Government Call Center Service Improvement Through Unstructured Data Analytics

For many companies, customer service is summarised in the following activities: responding to customer questions, receives commands, resolve disagreements about billing and handle claims. The service can be made directly at the customer’s house, at the supplier or through the media (telephone, fax, email, Internet, etc.) The increasing number of call centres demonstrates the importance that companies attach to customer service.

An empirical study was conducted to namely to investigate customer service in selected Hotels in Downtown Area, with the aim to establish criteria for excellent customer service as a benchmark for establishing relationships with customers, by means

My efforts of implementing the first visitor management system in the Seattle Office portrayed various aspects of my efforts related to client service. One of the focus areas is guiding the decisions and working with the Seattle Executive Team to understanding and planning for the least impact the system has on the working Attorney. The other aspect of client services during this implementation is the support of the Seattle Executive Team in supporting and providing the training, education, understanding and awareness of why this control was necessary.

This paper discusses the results from a customer relation assignment which was designed to create knowledge through observation of three different healthcare service organizations. This technique provided ample amounts of information on how different healthcare facilities handled costumer relations. The paper is written in a first person experience and covers the following information such as type of the organization and services provided at each of them. Then the paper will continue to explain the process required to obtain information and the willingness to offer information from the organizations. Further the paper will divulge my feelings regarding customer service at each facility and what changes I would have made in order to make

Descriptive statistics will be used to present summaries of data collected when necessary. Inferential statistics will be used to test the research

Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. We will find out a way how structured big data can be transformed into unstructured data to increase the performance. Storage price trends have shown that now a days it’s not a big deal to afford storage for big un structured data. As far as performance is concerned, big data manageability in terms of unstructured data is more efficient. So as far as revenue is concerned this research will provide

The hot topic around government usage of big data has been centered on meta-data crawlers. The meta-data which, as its name suggests, is data about some particular data. An easy example is of a phone call where the data is single audio file of the phone call and the meta-data is group of many attributes such as caller, recipient, call time, call duration, call location through signal triangulation etc. Since it is evident that meta-data is very large in quantity than its parent data itself, thus making it a perfect domain for government agencies to apply the big data analytics2.

The obvious need for satisfying the firm’s customer is to expand the business, to gain a higher market share, and to acquire repeat and referral business, all of which lead to improved profitability (Barsky, 1992). Studies conducted by Cronin and Taylor (1992) in service sectors such as: banking, pest control, dry cleaning, and fast food; found that customer satisfaction has a significant effect on purchase intentions in all four sectors. Similarly, in the health-care sector, McAlexander et al. (1994) found that patient satisfaction and service quality have a significant effect on future purchase intentions.(Kandampully and Suhartanto,

Provide an approach for research efforts towards developing highly scalable and autonomic data management systems associated with programming models for processing Big Data. Aspects of such systems should address challenges related to data analysis algorithms, real-time processing and visualisation, context awareness, data management and performance and scalability, correlation and causality and to some extent, distributed storage [1]. Provide an approach for framework for evaluating big data initiatives [2]. Provide an approach for summarize opportunities and challenges with big data. Recent technological advances and novel applications, such as sensors, cyber-physical systems, smart mobile devices ,cloud systems, data analytics, and social networks, are making possible to capture, process, and share huge amounts of data – referred to as big data - and to extract useful knowledge, such as patterns, from this data and predict trends and events. Big data is making possible tasks that before were impossible, like preventing disease spreading and crime, personalizing healthcare, quickly identifying business opportunities, managing emergencies, protecting the homeland, and so on [3]. Provide an approach for sources of structured and unstructured big data. Unstructured data is everywhere. In fact, most individuals and organizations conduct their lives around unstructured data [4]. Successful decision-making will increasingly be driven by analytics-generated

Big data is a vast amount of information, both structured and unstructured, unstructured is information that is not easy to interpret by traditional databases and structure being the opposite, which is easy to interpret data made form text or image. Structured or unstructured, the main business of big data is categorized in to three

Abstract— Big Data is a new term used to describe a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. We present in this issue, a broad overview of the topic, its current status, and forecast to the future. We also introduce some articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.

In the last few years the global marketplace has seen exponential growth in data volume. Every day people create unstructured large datasets of different types such as GPS coordinates, payment transactions, web data, e-mails or smart meter values that are termed as "big data" \cite{nasscom}.

Customer Satisfaction is one of the main topics rises recently. Before years there was a revelation of a customer service to concentrate on customer, to treat with him in a better way, to please and fulfill his demands of the customer. In order to achieve that, researchers analyzed the customer population then took random samples and after that generalized the results and came with procedures to treat and please the customers. The first thing is spark in the satisfaction of the customer concept added to the private sector, later applied to the other sectors.

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

Big data is a term to explain large complex data set, and big data is challenging the traditional data handling method. The big data itself is useless, but after processed and analyzed the big data would generate valuable information. This article would discuss relevant technologies and areas in the big data age.