Risk management in CRM security management

In an increasing competitive world, marketing survival can be depended simply on timely new information on customers and market trend. One of the most important strategies in CRM (Customer Relationship Management) is to capture enough information from customers and using this information carefully [Ryals , Tinsley]. Of course security of this information is very important in CRM data management [Bryan]. Data management is a method for scheduling and controlling data saving, recovering and processing. This activity has been done continually or periodically[Bryan]. Security level of this information depends on the security policy of the organization. CRM security policy is the directives and practices for managing, protecting and distributing assets which are included sensitive information, within an organization and its CRM systems[ISO/IEC TR 13335, ISO/IEC 17799, and BS7799]. CRM security policy is a high level plan that focuses on the strategic security methodology and is not limited to the guideline, standard or control way and plays a critical role in the defense of CRM systems and network [Barman, M.Amanda]. CRM risk evaluation is a method for increasing the efficiency of CRM security policy. In the manner that security threats and vulnerabilities against CRM is identified by its priority [Greenstein, Bryan, and ISO/IEC TR 13335]. First of all in this article, the importance of risk management in CRM is found out and then the suggested method of security risk management is introduced

Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire : Application for General Practitioner (GP) Classification in Northamptonshire

This paper presents a novel analytical approach for improving patients' experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as "kind", "exactly", "discharged", "long waits", "impolite staff", "worse", "problem", "happy", "late" and "excellent" were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS' rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients' experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback

Fraud Detection in Supply Chain with Machine Learning

A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results