Using Business Intelligence for Operational Decision-Making in Call Centers

2012 ◽  
Vol 4 (1) ◽  
pp. 43-54
Author(s):  
Eric Kyper ◽  
Michael Douglas ◽  
Roger Blake
Keyword(s):  
Business Intelligence ◽  
Call Center ◽  
Call Centers ◽  
Insurance Company ◽  
Service Levels ◽  
Factors Affecting ◽  
Intelligence System ◽  
Operational Decision Making ◽  
Using Data ◽  
Window Approach

This paper proposes an operational business intelligence system for call centers. Using data collected from a large U.S. insurance company, the authors demonstrate a decision tree based solution to help the company achieve excellence through improved service levels. The initial results from this study provide insight into the factors affecting this firm’s call center service levels, and the solution developed in this paper provides two distinct advantages to managers. First, it enables them to identify key factors and the role they play in determining service levels. Second, a sliding window approach is proposed which allows managers to see the effects of resource reallocation on service levels on an on-going basis.

scholarly journals Gelukkig in het elektronisch panopticon?

2013 ◽  
Vol 29 (2) ◽  
Author(s):  
Roel Schouteten ◽  
Jos Benders ◽  
Els van den Bosch
Keyword(s):  
Control Systems ◽  
Call Center ◽  
Call Centers ◽  
Document Analysis ◽  
Management Control ◽  
Labor Process ◽  
Insurance Company ◽  
House Call ◽  
Employee Reactions ◽  
Intensive Control

Happiness in the Digital panopticum Happiness in the Digital panopticum Employees are widely believed to perceive management control as negative. Labor-process researchers have discussed employee reactions to control regimes in terms of resistance. Yet, do employees always resist? Could control even be welcomed? A call center is an appropriate environment for answering these questions. Because of its intensive control systems, call centers have been labeled as ‘electronic panopticon’. Through controlled interviews, observation and document analysis, the opinions of call center agents regarding intensive control were studied in an in-house call center of a Dutch insurance company. Our research concluded that it is not possible to generalize respondent perception to management control as always negative; we therefore distinguished three types of perception regarding control: opponents (the ‘unwilling’), proponents (the ‘willing’) and disinterested (the ‘indifferent’). We conclude with recommendations for praxis.

scholarly journals Application of queueing models with abandonment for Call Center congestion analysis

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Sidney Carlos Ferrari ◽  
Reinaldo Morabito
Keyword(s):  
Call Center ◽  
Call Centers ◽  
Queueing Systems ◽  
Minimum Cost ◽  
Service Organizations ◽  
Analytical Models ◽  
Queueing Models ◽  
Phone Calls ◽  
Using Data ◽  
Mixed Distributions

Abstract This paper studies and applies queueing systems to Call Centers regarding the possibility of customer abandonment from the system before being served due to their impatience in waiting for a service. Call Centers are service organizations that predominantly serve customers via phone calls. One of the main concerns in managing them is to provide quality service at a minimum cost. Noticing the quality of services offered is expressed by customers, for example by abandonment from the queue. This paper shows that the M/M/c+G analytical queueing models with abandonment, with patience time represented by generic distributions (particularly mixed distributions), are more effective than the M/M/c+M analytical queueing models with abandonment, with Exponential patience, commonly used to evaluate congestion problems in Call Centers and support sizing and operational decisions in these systems. We conducted a study using data extracted from a Bank Call Center located in Israel and the parameters and some performance measures are determined based on this data. These sampling measures are compared with the same measures achieved by the M/M/c+M and M/M/c+G analytical queueing models considered in this research, which use parameters obtained empirically and the mixed and non-mixed distributions based on Exponential and Lognormal to represent user patience. An experimental discrete simulation model was also used to explore an alternative scenario, showing the potential of using the approaches based on analytical models with abandonment for Call Center analysis.

Business Intelligence for Healthcare

2016 ◽  
pp. 2146-2170
Author(s):  
Jack S. Cook ◽  
Pamela A. Neely
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Management Process ◽  
Insurance Company ◽  
Process Data ◽  
Study Approach ◽  
Intelligence System ◽  
Revenue Cycle

Using an interpretive case study approach, this chapter describes the data quality problems in two companies: (1) a Multi-Facility Healthcare Medical Group (MHMG), and (2) a Regional Health Insurance Company (RHIS). These two interpretive cases examine two different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the issues examined are MHMG's revenue cycle management and RHIS's provider enrollment and credentialing process. A Data and Information Quality (DIQ) assessment of the revenue cycle management process demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information-intensive environment. Based on the assessment of the revenue cycle management process, data quality problems associated with the key processes and their implications for the healthcare organization are described. This chapter provides recommendations for DIQ best practices and illustrates these best practices within this real world context of healthcare.

Business Intelligence for Healthcare

2014 ◽  
pp. 88-111
Author(s):  
Jack S. Cook ◽  
Pamela A. Neely
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Management Process ◽  
Insurance Company ◽  
Process Data ◽  
Study Approach ◽  
Intelligence System ◽  
Revenue Cycle

Using an interpretive case study approach, this chapter describes the data quality problems in two companies: (1) a Multi-Facility Healthcare Medical Group (MHMG), and (2) a Regional Health Insurance Company (RHIS). These two interpretive cases examine two different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the issues examined are MHMG’s revenue cycle management and RHIS’s provider enrollment and credentialing process. A Data and Information Quality (DIQ) assessment of the revenue cycle management process demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information-intensive environment. Based on the assessment of the revenue cycle management process, data quality problems associated with the key processes and their implications for the healthcare organization are described. This chapter provides recommendations for DIQ best practices and illustrates these best practices within this real world context of healthcare.

Business Intelligence for Healthcare

2016 ◽  
pp. 1506-1529
Author(s):  
Jack S. Cook ◽  
Pamela A. Neely
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Management Process ◽  
Insurance Company ◽  
Process Data ◽  
Study Approach ◽  
Intelligence System ◽  
Revenue Cycle

Using an interpretive case study approach, this chapter describes the data quality problems in two companies: (1) a Multi-Facility Healthcare Medical Group (MHMG), and (2) a Regional Health Insurance Company (RHIS). These two interpretive cases examine two different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the issues examined are MHMG's revenue cycle management and RHIS's provider enrollment and credentialing process. A Data and Information Quality (DIQ) assessment of the revenue cycle management process demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information-intensive environment. Based on the assessment of the revenue cycle management process, data quality problems associated with the key processes and their implications for the healthcare organization are described. This chapter provides recommendations for DIQ best practices and illustrates these best practices within this real world context of healthcare.

scholarly journals Academic data warehouse design using a hybrid methodology

2015 ◽  
Vol 12 (1) ◽  
pp. 135-160 ◽  
Author(s):  
Tria Di ◽  
Ezio Lefons ◽  
Filippo Tangorra
Keyword(s):  
Data Warehouse ◽  
Design Process ◽  
Business Intelligence ◽  
Quality Level ◽  
Academic Context ◽  
Factors Affecting ◽  
Academic Organizations ◽  
Intelligence System ◽  
Ontological Approach ◽  
Hybrid Methodology

In the last years, data warehousing has got attention from Universities which are now adopting business intelligence solutions in order to analyze crucial aspects of the academic context. In this paper, we present the architecture of a Business Intelligence system for academic organizations. Then, we illustrate the design process of the data warehouse devoted to the analysis of the main factors affecting the importance and the quality level of every University, such as the evaluation of the Research and the Didactics. The design process we describe is based on a hybrid methodology that is largely automatic and relies on an ontological approach for the integration of the different data sources.

The Analysis of Factors Affecting College Choice by Using Tableau

2018 ◽  
Vol 6 (9) ◽  
pp. 100
Author(s):  
Harika Devi Kotha ◽  
V Mnssvkr Gupta
Keyword(s):  
College Choice ◽  
Data Visualization ◽  
Business Intelligence ◽  
Visualization Tool ◽  
Data Discovery ◽  
Efficient Manner ◽  
Factors Affecting ◽  
The People ◽  
Using Data

Business depends on Data. Now a days we are dealing with large amounts of data, like petabytes. IT teams receive continuous requests from the users in the issues related to handling data. so the key objective now is, how we are going to handle data in an efficient manner , and how to represent that data in an understandable format. One of the available solution is using data visualization- an art of presenting the data in a manner that even a non-analyst can understand[1] . The most popular tools for visualizations / data discovery are Qlikview and Tableau. In this paper we are going to focus on One of the fastest evolving Business Intelligence (BI) and data visualization tool is Tableau . In short, Tableau - helps the people see and understand their data. In this paper we are going to introduce about Tableau and represent an organizational data by using this Tableau , Then we will focus on creating views and analysis of data.

Implementation System of Business Intelligence System In The Company

2017 ◽  
Vol 1 (2) ◽  
Author(s):  
Abdul Hamid Arribathi ◽  
Maimunah Maimunah ◽  
Devi Nurfitriani
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Research Library ◽  
Decision Making Process ◽  
Intelligence System ◽  
Business Intelligence Systems

This study aims to determine the stages that must be implemented in building a Business Intelligence System structured and appropriate in building Business Intelligence Systems in an organization, and understand the important aspects that must be considered for investment development Business Intelligence System is increasing. Business must be based on the conditions and needs of the organization in achieving the desired goals. If these conditions occur, then the decision-making process will be better and more accurate. The purpose of this study is to determine the important aspects that must be understood and prepared in using the Business Intelligence System in an organization. The method used is the explanation as well as the research library of several books, articles and other literature.

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