customer experience for Direct to Home Service customers.

Statistical tests like crosstabs, correlations, binary logistic regression and cluster analysis were performed to help

carry out the analysis. This analysis helped to model the KANO’s quadrants, Hub & Spoke. A descriptive analysis

on the intent to spend, recommend and overall experience was carried out to compute a loyalty score for the

customers.

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291 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

3. Contribution to the body of knowledge

The research adds to the body of knowledge by analysing the constructs that help understand churn, complaint

handling, & customer behavioural patterns for DTH services. It also gives insights on how better customer

experience impacts customer behavioural patterns: intent to spend, recommend the brand, analyse churn behaviour

and increase customer loyalty.

4. Literature Review

Experience has been increasingly discussed since the beginning of 2000 (Caru and Cova 2007), but it is rarely

defined. Sundbo and Hagedorn- Rasmussens (2008) defined Customer Experience as the customer’s direct and

indirect experience of the service process, the organization, and the facilities and how the customer interacts with

the service firm’s representatives and other customers. According to them, Customer experience is one of the major

factors influencing the consumer’s process for purchase decision. In the opinion of Davidson (1992) customer

experience is a method of creating a differential advantage for establishing customer’s loyalty.

Barlow and Maul (2000) have suggested that as per the experience economy philosophy, customers expect a

positive, emotional and memorable experience at every touch-point or transaction with an organization. The

customer experience economy as per O’Sullivan and Spangler (1998, p. 326) refers to individuals or firms whose

main purpose is to create such an experience that it will result in differential advantage and benefit for the

customers. According to Hongxiang (2011) quality of experience is one of the most important factors that will result

in satisfaction of the customers. Statistics has proven that about 82% of the users churn to another network because

they are not satisfied with the service quality given by the service provider, and 90% of them leave without

complaining to the customer care department. At the same time, one customer can convey the dissatisfaction to 13

other customers, which may have a bad effect on the service provider’s brand.

The research done by Belk et al.’s (1989) stresses on the importance of understanding the factors affecting

experience of the modern consumer. Similarly, Thompson et al. (1990) have also reiterated the fact that researchers

should do a study on customer experiences. Michela Addis (2005) have defined consumption as a result of the

experience which the customer gets after having a series of interactions of the product or service provided by the

organization. Van Der Wagen (1994) and Katz (1968) are of the opinion that different customers may have different

perception of experiences for a given product or service because each customer is unique in his understanding and

analysis given the fact that each one of them comes from a different educational, cultural background. Blythe (1997)

reiterates a similar opinion by saying that consumers analyze purchase decisions as a result of their previous positive

experiences.

From the literature review as stated above, customer experience has been studied across various sectors but very few

studies have been done with respect to customer experience in the Direct to Home services especially in India as this

sector is in the nascent stage.

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292 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

Thus the present study adds to the body of knowledge by analysing the constructs that help understand churn,

complaint handling, & customer behavioural patterns for DTH services. The research helps to analyse service

attributes that enhance customer experience for DTH services. It also gives insights on how better experience

impacts customer behavioural patterns: intent to spend, recommend the brand and analyze churn behaviour. Finally

it was observed from the research that vulnerable customers do become loyal after receiving better service

experience.

5. Conceptual framework and Analytical Constructs:

Three major analytical constructs were considered for the purpose of this study, the Kano’s model, Hub & Spoke

Analysis, Satisfaction-Loyalty Grid:

The analytical constructs i.e. KANO’s model & Satisfaction Loyalty Grid has been implemented with reference to

an article, “Adding Value to CSM: The Kano Model” by Michael Lieberman for the World Advertising Research

Centre.

They are explained as below.

5.1. KANO’s model implementation:

Kano analysis is, in principle, a measure of the importance of the features to the customer, and the performance

of the business. The KANO’s Grid is formulated taking into consideration the stated importance of service

attribute as responded by the customer & the derived importance that is the correlation between overall

experience of the customer & the service statements.

It defines the service parameters in the following way:-

Must be’s:- The service attributes without which the experience is incomplete

Satisfiers: - The attributes of high stated importance formulating the key drivers of service experience.

Indifference: - The attributes of least importance to customer can form minor areas of opportunity

Exciters: - The unexpected parameters which invariably increase the service experience

5.2. Hub & Spoke Analysis:

This area of analysis helps to quantify the customer behavioural patterns with respect to a unit increase in the

customer experience index score. The statistical technique used is Binary Logistic regression which indicates

the exponential increase or decrease in the key business drivers.

5.3. Formulating the Satisfaction-Loyalty Grid:

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293 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

e The grid segregates the customers into four major segments based on Individual satisfaction levels i.e. the

measure of customer experience index & Loyalty score i.e. defined as the average score of overall experience,

intent to recommend & intent to purchase more. It demarcates the following customer segments:-

• Champions: - Highly satisfied & loyal customers of the service provider

• nSwitchers: - Customers having high satisfaction scores but are equally likely to churn

• Captives: - Customers are loyal to the have low satisfaction levels

• Antagonists: - This segment probably has had bad experience with the DTH provider

The shift in customer behavioural pattern i.e. a shift towards brand loyalty is observed by carrying out Cluster

analysis. The predictors to observe customer movement were customer experience score, overall experience,

monthly spend, recharges per year & people referred.

Figure 1: Satisfaction-Loyalty Grid

6. Formation of Hypothesis

The review of literature gave deeper insights into the attributes or parameters affecting customer experience for

DTH services. For hypotheses formulation in this study researcher has considered:

• H1 = Parameters like Advertisements, regular payment alerts, trustworthiness, variety of channels packages

have different levels of impact on the overall customer experience

• H2 = Better customer experience impacts customer behavioural patterns

• H3 = Customer Experience drives customer loyalty

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294 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

7. Research Methodology:

7.1. Research Design

This study aims to build an outline to enhance customer experience for the DTH service providers. The study takes

into account certain analytical constructs, implemented with the help of business intelligence tools, SPSS &

Microsoft Excel.

Figure 2: (Hypothesis: Input-Statistical Constructs; Output-Enhancement of CE)

7.2. Primary research and sample size

Primary research was conducted on a sample size of 440 DTH customers in City of Pune, Maharashtra. The

respondents were administered a structured questionnaire.

The responses were recorded using a set of 25 service attributes measured on an importance & performance scale in

addition to other relevant information required for segmentation analysis. These attributes considered were extracted

from literature review and with specific reference to DTH customer life cycle. The importance measure is the degree

to which a service attribute is essentially critical to the customer. The performance measure indicated the experience

the customer receives at service delivery by the DTH provider.

For the primary data collection, a questionnaire was formed consisting of two sections

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295 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

• Section A: consisted of questions related to service attributes. The importance and performance of these

attributes were determined on a scale of 1-7. An additional question was asked to be able to quantify the

overall experience of the customer on the same scale.

• Section B: consisted of questions used for the segmentation analysis of the customers. Data on their

monthly spend, intent to spend, intent to recommend and intent to churn was captured. Additional

information related to the use of additional services and interactive services were also captured.

Figure 3: Research Steps

8. Statistical Analysis

Descriptive, Predictive and Prescriptive analysis of the data obtained was carried out. Statistical analysis tool SPSS

was used to help construct the above mentioned objectives. Several statistical tests like crosstabs, correlations,

binary logistic regression and cluster analysis were performed to help carry out the analysis. This analysis helped to

model the KANO’s quadrants, Hub & Spoke. A descriptive analysis on the intent to spend, recommend and overall

experience was carried out to compute a loyalty score for the customers. These customers were then segmented in

the satisfaction loyalty grid to understand their behavioural patterns.

e The objective with which the research analysis was carried out was to enhance the DTH customer experience. The

following opinions were kept into picture:

1) t Classify service attributes & determine what it takes to positively impact customer satisfaction. The construct

used was KANO’s model.

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296 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

2) Understand the impact of a better experience on customer behavioural patterns. The construct used was Hub &

Spoke analysis.

3) Observe customer behaviour: A shift towards brand loyalty. The construct used was formulating a Satisfaction

Loyalty Grid.

The analytical tools and techniques involved, give various predictive measures to formulate business strategies &

& decisions such as improving customer delight, enriching the customer experience most of all retaining customers &

improving loyalty & customer base.

Note: -The analytical constructs i.e. KANO’s model & Satisfaction Loyalty Grid has been implemented with

reference to an article, “Adding Value to CSM: The Kano Model” by Michael Lieberman for the World Advertising

Research Centre.

Bad Experience18

19%

For BetterServices/Offers

4546%

Price22

23%

Moving to OtherLocation

55%

Others6

6%

Blanks1

1%

REASONS TO CHANGE

Figure 5: % & No. of people planning to switch

Yes9799999997979797979797

22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%22%

Noo34378%

PLANNING TO CHANGE

Figure 4: Reasons to switch (% & No.)

Page 9: designing Customer Experience

297 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

OUTCOME OF ANALYSIS: Part 1

The analytical constructs help understand churn, complaint handling, & customer behavioural patterns

Analysis related to churn:Analysis related to churn:

After carrying out the sample survey, the first objective was to observe the sample behaviour. The major focus was

to observe the churn behaviour. This study shows that 22% of the sample wants to churn from their current service

pprovider. The top reasons were: to receive better service and pricing issues.

Analysis related to complaint handling:Analysis related to complaint handling:

In this study complaint analysis was carried out. fFrom the sample of 440 customers a big sample of population of

approx. 260 customers were facing issues in these areas. The graph indicated top six reasons for complaints. It was

observed that maximum customers had issues regarding signal, the second most important complaint was related to

bbilling followed by and service activation/deactivation, relocation on service issues and then payment related issues.

Complaints and churn go hand in hand.

y Customer dissatisfaction leading to churn has become a very major issue for the DTH service providers. The study

uses business intelligence to work out on these loop-holes, chalk out on strategies and identify the potential of the

market.

Figure 6: Major areas of complaints

SignalIssues

Billing/Rechar

geIssues

Service Activati

on/Deactivation …

Product/Offers Issue

Relocation

ServiceIssues

Payment

Issues

Series1 111 39 34 28 24 2339 34 28 24

111

3939 3434 2828 24 23

020406080

100120

Cust

omer

s

ISSUE OF COMPLAINTS

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298 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

Analysis related to increase customer spending and impact on DTH services:Analysis related to increase customer spending and impact on DTH services:

The research shows the impact of customer experience on reducing churn and influencing customers to increase

spending. It also builds up customer loyalty. It has been observed in the sample behaviour that customers in the

monthly income group of 50K-80K have received the highest experience and thus having the highest intent to spend.

Figure 7: CEI v/s Customer Behaviour; Segment: Various Income Groups

The dive into the study also provides deeper insights on exploring the cross-sell and up-sell opportunities. There is a

r huge demand for internet in TV. Other observations show an immense potential for the digital video recorder

market. 20.7% of the customers use interactive DTH services. This is a huge business opportunity.

Figure 8: % of sample using add-on services Figure 9: % of sample using additional services

This study thus provides a solution not only to manage customer’s trust for their brand, but also provide insights on

Less than Rs.

3000

Rs. 3000-6000

Rs. 6000-15000

Rs. 15K-30K

Rs. 30K-50K

Rs. 50K-80K

More than Rs. 80000

CEI 7.13 7.03 7.36 7.17 7.08 7.71 7.497.03 7.36 7.17 7.08 7.71

Average CEI 7.35 7.35 7.35 7.35 7.35 7.35 7.35

Ready to Spend More% 59.38% 59.09% 59.09% 35.00% 43.75% 75.25% 61.54%59.09% 59.09% 35.00% 43.75% 75.25%

Planning to Change% 12.50% 27.27% 18.18% 23.33% 23.96% 18.81% 25.00%

59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59 38%59.38%59.38%59.38%59.38%59.38%59.38%59.38% 59.09% 59.09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09%59 09% 559 09% 559 09% 559.09% 559.09% 559.09% 559.09% 559.09% 5 %59.09% 5 %59.09% 5 %

5.00%0%0%0%0%0%0%0%0%0%0%0%0%35 00%35 00%35 00%35 00%35 00%35 00%35 00%35 00%35 00%35 00%35 00%35 00%%35 00%%35 00%%35 0035 0035 0035 0035 035.035.035.035.035.353543.75%43.75%75%75%75%75%75%75%75%75%75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%43 75%4 %4 %%%%%%%%

75.25%7777777777575757575757575.75.75.75.275.261.54%61 54616666666161616161616161616161616161616161

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0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%

6.606.807.007.207.407.607.80

% o

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ACEI

Sco

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Income v/s Behaviour

Internet, 3III tI tI tI tInterInterInterInteInteInteInteInteInteInteInteInteInteInteInteInteInteInteInteInte2.05%222222E-EEEEEEEEEE-----

Commercrcccccccce, 7.27%

Social Networkinnnnnnnnnnnnnnnnnninining, 11.82%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%2%%

Telemarketing, 2.27

%

Interactive

Games, 6.59%

ADDITIONAL SERVICESI ii Parental Lock, 7.

73% Video On

DemandDDDDDDDDDeDeDeDeDeDeDeDeDeDeDeDeDeDeDeDeDeDeDeDe, 9.55%999999999999999999999, 9, 9, 9, 9, 9, 9, 9, 9,,,,,

Active Movies, 12.95%

Active iA iA iA tiA tiA tiA tiActi eActiveActiveActiveActivectivectivectivetivetivetiveiveveveeContent, 1.14%

3D Channels, 4.09%

DVR, 19.09%

ADD-ON SERVICES

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299 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

which other opportunities to explore and enhance upon the quality of service.

Part 2: OUTCOME OF STATISTICAL ANALYSIS (Part 2)

1) KANO’s Analysis

The distribution of various parameters into various segments of the grid is arrived at by creating a plot between (X-

axis as Stated Importance; Y-axis as Derived Importance). The following table indicates the Stated Importance &

the Derived Importance values. The derived importance is arrived at by performing a cross-tab correlation between

the overall experiences of the customer with the 25 service attributes considered.

Parameters Stated Importance Derived ImportanceAdvertisements 0.71 0.15 Promotions 0.74 0.22 Plans & offers 0.81 0.20 Pricing is value 0.81 0.27 Trustworthy operator 0.81 0.31 Variety of Channel Packages f 0.83 0.01 I feel valued customer 0.74 0.25 First time installation 0.79 0.12 Activation of first time services 0.82 0.20 Behaviour of installation staff 0.80 0.23 Picture & Audio Quality 0.84 0.31 Continued Service in heavy rains 0.79 0.26 User Friendly equipment 0.79 0.23 Experience of change in service 0.77 0.22 Experience in Relocation of services 0.77 0.17 Dealers are widely spread 0.78 0.31 Experience at Outlets 0.74 0.20 Interactive website 0.77 0.18 Customer Helpline easy Access 0.79 0.15 Helpline staff are polite & courteous 0.81 0.26 Queries solved efficiently & quickly 0.80 0.21 Maintenance & Repair Options 0.81 0.27 Accurate (Fair) Charge deduction 0.81 0.22 Regular payment alerts 0.78 0.27 Recharge Flexibility 0.82 0.30

Table 1: Service Parameters with corresponding Values of Stated & Derived Importance

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Figure 10: Kano's Grid; Ref: Paper by Michael Lieberman

9. Hypothesis Testing & Inferences

H1 = Parameters like Advertisements, regular payment alerts, trustworthiness, variety of channels packages have

different levels of impact on the overall customer experience

The hypothesis H1 is thus evidenced by the following findings indicating the impact of service level attributes on

customer experience:

•• The Performance Attributes i.e. trustworthy operator, picture & audio quality, recharge flexibility,trustworthy operator, picture & audio quality, recharge flexibility,

bbehaviour of customer care staff has a concentrated impact on customer experience & business

performance.performance.

•• Parameters like promotions; value to the customers &regular payment alerts provide additional satisfaction

& add to the surprise factor to the customers & make the customers more loyal to the brand.

•• The Must Be Attributes, like Accurate billing, plans and offers, channel packages are the compulsory

requirements of customers. They must provide a higher level of experience or else would result into

dissatisfaction & thus churn in the system.

•• Interactive website, Relocation of service, Advertisements may have an impact on decision making but do

not add up the experience.

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301 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

Managerial Implications:

Customer experience can be enhanced by winning customer trust & providing value for services. 2) Hub & Spoke Analysis

This analysis helps to study the impact of enhancing the customer experience by 1 unit onto the key business

drivers. The statistical technique, Binary Logistic Regression, is used to evaluate relationships between a

dichotomous dependent variable and metric or dichotomous independent variables. It is used to estimate the

probability that an event, forming part of the defined groups, will occur in accordance to the change in the

independent variable.

Hence the following table depicts the incidence of change in the behavioural patterns by enriching the customer

experience by 1 unit. The positive sign indicates an increase in intention to recommend, purchase & spend, while the

negative sign indicates a decrease in intent to churn & reduced complaints.

Hypothesis Testing &Inferences:

H2 = Better customer experience impacts customer behavioural patterns

The hypothesis H2 is ascertained by following insights

The key business drivers as depicted are positively impacted if the customer experience is increased by a unit level.

Areas of opportunity, positive word of mouth, saving costs are some factors that every service provider looks for

today. This analysis gives a clear insight on the changes in customer behavioural patterns & quantifies the same.

Table 2: Business driver with Statistic Value exp(B)

Business Driver Impact: Value of exp(B) Intent to Recommend +1.050 times Intent to Purchase +1.217 times Intent to Change operator -1.023 times Intent to Send More +1.254 times No. of Complaints raised -0.068 times

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Figure 11: Hub & Spoke (Behaviour & Corresponding Changes)

Managerial Implications: Enhancement in customer experience will result into following:-

Unit Increase in CEI Effects on business

Table 3: Business effect of Enhancement in CE

3) Satisfaction-Loyalty Grid

The grid divides the customers into various segments & is arrived at by a plot between (X-axis being loyalty score &

Y-axis being the individual experience index scores). The Loyalty score is the simple average of responses to three

major questions, the overall experience, the intention to recommend, & the intent to purchase more. The satisfaction

scores are arrived at by applying the weighted average technique on the importance & performance scores obtained

on the 25 service statements.

Chances of RecommendationLess Customer ChurnSpend More for better servicesLess complaints raisedPurchase more services

Increase in Positive PublicityHigher Satisfaction ScoresMore Revenue OpportunitiesLess Customer Care CostMore Cross-Sale; Up-Sale

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Hypothesis Testing &Inferences:

H3 = Customer Experience drives customer loyalty

The need of the hour is customer segmentation, so that strategies can be focused on enhancing customer experience.

This analysis gives an insight on similar lines, Champions being the most loyal & most satisfied segment is the

desired category. 42.05% of the surveyed sample is highly satisfied and loyal. 35.23% of the customers are

vulnerable. After running through Cluster analysis it was observed in the satisfaction loyalty grid that the size of

champions category increased by 2.75%.A shift in customer behavioural pattern has been observed. On receiving

better experience customers migrate from the vulnerable category to the loyal and satisfied category.

Managerial Implications:

The vulnerable segment i.e. approx. 9% (Switchers)& 26% (Antagonists).Needs to be catered so as to increase their

experience levels &win their loyalty to make them more valuable to the business, increase the customer base, spread

a positive word of mouth, & in turn enhance business performance. The intent is to move the captives to the

champion’s category & win the confidence of antagonists.

Antagonists11326%

CaptivesCCC tC tCCCapCapCapCapCapCapCapCapCapCapCapCapCapCapCapCaCaCaCaCaCaCa10023%

Switchersi hS i hS i hi hit hit hit hit hitcherswitcherswitcherswitcherswitcherswitchersitchersitchersitcherstcherstcherstcherstcherscherscherscherscherschershershershershers429%

Champions18542%

CUSTOMER CATEGORIES

Figure 12: Customer segments; Ref: Paper by Michael Lieberman)

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10. Limitations of the Research:

•• Sample size -- The number of the units of analysis was small. Open to more advanced & wider data

collection.

•• Availability of data -- Reliable & sufficient data from the DTH service providers will give a more clear

understanding of the trends in the services & experiences.

•• Limited Demographics -- The study was limited to students, professional & employees, only in Pune area.

An increase in sample size & varied demographics would provide a more heterogeneous sample.

•• Rural coverage -- The rural areas have not been covered & would formulate a wider scope for future

research.

11. Findings and Conclusions:

The research findings can be used by Direct to Home service providers while formulating their churn management

strategy, customer experience strategy, while formulating their marketing strategy as well as their customer retention

strategy. The findings of this research convey a strong message to Direct to Home service providers that

Customer experience can be enhanced by winning customer trust & providing value for services. Performance

attributes like. Trustworthy operator, picture & audio quality, recharge flexibility, behaviour of customer care staff

has a concentrated impact on customer experience & business performance. Parameters like promotions; value to the

customers & regular payment alerts provide additional satisfaction & add to the surprise factor, and make the

customers more loyal to the brand. The Must Be Attributes, like Accurate billing, plans and offers, channel packages

are the compulsory requirements of customers. They must provide a higher level of experience or else would result

into dissatisfaction & thus churn in the system. Interactive website, Relocation of service, Advertisements may have

an impact on decision making but do not add up the experience.

The research findings also show theta unit increase in customer experience will lead to 1.05 times increase in the

intent to spend, 1.21 times increase in the intent to purchase, and 1.25 times increase the intent to spend more.

This will help the managers to enhance their business as the chances of recommendation will increase leading to

increase in positive publicity. Secondly, Customer churn will be reduced leading to higher satisfaction scores

leading to customer retention. Thirdly, increase in customer spending will lead to more revenue opportunities. Also

as customers are willing to spend more, chances of cross-selling and up-selling will be more. Lastly, lesser number

of complaints will lead to lesser spend on customer care cost.

The research findings based on the satisfaction loyalty grid show that on receiving better experience customers

migrate from the vulnerable category to the loyal and satisfied category. This conveys a strong message to the

managers that the vulnerable segment i.e. approx. 9% (Switchers) & 26% (Antagonists) needs to be catered so as to

increase their experience levels & win their loyalty to make them more valuable to the business, increase the

customer base, spread a positive word of mouth, & in turn enhance business performance. The intent is to move the

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305 Sujata Joshi et al. / Procedia Economics and Finance 11 ( 2014 ) 289 – 305

captives to the champion’s category & win the confidence of antagonists.

12. Scope for Future Research:

• The survey & analysis can be completed on a larger scale to include more customer & industry insights.

• This tested study can further be applied to other telecom verticals such as broadband & mobile.

• Can be used as a consulting solution for service providers.

References

* MARC Group report entitled "Smart Card Industry in India: SIM, Identity, Banking, Transport, Healthcare, Pay TV, Loyalty & PDS"

* Sales and Marketing, “ Going Digital: Key DTH and IPTV Offerings in the market”, May27, 2013, p 1

* TRAI consultation paper on, “ Bharti Airtel Response to TRAI’s draft tariff order prescribing standard tariff package(STP) for Set Top Boxes

(STB’s), page 4.

Bairsto, A. (2009) ‘Customer retention and churn management ’, Chorleywood Consulting, ISBN-10 1903950252; ISBN-13 978 1903950258

Barlow, J., & Maul, D. (2000).Emotional Value–Creating Strong Bonds with Your Customers. San Francisco,USA: Berrett-Koehler Publishers.

Belk, R. W., Wallendorf, M., & Sherry, J. F. (1989). The Sacred and the Profane in Consumer Behavior: Theodicy on the Odyssey. Journal of

Consumer Research, 16(1), 1-37.

Blythe, J. (1997). The Essence of Consumer Behaviour. London: Prentice-Hall.

Caru , A., Cova, B., 2007. Consuming Experience, Routledge, London

Davidson, R. (1992). Tourism in Europe. London: Pitman Publishing.

Hongxiang.T. (2011).Volumetric Heat Capacity Enhancement in Ultrathin Fluorocarbon Polymers for Capacitive Thermal Management.

Submitted in partial fullfillment of the requirements for the degree of Master of Science in Mechanical Engineering in the Graduate College

of the University of Illinois at Urbana-Champaign

JeongyunHeo, Sanhyun Park, and Chiwon Song, (2007), “A study on the Improving Product Usability Applying the Kano’s Model of Customer

Satisfaction.” J. Jacko (Ed), Human Computer Interaction, Part 1, HCII 2007, LNCS 4550, pp 482-489, 2007

Journal of Service Management, Volume 23, Issue 5 (2012-09-29)

Katz, E. (1968). On reopening the question of selectivity in exposure to mass communications.In Abelson, I.

(Ed), Theories of Cognitive Consistency: A Source Book. Chicago, IL: Rand McNally.

Michela A. (2005). New technologies and cultural consumption – edutainment is born! European Journal of Marketing, 39(7/8), 729-736.

Liebarman Michael (2008). Adding value to CSM the Kano Model.World Advertising Research Centre, 48-50.

O’Sullivan, E., & Spangler, K. (1998).Experience Marketing – Strategies for the New Millenium. Pennsylvania: Venture Publishing, Inc.

Paula, ratiu Monica. “Customer Experience Management-The Most Important Dimension of the Service Firm Strategy”Annals of the University

of Oradea, Economic Science series, /15825450, 20081201

Rahman, Muhammad Sabbir;Haque,, Md., Mahunudul and Khan, Abdul Highe. “A Conceptual Study on Consumers’ Purchase Intention of

Broadband Services: Service Quality and Experience Economy Perspective”, International Journal of Business & Management, 2012

Thompson, C., Locander, W., &Pollio, H. (1989).Putting Consumer Experience Back into Consumer Research the Philosophy and Method of

Existential-phenomenology.Journal of Consumer Research, 16(2), 133-47.

Van Der Wagen, L. (1994). Building Quality Service with Competency Based Human Resource Management (p. 3). Butterworth-Heinemann,

Port Melbourne