Enhancing Customer Experience Using Business Intelligence Tools ...

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ScienceDirect Procedia Economics and Finance 11 (2014) 289 – 305

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 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.

© 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2013 The Authors. Published by Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or Institute of of Management Studies. Selection and/or peer-review peer-reviewunder underresponsibility responsibilityofofSymbiosis Symbiosis Institute Management Studies.

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

* Corresponding author. E-mail address:[email protected]

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

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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.


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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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:


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

•

Switchers: - Customers having high satisfaction scores but are equally likely to churnn

•

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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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


•

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. The objective with which the research analysis was carried out was to enhance the DTH customer experience. Thee following opinions were kept into picture: 1) Classify service attributes & determine what it takes to positively impact customer satisfaction. The constructt used was KANO’s model.

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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.

PLANNING TO CHANGE Yes 97 97 22%

No o 343 78% Figure 4: Reasons to switch (% & No.)

REASONS TO CHANGE Moving to Other Location 5 5%

Others 6 6%

Blanks 1 1%

Bad Experience 18 19%

Price 22 23% For Better Services/Offers 45 46%

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

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.


OUTCOME OF ANALYSIS: Part 1 The analytical constructs help understand churn, complaint handling, & customer behavioural patterns 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 provider. The top reasons were: to receive better service and pricing issues. Analysis related to complaint handling: In this study complaint analysis was carried out. From the sample of 440 customers a big sample of population off 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 billing followed by and service activation/deactivation, relocation on service issues and then payment related issues. Complaints and churn go hand in hand. Customer dissatisfaction leading to churn has become a very major issue for the DTH service providers. The studyy uses business intelligence to work out on these loop-holes, chalk out on strategies and identify the potential of the market.

Customers

ISSUE OF COMPLAINTS 120 100 80 60 40 20 0

Series1

111 39

34

28

24

23

Signal Issues

Billing/ Rechar ge Issues

Service Activati on/ Deactiv ation…

Produc t/Offer s Issue

Relocat ion Service Issues

Payme nt Issues

111

39

34

28

24

23

Figure 6: Major areas of complaints

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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.

7.80 7.60 7.40 7.20 7.00 6.80 6.60

80.00% 70.00% 61.54% 6 61 1 54 60.00% 59 38% 59.09% 59.38% 59 09% 5 59.09% % 50.00% 43.75% 43 4 75% % 40.00% 35 35. 35.0 5.00% 00% 00 00% % 25.00% 25. 25 2 5.00% 5. 5 .00 30.00% 27.27% 27 27. 2 7.2 ..27 27 2 27% 7% 23 23.9 23.96 3 3.96 3.96% .96% 96 9 96% 6% 6 % 1 23 2 3 3.3 3.33 ..33 3 33% 33 3% 2 3% 20.00% 18.81% 18 8 8.8 ..8 .81 81% 8 81 1% 1 % 18.18% 18 1 8..18 .1 1 18 18% 8% 8 % 12.50% 1 25 50 50% 0% 10.00% 0.00% More Rs. Rs. Less Rs. 15K- Rs. 30K- Rs. 50Kthan Rs. 6000than Rs. 300030K 50K 80K 80000 15000 6000 3000 75 75.25% 75.2 75. 75

CEI

7.13

7.03

7.36

7.17

7.08

7.71

7.49

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%

Planning to Change%

27.27%

18.18%

23.33%

23.96%

18.81%

25.00%

12.50%

% of Customers

ACEI Score

Income v/s Behaviour

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 huge demand for internet in TV. Other observations show an immense potential for the digital video recorderr market. 20.7% of the customers use interactive DTH services. This is a huge business opportunity.

ADDITIONAL SERVICES Telemarke ting, 2.27 % Social Networkin in n g, 11.82% 2% %

IInteractiv i e Games, 6. 59%

EE-Commerc rcc e, 7.27%

Figure 8: % of sample using add-on services

DVR, 19. 09%

IInternet, Inte Inter t 3 2 2.05%

3D Channel s, 4.09%

ADD-ON SERVICES Parental Lock, 7. 73% Video On De Demand De ,9 9.55%

Active Acti Active ti tive iive ve e Content , 1.14%

Active Movies, 12.95%

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


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 (Xaxis 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 Advertisements Promotions Plans & offers Pricing is value Trustworthy operator Variety off Channel Packages I feel valued customer First time installation Activation of first time services Behaviour of installation staff Picture & Audio Quality Continued Service in heavy rains User Friendly equipment Experience of change in service Experience in Relocation of services Dealers are widely spread Experience at Outlets Interactive website Customer Helpline easy Access Helpline staff are polite & courteous Queries solved efficiently & quickly Maintenance & Repair Options Accurate (Fair) Charge deduction Regular payment alerts Recharge Flexibility

Stated Importance 0.71 0.74 0.81 0.81 0.81 0.83 0.74 0.79 0.82 0.80 0.84 0.79 0.79 0.77 0.77 0.78 0.74 0.77 0.79 0.81 0.80 0.81 0.81 0.78 0.82

Derived Importance 0.15 0.22 0.20 0.27 0.31 0.01 0.25 0.12 0.20 0.23 0.31 0.26 0.23 0.22 0.17 0.31 0.20 0.18 0.15 0.26 0.21 0.27 0.22 0.27 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, behaviour of customer care staff has a concentrated impact on customer experience & business performance.

•

Parameters like promotions; value to the customers ®ular 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.


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.

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

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.

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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 Chances of Recommendation Less Customer Churn Spend More for better services Less complaints raised Purchase more services

3)

Effects on business

Table 3: Business effect of Enhancement in CE

Increase in Positive Publicity Higher Satisfaction Scores More Revenue Opportunities Less Customer Care Cost More Cross-Sale; Up-Sale

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.


Hypothesis Testing &Inferences:

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

CUSTOMER CATEGORIES Antagonists 113 26%

Champions 185 42%

SSwitchers witchers itchers it itchers chers h hers 42 9%

Captives C Ca Cap apt 100 23%

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.

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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


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.

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