Opportunities and Challenges
This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction levels. The paper also includes a comparison of the common resources that are used to measure customer satisfaction. The main part of the paper subsequently describes the related concerns and challenges that are faced by the business company to realize customer satisfactions. This paper presents a review of automated technology integrations for organizations to assess their customer satisfaction. These components can be integrated into communication tools to solve the existing problems efficiently, and improve the way of assessing customer satisfaction. The limitations or challenges of current approaches in technology related ways to realize the satisfactions are also discussed. The end of the paper gives recommendations and solutions to show the possible ways in solving the existing problems and improving the way of assessing customer satisfaction by integrating the appropriate technology.
Customer relationship management (CRM) is a term that refers to the practices, strategies and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, with the goal of improving business relationships with customers, assisting in customer retention, and driving sales growth. With the increasing challenges from competitors, customer relationship has become an essential asset for businesses to pursue prosperity. CRM has been widely implemented in many farsighted companies’ business strategies to deliver a more effective and efficient way of marketing, selling, and servicing customers. CRM covers the three stages as shown in Figure 1 which are: 1) Marketing stage, which focus on attracting customers by setting up promotions and advertisement, 2) Selling and Delivering stage, which conducts business activities to sell products or deliver services to customers, and 3) Customer Servicing stage which provide the after-sales services to retain customers and build customer loyalty. In order to effectively and efficiently build customer loyalty and retain customers, customer satisfaction is needed to be carefully and correctly addressed in the stage 3 of the CRM.
| Figure 1. Three stages of customer relationship management |
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Organizations are setting themselves strategies to ensure customer retention, and changing their employees to be more customer-focused and service-oriented (Mohsan et al., 2011). In 2014, the Gartner Group found that top marketers were focused on delivering and investing in a streamlined customer experience. The research found that digital marketers were spending almost as much to retain customers (45%) as they do to gain new ones (55%) (Ross, 2014a). In 2015, companies will recognize that a better customer experience will improve customer satisfaction, increase loyalty and improve retention. They will also see their customer acquisition costs increase and look to invest more in customer retention strategies. Companies will want technologies or tools that integrate easily with their existing marketing technology and customer relationship management (CRM) tools. The top drivers for spending on new technologies are all customer-related: (i) Improving customer service / customer satisfaction (62%); (ii) Increasing customer retention (59%); and (iii) Deliver better customer experience (55%). (MarketingCharts, 2014)
The advancements of technology and the internet have changed the ways business activities operate. Social media has become a main communication network in the daily lives of people around the world, with over a billion users of social media network worldwide. The advent of social media has caused organizations to engage customers directly through social media platforms such as Facebook, Twitter and LinkedIn. To encourage customer interactions on social media, businesses hope to monitor social conversations, from specific mentions of a brand to the frequency of keywords used, to determine their target audience and which platforms they use. Companies are interested in capturing sentiments such as a customer's likelihood of recommending their products and the customer's overall satisfaction in order to develop marketing and service strategies. Companies aim to integrate the data from social media with other customer data obtained from sales or marketing in order to get a single view of the customer. The proliferation of mobile devices and the advent of smartphones have caused CRM providers to consider upgrading their systems to include new features that cater to customers who use these technologies. In particular, mobile devices have been shown to be very personal devices which may provide firms with opportunities to build and maintain one-to-one relationships with their customers; combined with a large reach, low cost, rapid feedback, and constant accessibility and localization possibilities.
Recent technological advancements have also led to a deluge of data from many domains over the past decade. The term Big Data was termed to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. Another trend is the rise of social media which has become a main communication network in the daily lives of people around the world. Currently, social media embodies the leading and biggest source of consumer data which publishes hundreds and thousands of posts about a company’s products or services every day. An important question is how Big Data would change the way businesses manages customer relationships. Businesses gain customer insight by collecting and analyzing customer feedback data from different sources, including customer feedback surveys, social media sites, branded online communities and emails. Using customer feedback data, companies identify the customer experiences that are closely linked to customer loyalty and use that information to allocate resources to improve those customer experiences, and, consequently, increase customer loyalty. By integrating different business data silos, businesses can more fully understand how other business metrics could impact or be impacted by customer satisfaction and loyalty.
The previous three paragraphs have brought out some current trends which will drive the technologies or development of tools that integrate easily with the existing marketing technologies and CRM systems to improve customer satisfaction. This paper aims to present a review of automated technology integrations for organizations to assess their customer satisfaction. This paper first covers some essential background information before presenting the technological aspects. We organize the rest of the paper into three main parts. The first part summarizes the technology utilizations for organizations to communicate with customers. It also compares the common resources that are used to measure customer satisfaction. The second part describes the related concerns and challenges that are faced by the business company to realize customer satisfactions. These issues are due to the advent of Big Data, smartphone/mobile and social network technologies, and will be further elaborated. This part is followed by the third part which includes some recommendations and solutions to address the identified concerns and challenges. The paper also includes discussions for the roles of new technology components such as Big Data analytics, emotion recognition systems, and text content analysis tools to solve the existing problems efficiently and improve the ways for assessing customer satisfaction.
This section covers the essential background information before presenting the technology integration for customer satisfaction. Terms such as customer satisfaction, CRM, etc, will be defined. Critical elements of CRM and current CRM technology markets will be described. This section also discusses how customer satisfaction can be measured. Previous studies on relationship between effectiveness of CRM and customer satisfaction will be briefly reviewed.
Definitions
This subsection provides the definition for common terms used in this paper. Some terms will be further elaborated.
Customer Satisfaction
Customer satisfaction is the measure of how the needs and responses are collaborated and delivered to excel customer expectations. The marketing literature suggests that customer satisfaction operates in two different ways: transaction-specific and general overall (Yi, 1991). The transaction-specific concept concerns customer satisfaction as the assessment made after a specific purchase occasion, whereas overall satisfaction refers to the customer’s rating of the brand, based on all encounters and experiences (Johnson and Fornell, 1991). Overall satisfaction can also be viewed as a function of all previous transaction-specific satisfactions (Jones and Suh, 2000). Cumulative customer satisfaction is an overall evaluation based on the total purchase and consumption experience with goods or services over time. Transaction-specific satisfaction may provide specific diagnostic information about a particular product or service encounter, whereas overall satisfaction is a more fundamental indicator of the firm’s past, current and future performance (Anderson et al., 1994). This is because customers make repurchase evaluations and decisions based on their purchase and consumption experience to date, not just on a particular transaction or episode (Johnson et al., 2001).
In today’s competitive business marketplace, customer satisfaction is an important performance exponent and basic differentiator of business strategies. Two other closely related terms towards customer satisfaction are:
Measurement of Customer Satisfaction Index
Customer satisfaction index (CSI) is normally used to measure the satisfaction levels. Customer satisfaction models normally include the attributes that describe a product or service, the benefits of consequences these attributes provide customers, a customer's overall evaluation of their purchase and use experience, and their intentions. The CSI model is a structural model based on the theory that customer satisfaction is created by factors such as quality, value, expectations of customers, and the image of a company (Fornell et al., 1996). The model also estimates the results when a customer is satisfied or not. These results of customer satisfaction are consequence factors, such as complaints or loyalty of the customer (Fornell, 1992). Each factor in the CSI model is a latent construct, which is given by multiple indicators (Turkyilmaz & Ozkan, 2007). The Swedish Customer Satisfaction Barometer (SCSB) of 1989 was the first national CSI (Fornell, 1992). It was applied to 130 companies from 32 Swedish industries. However, while Fornell (1992) described the marketing foundations of SCSB in detail, he only examined the statistical aspects of the problem. To overcome the limitations of the method, the German Customer Satisfaction Barometer (German Customer Barometer, 1995) was introduced. It covers 42 different industries, for which telephone interviews are conducted with the public. Standardized questions about the levels of and reasons for satisfaction/dissatisfaction, recommendation of products and services, and complaint handling are included.
The American Customer Satisfaction Index (ACSI) model, which builds upon the original SCSB model, was launched in 1994 and has served as the basis for other CSI models developed in many countries around the world (Fornell & Larcker, 1981). The model is composed of six factors: perceived quality, customer expectations, perceived value, overall customer satisfaction, customer complaints, and customer loyalty. Each factor is linked to the others through a causal relationship (Fornell et al., 1996). A high level of customer satisfaction tends to reduce customer complaints, while also increasing customer loyalty. Thus, the causal model explains the inverse proportional relationship between customer complaints and customer loyalty (ACSI, 2010). The European Customer Satisfaction Index (ECSI) was introduced in 1999 in 11 European countries. The ECSI model (1999) employs six constructs including image, customer expectations, perceived qualities of hardware and software, perceived value, customer satisfaction, and customer loyalty. These six factors are linked through causal relationships. The image has a determining influence on customer expectations, while customer expectations, in turn, affect the perceived quality of hardware or software (O'Loughlin and Coenders, 2004). The National Customer Satisfaction Index (NCSI) was developed by the Korea Productivity Center and has been in use since 2009 (NCSI, 2014). The NCSI gauges the satisfaction level of a product, and the results are synthesized into data in the following categories: company, industry, economic sector and national levels. Specifically, the NCSI model assesses the expectancy level, quality, recognition of value, total satisfaction, complaint rate, customer loyalty, and customer maintenance rate. The advantage of this index is that it analyzes the cause and effect of the findings in its reports.
Customer Relationship Management (CRM)
The Gartner Consulting Group defines CRM as follows: CRM is a business strategy that its achievements, profitability, revenue and customer satisfaction with the organization, customer classification, promotion behaviours and implementing customer satisfaction for the binding process gives the customer the optimum circuit. It is dividable into two separate parts: 1) a business strategy that prepares organization for being precursor through customers or customer centric, 2) a strong tool in delivering profit making goods to customers through understanding and foreseeing their needs. The main elements of CRM can be illustrated as a customer interface, customer database, service catalogue, customer transactions, general information, policies and processes, service delivery organization and customer satisfaction. By accumulating information across customer interactions and processing this information to discover hidden patterns, CRM applications help firms customize their offerings to suit the individual tastes of their customers. CRM can also be defined as an integrated approach which aids the firm in every interaction it has with customers in business purposes like marketing, sales and support. The internet has altered the traditional business model of the organization. The technology aspects of CRM will be further discussed in the later section of this paper.
Review of Previous Studies on the Relationship between Effectiveness of CRM, Customer Satisfaction
Some research works on the effect of CRM on customer knowledge and customer satisfaction will be briefly discussed in this subsection. Khaligh et al. (Khaligh, 2012) investigate the impact of CRM on customer loyalty and retention in the telecom industry in Iran. The data are collected from 200 Iranian telecom services users. Their findings show that commitment and vision of the management system is highly required for a successful CRM implementation. The structure of the strategy should be based on flexibility and explicity of the policies especially pricing policies. These factors are very important to increase customer loyalty and benefit of the firm (Khaligh, 2012). According to the research by Bhattacharya (Bhattacharya, 2011), CRM is implemented in an organization to reduce cost and increase company performance. In a successful CRM implementation, data are collected from internal and external sources such as sales department, customer service, marketing, after sales services, procurement, and others. The findings of this study show that the customer perception and treatment given to each customer individually are able to assist in solving many customers’ problems. According to the conceptual framework proposed by Faed (Faed, 2010), customer relationship management amplifies the relationships of customers and competitors in a firm to increase the share of the organization in the marketplace by integrating technology, procedures and people.
Kim et al., (Kim, 2003) stress that each perspective of the CRM framework is evaluated by a set of related metrics. In this regards, a case study has been carried out by Kim et al., (Kim, 2003) for an online shopping company in South Korea that have sales of 30,000 products in 12 categories. Data are collected through experts’ interview, questionnaires and weblog analysis. The findings show that the factor which is very important to increase customer satisfaction, customer loyalty and benefits of the firm is to clear all vagueness and implicit problems that exist in the top level of strategic managements. A single and explicit language would be provided for accurate communication in an organization. According to a study on 100 firms who are active in different areas of industry such as manufacturing, communication, financial and others, Bohling et al. (Bohling, 2006) develop a number of criteria to implement CRM successfully. These criteria are divided into three main groups: (1) project focused; (2) internally oriented metrics, employees’ adoption; and (3) externally oriented metrics, customer satisfaction and loyalty. According to the findings, the most important externally oriented criteria are as follows: (1) verified customer influence in terms of loyalty and satisfaction, (2) measureable revenue development, (3) enhanced information and perception, (4) measurable cost drop, (5) enhanced employee efficiency, (6) practice by employees, and (7) compliance to particulars.
Wang and Lo (Wang, 2004) found that the CRM model is based on two perspectives. The first perspective measures the factors related to customer behavior such as: repurchasing cross and up-selling and customer acquisition rate, and the second perspective measures the relationship quality, such as customer satisfaction and customer loyalty (Wang, 2004). Data were collected randomly from 400 selected customers for two security companies from China. The findings show that emotional and functional behavior of customers have a a positive impact on customer satisfaction, and customer satisfaction has a positive effect on the customer behavior based on CRM elements. Finally, the result of this study shows that customer behavior based on CRM have a positive effect on customer and brand loyalty (Wang, 2004). Zineldin, (Zineldin, 2006) developed a triangle strategy between quality, CRM, and customer loyalty which is leading to companies competitiveness. This research was designed to measure satisfaction and loyalty of the customers based on two main conditions where the customer database information and strategy of CRM should be well structured and the capacity of the system should be enough to produce data for accurate analysis. According to the findings of the research, any changes for the quality of the services or productions in a firm over time could be used as an indicator to find the level of customer loyalty through a well-structured CRM strategy.
Izquierdo et al. (Izquierdo, 2005) developed a model in which, car repair and maintenance are tested as a case where long term customer relationship is frequentative. Path analysis is used to evaluate the association of customers’ perception, market loyalty and market position. The hypotheses were evaluated using a path analysis, which examines the relationship between marketing activities and economic performance. This model is proposed based on performance of the market and economic. The measures of proposed model are as follows (Izquierdo, 2005): the position of market, loyalty, customer insight, economic and market performance. The findings suggest that CRM implementation include attraction activities which are service quality, commercial practices and loyalty programs such as bonus, contact, satisfaction and complaints handling. These results in appropriate perception of customers leading to increasing customer loyalty and therefore, the economic performance of the firm would be increased. Feinberg and Kadam (Feinberg, 2002) argue that emphasizing towards the online business rather than continuing with the traditional way of business is necessary nowadays. The usage of internet provides an opportunity for business to use it as a tool for CRM. According to their research, there are 42 different eCRM features used by the retailers. The finding shows that there is significant relationship between CRM implementation on websites of the retailers and customer satisfaction which leads to customer loyalty. However, all attributes of implemented CRM are not equal in terms of predicting the customer satisfaction and loyalty (Feinberg, 2002). Choi (Choi, 2013) examined the impact of CRM elements on customer satisfaction and loyalty. Four critical elements of CRM are examined in this study. These elements are interaction management, relationship development, customer service and employees’ behavior. Multiple regression analysis is used to examine the relationship of the variables. The results show that CRM does have a positive relationship with the dependent variables (customer satisfaction and loyalty). However, not all elements have significant impact on the dependent variables. The behavior of the employees and relationship development was found to contribute most to customer satisfaction. The findings show that the behavior of the employees is significantly related and contributed to customer satisfaction and loyalty.
TECHNOLOGIES: COMMUNICATION AND CRM
This section covers some communication technologies commonly used in business activities. The use of CRM and its technologies are also elaborated in this section.
Utilization of Communication Technologies or Tools in Business Activities
Since the past decades in the area of marketing, there have been a number of innovative ways of integrating technology components into the implementations of business strategies. One of the largest improvements or advantages delivered is the different communication process from one way communication to two-ways communication (Kerin et al., 2013). This transforms the “end-path” of the connection (between company and customer) which from traditional media such as television or magazine advertisement that ends with the received customer, to social media such as Facebook and Twitter that does not end with any individual receiver. These improved communication processes not only gives the organizations faster and a more convenient way to approach their customers, it provides the advancement of survey tools at the same time. The communication technologies or tools which are commonly utilized include:
Table 1 shows a comparison of some advantages and disadvantages for some of the most commonly used survey tools.
Table 1. Comparison of advantages and disadvantages for commonly used survey tools
| Sources | Advantages | Disadvantages |
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| Telephone Call, and Video Conferencing | • Customer and company are able to give immediate responses • Very helpful in emergency cases • Customer and company are able to give immediate responses • The customer can watch the consultant’s demonstrations, presentations or expressions during video conferencing |
• Customer or company requires to compensate for the telephony services • Both customer and company sides need to have the basic requirement of video conferencing such as web-camera and microphone • Highly dependent on the professionalism of the call agent at the company side |
| Survey (Online, Web) | • Free • Easier and more convenient to administer compared to the paper survey • Straightforward to assess customer satisfaction based on the questions and satisfaction scales set by the company |
• Hard to have customers recall their experiences • Clients have high possibilities of not stating the truth • Required to develop or design multiple useful and relevant survey questions |
| Comments or posts in Social Media | • Free • The customer saves time and energy • Social media is popularly used recently |
• Not suitable if the customer’s complaint needs immediate response from the company • Emotions are difficult to be identified by company from text • Normally is set up with a language only |
Technologies and CRM
CRM involves set of tools, technologies, and procedures to organize, automate, and synchronize business processes. Technological developments continue to affect the organisation and the marketing of its products and services. CRM needs to be seen as more than just technology with the technology being regarded as the enabler of the CRM strategy (Xu, 2002). In using technology, a number of technology applications can be identified that are used in the development of CRM strategy (Xu & Walton, 2005; Zaayman, 2004; Chen, 2003; META, 1999). The three main components of CRM systems can be identified, as illustrated in Figure 2 as:
| Figure 2. Technology applications of CRM |
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The CRM Technology Market
The four main vendors of CRM systems are Salesforce.com, Microsoft, SAP and Oracle. Other providers are popular among small to mid-market businesses, but these four tend to be the choice of large corporations.
In this section, the concerns and challenges from management and organization are identified. Traditional concerns on customer satisfaction due to lack of automated technologies will be presented first. This is followed by the concerns and challenges due to advancement of technologies which include social network, mobile technologies and Big data. Subsequently, the solutions and recommendations for technologies to have better customer experience and satisfaction will be presented in the next section.
Traditional Concerns
Customer satisfaction is one of the main sources to acknowledge the companies about the quality of their products or service and the customer’s loyalty (Hill, 2006). Different levels of satisfactions from customers can be categorized from the feedback resources. Some concerns of the company when handling those feedbacks or reviews of customer include:
Traditionally, many organizations conduct a customer satisfaction survey every year. Although they may hold a remarkable satisfaction results from the customer, the data may not be exact. Customers who respond may fail to call up their experience and complete the survey based on their overall impressions. Without the specific customer information based on the literal experiences, it is difficult for the organization to track for their error and revise for improvements in their services or marketing schemes.
Concerns and Challenges due to Social Networks
Social media is changing our world. Through the advent of smartphones and social media, accessibility of information is higher than it ever has been before. Customers are frequently asked to “like” companies on Facebook, to “follow” companies on Twitter, or to “connect” via LinkedIn. Incorporating the use of social media in customer interactions is a logical progression for firms to expand communication with their customers (Avlonitis, 2010). For example, trade-media encourage the use of social media (Wirthman, 2013) for firms, suggesting that social media is important for business as it aids in generating business exposure, increasing traffic, and providing marketplace insight (Stelzner, 2012). From a sales force perspective, Andzulis, Panagopoulos, and Rapp (Andzulis, 2012) assert that social media should be an integral part of a firm's repertoire, as it allows salespeople to engage customers and build social capital that would “encourage customers to interact, engage, and establish relationships with them” (Agnihotri, 2012). For example, networks on LinkedIn can be used to build awareness and gain referrals (Andzulis, 2012).
There are two key downstream effects of social media within the sales domain. First, social media provides a means to communicate to customers in a manner that may plausibly enable greater salesperson responsiveness. For instance, when consumer complaints are lodged on a social networking site, 58% of consumers want a response; yet only 22% report receiving a response (Right Now Technologies, 2010). Hence, social media may provide one means to enable the salesperson to communicate in a more responsive manner. Second, social media may have implications on customer satisfaction. With increased interactions and contact with firms, power is shifting from seller to buyer (Prahalad, 2004). An increase in buyer-seller collaboration and co-creation of knowledge and value (Greenberg, 2010) has placed buyers on a more equal footing with sellers. As such customers may hold higher expectation for these interactions and engagements, such that firms and customer contact employees must adapt (Hibbert, 2012) or risk alienating or losing their customer base.
Social media introduces a new avenue for two-way communication and creates possibilities for more positive interactions between buyers and sellers. By extending sales interactions in a way that welcomes two-way communication, non-selling activities and relationship components such as prospecting and after-sales follow-ups are encouraged through the use of social media. This makes it easier for potential customers to ask questions or express needs while also making it more natural for salespeople to uncover additional selling opportunities, track customer activity, and communicate success stories (Andzulis, 2012). The literature also highlights the importance of the link between the salesperson's use of social media and information communication. The literature has argued that the use of technology within the sales force “represents only a necessary, but not sufficient, criterion for performance” and “it is important to note that the mediating role of the manner of use is equally vital” (Sundaram, 2007). For instance, Hunter and Perreault (Hunter, 2007) posit that sharing market knowledge will mediate the impact of sales technology and performance outcomes. One must be mindful of salesperson behaviors and other goals of social media interactions when examining customer satisfaction.
Concerns and Challenges due to Mobile Technologies
The proliferation of mobile devices and the advent of smartphones have caused CRM providers to upgrade their systems to include new features that cater to customers who use these technologies. Because of the rapid developments in mobile technologies, technologies need to be further extended to match this new channel. Although most companies now have their websites and digital assets optimized for mobile devices, it’s still more or less an add-on feature. In 2015, it is observed that the progressive customer-centric companies are moving towards a mobile-first policy. Some main reasons for customers to shift to mobile are as follows: (Ross Beard, 2014b).
Concerns and Challenges due to Big Data
The concept of big data has been defined through the 3V model, which was defined in 2001 by Laney (Laney, 2001) as: “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making”. More recently, in 2012, Gartner (Beyer, 2012) updated the definition as follows: “Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization”. Both definitions refer to the three basic features of big data: Volume, Variety, and Velocity. Other organisations and Big Data practitioners have extended this 3Vmodel to a 4V model by including a new “V”: Value (Hashma, 2015). This model can be even extended to 5Vs if the concept of Veracity is incorporated into the big data definition. Big data solutions aim to analyse data in order to make sense of them and exploit their value. Big data refers to datasets that are terabytes to petabytes in size, and the massive sizes of these datasets extend beyond the ability of average database software tools to capture, store, manage, and analyse them effectively. For businesses, the problem of Big Data is one of applying appropriate data federation and analytic techniques to these disparate data sources to extract usable insight to help them make better business decisions.
Figure 3 illustrates some common ways companies integrate disparate data sources. The columns represent the different types of customer feedback sources and customer metrics. The rows represent the other data sources and metrics: financial, operational and constituency. Even though many different data sources can be integrated, this approach is considered as a “customer-centric” approach because the data are organized to gain insight about the causes and consequences of customer satisfaction, loyalty, etc. Businesses gain customer insight primarily by collecting and analyzing customer feedback data from different sources, including customer feedback surveys, social media sites, branded online communities and emails. Using customer feedback data, companies identify the customer experiences that are closely linked to customer loyalty and use that information to allocate resources to improve those customer experiences, and, consequently, increase customer loyalty. Customer feedback is just one type of data that need to be analyzed and managed. By integrating different business data silos, businesses can more fully understand how other business metrics could impact or be impacted by customer satisfaction and loyalty. Big Data analytics can help to answer bigger questions about customer needs. Big Data analytics for data which includes data from social media is inherently interdisciplinary and spans areas such as data mining, machine learning, statistics, graph mining, information retrieval, linguistics, natural language processing, the semantic Web, ontologies, and big data computing.
| Figure 3. Different data sources to be integrated in a big data system |
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The gathering, fusion, processing and analyzing of the Big Data from unstructured (or semi-structured) sources to extract value knowledge is an extremely difficult task which has not been completely solved. The classic methods, algorithms, frameworks and tools for data management have became inadequate for processing the vast amount of data. This issue has generated a large number of open problems and challenges on big data domain related to different aspects as knowledge representation, data management, data processing, data analysis, and data visualisation (Kaisler, 2013). Some of these challenges include accessing to very large quantities of unstructured data (management issues), determination of how much data is enough for having a large quantity of high quality data (quality versus quantity), processing of data stream dynamically changing, or ensuring the enough privacy (ownership and security).
In this section, recommendations by adopting new technologies into the existing customer structures are included. New or advance technologies for (i) supporting of CRM, (ii) data analytics, and (iii) Big Data, are recommended. This section also provides two specific automated systems which can be incorporated into the existing communication tools or CRM systems.
New Technologies in Supporting of CRM
One of the newer CRM application software which is related to real value of electronic business is Electronic CRM (eCRM). It helps companies improve the effectiveness of their interaction with customers while at the same time making the interactions personalized. This subsection also recommends a new kind of CRM which works with wireless device called customer relationship management based on mobile or mCRM.
Social CRM
Social CRM refers to businesses engaging customers directly through social media platforms. Companies are interested in capturing sentiments such as a customer's likelihood of recommending their products and the customer's overall satisfaction in order to develop marketing and service strategies. From the technical point of view, Social CRM adds a deeper layer of information onto traditional CRM by adding data derived from social networks like Facebook, Twitter, LinkedIn or any other social network where a user publicly shares information. The key benefit of Social CRM is that it enables companies to track a customer's social influence and source data from conversations occurring outside of formal, direct communication, and to keep a full audit history of all customer interactions, regardless of different social channels they may have used. (Social CRM, 2015)
Table 2. Comparison of mobile CRM Systems
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Mobile CRM
Mobile CRM (mCRM) is a subset of Electronic CRM (eCRM). Whereas eCRM (ECRM, 2015) allows customers to access company services from more places over the Internet, mCRM takes this one step further and allows customers to access the systems from a mobile phone, smartphone or tablets with internet access, resulting in high flexibility. Mobile CRM apps take advantage of features that are unique to mobile devices, such as GPS and voice-recognition capabilities, in order to better serve customers. mCRM should be integrated in the complete CRM system to serve a complete range of customer relationship activities. There are three main reasons that mCRM is becoming so popular: 1) The first is that the devices consumers use are improving in multiple ways that allow for this advancement. Displays are larger and clearer and access times on networks are improving overall, 2) Secondly, the users are also becoming more sophisticated and adapts easily to technology, and 3) Thirdly, the software being developed for these applications has become worthwhile and useful to end users. Table 2 shows a comparison of some mCRM and their mobile platforms (Mobile CRM, 2015).
Technologies for Data Analytics
This subsection briefly reviews some data analytic techniques. It also provides two specific technical recommendations: (i) automated text content analysis to analyze and estimate customer satisfaction from text from different sources, and (ii) automated emotion recognition to analyze and estimate customer satisfaction from video chat or video conferencing.
Structured Data Analytics
Data analytics is grounded in the fields of data mining and statistical analysis, which have been thoroughly studied in the past few decades. Most current machine-learning algorithms depend on human-designed representations and input features, which is a complex task for various applications. Recently, deep learning, a set of machine-learning methods based on learning representations, is becoming an active research field. Deep-learning algorithms incorporate representation learning and learn multiple levels of representation of increasing complexity (Hinton, 2007). Statistical machine learning, based on precise mathematical models and powerful algorithms, has already been applied. Utilizing data characteristics, temporal and spatial mining can extract knowledge structures represented in models and patterns for high-speed data streams and sensor data (Gaber, 2005).
Text Analytics
Text mining is an interdisciplinary field at the intersection of information retrieval, machine learning, statistics, computational linguistics, and data mining. Most text mining systems are based on text representation and natural language processing (NLP). NLP techniques can enhance the available information about text terms, allowing computers to analyze, understand, and even generate text using approaches like lexical acquisition, word sense disambiguation, part-of-speech tagging, and probabilistic context free grammars (Manning, 1999). Based on these approaches, several technologies have been developed for text mining, including information extraction, topic modeling, summarization, categorization, clustering, question answering, and opinion mining. Information extraction refers to the automatic extraction of specific types of structured information from text. As a subtask of information extraction, named-entity recognition (NER) aims to identify atomic entities in text that fall into predefined categories, such as person, location, and organization. The purpose of text categorization is to identify the main themes in a document by placing the document into a predefined topic or set of topics. Text clustering is used to group similar documents and differs from categorization in that documents are clustered as they are found instead of using predefined topics. Opinion mining, which is similar to sentiment analysis, refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in news, commentaries, and other user-generated contents. It provides exciting opportunities for understanding the opinions of the general public and customers regarding social events, political movements, company strategies, marketing campaigns, and product preferences (Pang, 2008).
Web Analytics
Web analytics aims to retrieve, extract, and evaluate information for knowledge discovery from web documents and services automatically. Web mining can be categorized into three areas of interest based on which part of the web is mined: web content mining, web structure mining, and web usage mining (Pal, 2002). Web content mining is the discovery of useful information or knowledge from website content. However, web content may involve several types of data, such as text, image, audio, video, symbolic, metadata, and hyperlinks. Recent research on mining image, audio, and video is termed multimedia analytics, which will be investigated in the following section. Because most of the web content data are unstructured text data, much of the research effort is centered on text and hypertext content. Text mining is a well-developed subject, as described above. Hypertext mining involves mining semi-structured HTML pages that have hyperlinks. Supervised learning or classification plays a key role in hypertext mining, such as in email management, newsgroup management, and maintaining web directories (Chakrabarti, 2000) Web structure mining is the discovery of the model underlying link structures on the web. In this case, structure represents the graph of links in a site or between sites. The model is based on the topology of the hyperlink with or without link description. This model informs the similarities and relationships among different websites and can be used to categorize websites. The Page Rank (Brin, 1998) and CLEVER (Konopnicki, 1995) methods exploit this model to find webpages. Focused crawling (Chakrabarti, 1999) is another example that successfully utilizes this model. Web usage mining refers to mining secondary data generated by web sessions or behaviors. Web usage mining differs from web content mining and web structure mining, which utilize the real or primary data on the web. The web usage data includes the data from web server access logs, proxy server logs, browser logs, registration data, user sessions or transactions, cookies, user queries, bookmark data, mouse clicks and scrolls, and other data generated by the interaction of users and the web.
Multimedia Analytics
Multimedia content analytics refers to extracting interesting knowledge and understanding the semantics captured in multimedia data. Research in multimedia analytics covers a wide spectrum of subjects, including multimedia summarization, multimedia annotation, multimedia indexing and retrieval, multimedia recommendation, and multimedia event detection. Audio summarization can be performed by simply extracting salient words or sentences from the original data or by synthesizing new representations. On the other hand, Video summarization involves synthesizing the most important or representative sequences of the video content. It can be static or dynamic. Static video summarization methods use a sequence of key frames or context-sensitive key frames to represent video. Dynamic video summarization methods utilize a sequence of video segments to represent the video and employ low-level video features and perform an extra smoothing step to make the final summary look more natural.
Multimedia annotation refers to assigning images and videos a set of labels that describe their content at syntactic and semantic levels. With the help of these labels, the management, summarization, and retrieval of multimedia content can be accomplished easily. Multimedia indexing and retrieval concerns the description, storage, and organization of multimedia information to help people and multimedia resources conveniently and quickly (Hu, 2011). A general video retrieval framework consists of four steps: structure analysis; feature extraction; data mining, classification and annotation; and query and retrieval. Structure analysis aims to segment a video into a number of structural elements with semantic content, using shot boundary detection, key frame extraction, and scene segmentation. Upon obtaining the structure analysis results, the second step is to extract features-consisting mainly of features of the key frames, objects, text, and motion for further mining (Li X, 2008; Li X, 2010). This step is the basis for video indexing and retrieval. Using the extracted features, the goal of data mining, classification, and annotation is to find patterns of video content and assign the video into predefined categories to generate video indices. When a query is received, a similarity measure method is employed to search for the candidate videos.
The objective of multimedia recommendation is to suggest specific multimedia contents for a user based on user preferences, which has been proven as an effective scheme to provide high-quality personalization. Most current recommendation systems are either content-based or collaborative filtering based. Content-based approaches identify common features of user interest, and recommend to the user other content that shares similar features. These approaches rely on content similarity measures and suffer from the problems of limited content analysis and over-specification. Collaborative filtering-based approaches identify the group of people who share common interests and recommend content based on other group members' behavior (Park, 2009). Multimedia event detection aims to detect the occurrence of an event within a video clip based on an event kit that contains a text description about the concept and video examples. Video event detection can be used to detect sports or news events, repetitive patterns events such as running or unusual events in surveillance videos. Ma et al. (Ma Z., 2012) proposed a novel algorithm for ad hoc multimedia event detection, which addresses a limited number of positive training examples.
| Figure 4. The flow diagram of future direction where both automated systems can be integrated in cross-platform application |
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Specific Recommendations: Automated Text Content Analysis & Emotion Recognition for Customer Satisfaction Assessment
This subsection provides some recommendations for adopting automated systems into the existing tools to show how the previously mentioned concerns and challenges can be addressed. Integrations of cross-platform customer satisfaction assessment tools can also be done as shown in Figure 4. Based on various communication tools such as video conferencing, telephone call, and online feedback in social media or email, different data can be captured as shown in Figure 5. These captured data from the communication tools can be in the form of images, speech signals and text. Typically in most organizations, these data are stored in databases for future analysis after capture. However, this is not cost-effective because of the high volume of data needed to be stored before the analysis is performed. So it is recommended to use the captured data and apply analysis to extract important information simultaneously. In this case, automated emotion recognition system and automated text content analysis tool can be adopted to solve the problems.
| Figure 5. Captured data from different communication tools |
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Automated Text Content Analysis
Most text mining systems are designed to classify trending topics from social media tools into desired categories or classes such as positive, neutral, negative, etc. The generic process of the text mining system (as shown in Figure 6) can be conducted in the following steps:
| Figure 6. Process flow of the information and data between customer and consultant over the communication platform with integrated text mining system |
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Some examples of automated text content analysis tools GetSatisfaction (https://getsatisfaction.com/corp/), Optimove (http://www.optimove.com/), and Temper (http://temper.io/).
Automated Emotion Recognition System
Emotional states are more directly related to satisfaction from customers compared with other information forms extracted from image and speech. (Kasiran, 2007). Using the automated emotion recognition system, the consultants or agents are able to assess customer satisfaction based on their emotions. Alternative solutions can then be given to the customer. The process flow can be visualized as shown in Figure 7. The automated emotion recognition system consists of five main steps:
| Figure 7. Process flow of the information and data between customer and consultant over the communication platform with integrated audio-visual emotion recognition |
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Big Data Technologies
The previous subsection has recommended some data analytics technologies which can be used to analyse different data types. As mentioned in the previous subsections for concerns and challenges due to social networks and Big Data, the exponential growth of social media has created serious problems for traditional data analysis algorithms and techniques such as data mining, statistics, machine learning, etc., due to their high computational complexity for large datasets. These methods do not scale as the data size increases. For this reason, the methodologies and frameworks which can address the need of Big Data are becoming important in a wide number of research and industrial areas. In order to analyze the social media data, the traditional data analytic techniques require adapting and integrating them to the new big data paradigms emerging for massive data processing. Various Big Data frameworks have been arising to allow the efficient application of data mining methods and machine learning algorithms in different domains. This subsection provides a short introduction to the methodology based on the MapReduce paradigm and a description of the most popular framework that implements this methodology, Apache Hadoop. Afterwards Apache Spark is described as an emerging big data framework that improves the current performance of the Hadoop framework. Finally, some implementations and tools for big data domain related to distributed data file systems, data analytics, and machine learning techniques are also recommended.
MapReduce
The MapReduce (Dean, 2004; Dean, 2008) programming paradigm and its related algorithms (Shim, 2012), were developed to provide significant improvements in large-scale data-intensive applications in clusters (Zaharia, 2008). The programming model implemented by MapReduce is based on the definition of two basic elements: mappers and reducers. The concept behind this programming model is to design map functions called mappers that are used to generate a set of intermediate key/value pairs, after which the reduce functions will merge (reduce can be used as a shuffling or combining function) all of the intermediate values that are associated with the same intermediate key. The key aspect of the MapReduce algorithm is that if every map and reduce is independent of all other ongoing maps and reduces, then the operations can be run in parallel on different keys and lists of data.
Apache Hadoop
Apache Hadoop (White, 2009) is an open-source software framework written in Java for the distributed storage and distributed processing of very large datasets using the MapReduce paradigm. All of the modules in Hadoop have been designed taking into consideration the assumption that hardware failures (of individual machines or of racks of machines) are commonplace and thus should be automatically managed in the software by the framework. The core of Apache Hadoop comprises a storage area, the Hadoop Distributed File System (HDFS), and a processing area (MapReduce). The HDFS spreads multiple copies of the data across different machines. This not only offers reliability without the need for RAID-controlled disks but also allows for multiple locations to run the mapping. If a machine with one copy of the data is busy or offline, another machine can be used. A job scheduler called JobTracker keeps track of which MapReduce jobs are executing, schedules individual maps, reduces intermediate merging operations to specific machines, monitors the successes and failures of these individual tasks, and works to complete the entire batch job.
Apache Spark
Apache Spark (Zaharia, 2010) is an open-source cluster computing framework that was originally developed in the AMPLab at University of California, Berkeley. Spark had over 570 contributors in June 2015, making it a very high-activity project in the Apache Software Foundation and one of the most active big data open source projects. It provides high-level APIs in Java, Scala, Python, and R and an optimized engine that supports general execution graphs. It also supports a rich set of high-level tools including Spark SQL for structured data processing, Spark MLlib for machine learning, GraphX for graph processing, and Spark Streaming. The Spark framework allows for reusing a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms as well as interactive data analysis tools. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Compared to Hadoops two-stage disk-based MapReduce paradigm (mappers/reducers), Sparks in-memory primitives provide performance up to 100 times faster for certain applications by allowing user programs to load data into a clusters memory and to query it repeatedly. One of the multiple interesting features of Spark is that this framework is particularly well suited to machine learning algorithms (Xin, 2013).
Other Frameworks and Software Implementation
This subsection lists some recent frameworks and software implemented what are commonly used to develop efficient MapReduce-based systems and applications in Big Data. Apache Mahout, Spark MLib, MLBase are developed for the field of Machine Learning. Apache Nutch, Apache Zeppeline, Pentaho and SparkR can be used for business intelligence and data analysis. Apache Cassandra, Apache Giraph and MangoDB are designed for document and graph data models. The remainders are related to distributed programming and distributed files systems.
The advancement of technology has given a big potential not only to extend the marketing strategy, but also to improve the quality of services provided by business companies. To provide the highest quality of services to customers and retain them, their satisfactions have to be carefully and professionally handled. Based on the current available communication technology, the assessments of customers’ satisfactions will no longer be difficult and time-consuming if automated systems are integrated into the business processes. This paper has provided some possible solutions and recommendations. It has also shown how the advanced technology components are capable of measuring customer satisfactions automatically from speech, facial expressions, and text data collected from the organization’s daily used communication tools.
This research was previously published in the International Journal of Technology and Educational Marketing (IJTEM), 6(2); edited by Purnendu Tripathi and Siran Mukerji; pages 49-78, copyright year 2016 by IGI Publishing (an imprint of IGI Global).
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