Using text-mining-assisted analysis to examine the applicability of unstructured data in the context of customer complaint management

Double DegreeIn quest of gaining a more holistic picture of customer experiences, many companies are starting to consider textual data due to the richer insights on customer experience touch points it can provide. Meanwhile, recent trends point towards an emerging integration of customer relationship management and customer experience management and thereby availability of additional sources of textual data. Using text-mining-assisted analysis, this study demonstrates the practicality of the arising opportunity with means of perceived justice theory in the context of customer complaint management. The study shows that customers value interpersonal aspects most as part of the overall complaint handling process. The results link the individual factors in a sequence of ‘courtesy → interactional justice → satisfaction with complaint handling’, followed by behavioural outcomes. Academic and managerial implications are discussed

User dependent cryptography for security in future mobile telecommunication systems

In this paper we propose a user dependent scheme for enhancing security of the transmitted content in the future telecommunication systems. In order to achieve a higher level of security we introduce a scheme where the user identity gets involved in the encryption/decryption processes using an additional component for the block cipher which represents the user’s behavioural model. Applying such a scheme, in addition to introducing more difficulties to an attacker due to the user dependency of the cipher algorithm, gives the mobile operator the opportunity to ensure that a licensed service has not been shared by the customer. To show the feasibility of our approach we use the concept of invertible Boolean functions as an example

Fast and Accurate Indonesian QnA Chatbot Using Bag-of-Words and Deep-Learning For Car Repair Shop Customer Service

A chatbot is a software that simulates human conversation through a text chat. Chatbot is a complex task and recent approaches to Indonesian chatbot have low accuracy and are slow because it needs high resources. Chatbots are expected to be fast and accurate especially in business settings so that they can increase customer satisfaction. However, the currently available approach for Indonesian chatbots only has low to medium accuracy and high response time. This research aims to build a fast and accurate chatbot by using Bag-of-Words and Deep-Learning approach applied to a car repair shop customer service. Sixteen different intents with a set of their possible queries were used as the training dataset. The approach for this chatbot is by using a text classification task where intents will be the target classes and the queries are the text to classify. The chatbot response then is based on the recognized intent. The deep learning model for the text classification was built by using Keras and the chatbot application was built using the Flask framework in Python. Results showed that the model is capable of giving 100% accuracy in predicting users’ intents so that the chatbot can give the appropriate responses and the response time is near zero milliseconds. This result implies that developers who aim to build fast and accurate chatbot software can use the combination of bag-of-words and deep-learning approaches. Several suggestions are presented to increase the probability of the chatbot’s success when released to the general public

Integrating a web-based GIS in the optimization of the customer connection process for utility company: A case of Kenya Power; Lighting Company, Ltd.

Great strides have been made world over in the use of GIS as a tool for the management of resources and decision making in the utilities. Utilities are now integrating GIS with other company systems in a bid to reduce operational costs, maximize revenue as well as improve efficiency and care to customers. However, this use has been confined to ensuring optimised service delivery to existing customers and overlooks new prospective customers. With privatization and deregulations, utility companies are now faced with a new challenge to strive for the market share in the most efficient and cost-effective ways. This research sought to develop a complementary web-based GIS application that can be integrated with existing utility company systems to improve efficiency in the new customer connection process. Waterfall System Development Methodology (SDM) was adopted in this research. Its simplicity and straightforwardness gave it a niche over other SDMs in-terms of implementation as one only moved to the next stage once the previous stage had been fully completed and tested. Digital online map, counties information data, enquiry for supply forms as well as the supply contract forms were used as the main datasets in the study. The objectives of this research were achieved by the development of a geodatabase to record, store and retrieve customer information; and a web-based GIS application to facilitate recording and upload of this information. It is possible to develop a web based GIS application that can be integrated with existing company's systems. Through integration, the system will automate and augment most of the manual processes in the new connection work-flow. This development would greatly improve new customer connection efficiency, maximize revenue collection for the utility and elevate the customers' socio-economic statuses. The system would also provide a platform for the monitoring and analysis of the infrastructure development geared towards the achievement of Kenya's Vision 2030

The Mobile Commerce Prospects: A Strategic Analysis of Opportunities in the Banking Sector

Mobile Commerce has gained increasing acceptance amongst various sections of the society in previous years. The reasons for its growth can be traced back to technological and demographical developments that have influenced many aspects of the socio-cultural behaviour in today's world. The need (and/or wish) for mobility seems to be the driving force behind Mobile Commerce. The launch of UMTS technology has provided Mobile Commerce with the necessary verve.Mobile Banking presents an opportunity for banks to retain their existing, technology-savvy customer base by offering value-added, innovative services and to attract new customers from corresponding sections of the society. The customer survey provides evidence that such sections in the meanwhile include the affluent and financially relevant groups of the society in Germany. The time seems to be ripe to convert this non-negligible customer interest into business-driving customer demand. A proactive attitude on the part of the banks seems to be therefore recommendable.Many banks in Germany have come to regard Mobile Banking as a necessary tool for thwarting negative differentiation vis-à-vis rivals and to foster/retain an innovative image. This self-reinforcing dynamism is expected to gain currency in near-future so that Mobile Banking services could soon advance to a standard product - on the lines of Online Banking - offered by more or less each and every bank

Usefulness and Applications of Data Mining in Extracting Information from Different Perspectives

Discusses the concept of data mining, its applications, benefits and the standard tasks involved in the process are explained. Such pattern-seeking techniques usually performed with a wide range of related areas (viz. statistics, neural networks, genetic algorithms, machine learning, pattern recognition, knowledge-based systems, etc.) are described. Also focuses on bibliomining opportunities to be useful to information retrieval, semantic analysis of unstructured texts, web-usage mining and to make proactive as well as knowledge-driven decision across library services Suggests use of data mining in combination with other techniques of evaluation, exploiting large data warehouses by skilled specialists, and advises for ethical uses without privacy invasion

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference