Survivor Curve Selection and Customer Relationship Value

2007 ◽  
Vol 26 (4) ◽  
pp. 102-105
Author(s):  
Richard K. Ellsworth
Keyword(s):  
Customer Relationship ◽  
Attrition Rate ◽  
Statistical Techniques ◽  
Age Cohorts ◽  
Rate Analysis ◽  
Relationship Value ◽  
Rate Characteristic ◽  
Curve Selection ◽  
Survivor Curve ◽  
Attrition Analysis

Abstract The valuation profession employs a variety of statistical techniques, from simple attrition analysis to more complex retirement rate analysis, to assess customer population life characteristics. For many customer populations, the account survival characteristics are assumed to be constant across the various age cohorts of the population such that customer relationship value is indifferent to account age. However, relaxation of the constant attrition rate characteristic can have a direct influence on the magnitude of the value ascribed to the customer relationship value.

Beyond Attrition Analysis for Estimating Customer Relationship Remaining Life

2008 ◽  
Vol 27 (2) ◽  
pp. 85-89
Author(s):  
Richard K. Ellsworth
Keyword(s):  
Customer Relationship ◽  
Attrition Rate ◽  
Remaining Life ◽  
Simple Method ◽  
Attrition Analysis

Abstract Attrition analysis is recognized by valuation professionals as a simple method to estimate customer population life characteristics. With attrition analysis, customer retirement rates are assumed to be constant across the age vintages of the population. However, many customer populations exhibit irregular retirement rates across population age vintages such that relaxation of the constant attrition rate assumption alters the population remaining life characteristics.

Measuring Customer Relationship Value: The Role of Switching Cost

2006 ◽  
Author(s):  
Shane S. Dikolli ◽  
William R. Kinney, Jr. ◽  
Karen L. Sedatole
Keyword(s):  
Switching Cost ◽  
Customer Relationship ◽  
Relationship Value

Attrition of human trafficking and slavery cases through the Australian criminal justice system

2021 ◽  
Author(s):  
Samantha Lyneham
Keyword(s):  
Criminal Justice ◽  
Criminal Justice System ◽  
Human Trafficking ◽  
Justice System ◽  
Attrition Rate ◽  
Attrition Analysis

Police and prosecutors face a range of challenges while investigating, prosecuting and, ultimately, attempting to secure a conviction for human trafficking and slavery offences in Australia. In this study, investigation and prosecution data were analysed to chart the progression of matters and identify reasons for attrition. Analysis revealed an overall prosecution attrition rate of 73 percent. Attrition was most evident during the initial phases of prosecution, when the decision to lay charges was being considered. However, there was a 60 percent chance of conviction as a result of the defendant either pleading or being found guilty. Defendants were more likely to be convicted for ancillary charges (eg migration offences) than the most serious charges of human trafficking and slavery. The prosecution attrition rate for the most serious charges was 80 percent, compared to 54 percent for lesser charges.

scholarly journals Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction

2021 ◽  
Vol 29 (6) ◽  
pp. 1-29
Author(s):  
Praveen Ranjan Srivastava ◽  
Prajwal Eachempati
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Human Resource ◽  
Employee Retention ◽  
Attrition Rate ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Churn Prediction ◽  
Rate Analysis ◽  
Retention System

The paper aims to examine the factors that influence employee attrition rate using the employee records dataset from kaggle.com. It also aims to establish the predictive power of Deep Learning for employee churn prediction over ensemble machine learning techniques like Random Forest and Gradient Boosting on real-time employee data from a mid-sized Fast-Moving Consumer Goods (FMCG) company. The results are further validated through a regression model and also by a multi-criteria Fuzzy Analytical Hierarchy Process (AHP) model which takes into account the relative variable importance and computes weights. The empirical results of the machine learning models indicate that Deep Neural Networks (91.2% accuracy) are a better predictor of churn than Random Forest and Gradient Boosting Algorithm (82.3% and 85.2% respectively). These findings provide useful insights for human resource (HR) managers in an organizational workplace context. The model when recalibrated by the human resource team of organizations helps in better incentivization and employee retention.

Positive trends in neurosurgery enrollment and attrition: analysis of the 2000–2009 female neurosurgery resident cohort

2016 ◽  
Vol 124 (3) ◽  
pp. 834-839 ◽  
Author(s):  
Jaclyn J. Renfrow ◽  
Analiz Rodriguez ◽  
Ann Liu ◽  
Julie G. Pilitsis ◽  
Uzma Samadani ◽  
...  
Keyword(s):  
Career Paths ◽  
Primary Outcome ◽  
American Association ◽  
Training Programs ◽  
Female Gender ◽  
Attrition Rate ◽  
Attrition Rates ◽  
Residency Programs ◽  
Neurological Surgery ◽  
Attrition Analysis

OBJECT Women compose a minority of neurosurgery residents, averaging just over 10% of matched applicants per year during this decade. A recent review by Lynch et al. raises the concern that women may be at a higher risk than men for attrition, based on analysis of a cohort matched between 1990 and 1999. This manuscript aims to characterize the trends in enrollment, attrition, and postattrition careers for women who matched in neurosurgery between 2000 and 2009. METHODS Databases from the American Association of Neurological Surgeons (AANS) and the American Board of Neurological Surgery (ABNS) were analyzed for all residents who matched into neurosurgery during the years 2000–2009. Residents were sorted by female gender, matched against graduation records, and if graduation was not reported from neurosurgery residency programs, an Internet search was used to determine the residents’ alternative path. The primary outcome was to determine the number of women residents who did not complete neurosurgery training programs during 2000–2009. Secondary outcomes included the total number of women who matched into neurosurgery per year, year in training in which attrition occurred, and alternative career paths that these women chose to pursue. RESULTS Women comprised 240 of 1992 (12%) matched neurosurgery residents during 2000–2009. Among female residents there was a 17% attrition rate, compared with a 5.3% male attrition rate, with an overall attrition rate of 6.7%. The majority who left the field did so within the first 3 years of neurosurgical training and stayed in medicine—pursuing anesthesia, neurology, and radiology. CONCLUSIONS Although the percentage of women entering neurosurgical residency has continued to increase, this number is still disproportionate to the overall number of women in medicine. The female attrition rate in neurosurgery in the 2000–2009 cohort is comparable to that of the other surgical specialties, but for neurosurgery, there is disparity between the male and female attrition rates. Women who left the field tended to stay within medicine and usually pursued a neuroscience-related career. Given the need for talented women to pursue neurosurgery and the increasing numbers of women matching annually, the recruitment and retention of women in neurosurgery should be benchmarked and assessed.

Organizational structure, innovation performance and customer relationship value in the Greek advertising and media industry

2017 ◽  
Vol 32 (3) ◽  
pp. 385-397 ◽  
Author(s):  
Paraskevi Dekoulou ◽  
Panagiotis Trivellas
Keyword(s):  
Organizational Structure ◽  
Financial Performance ◽  
Innovation Performance ◽  
Customer Relationship ◽  
Media Industry ◽  
Direct Supervision ◽  
Content Type ◽  
Financial Outcomes ◽  
Relationship Value ◽  
Business Customers

Purpose This paper aims to explore the impact of organizational structure dimensions on innovation performance as well as its implications on business customers’ relationship value and financial performance in the business-to-business (B2B) market of the Greek advertising and media industry. Design/methodology/approach Based on a sample of 180 executives, who are at the helm of 163 Greek advertising and media organizations, the authors apply the partial least square method to test the association of organizational structure with innovation performance, business customers’ relationship value and financial outcomes. Findings Findings have brought to light that training boosts organization’s capacity to innovate, whereas direct supervision as a coordination mechanism significantly restricts this capacity. Innovation performance in the advertising B2B market fosters business customers’ relationship value and financial performance, while financial outcomes are also beneficially affected by profitable relationships with customer relationship value. Practical implications Because of the dramatic decline in their profitability caused by the economic crisis in the past five years, Greek advertising and media companies are threatened with extinction; thus, they are required to enhance their effectiveness through the adoption of a more innovation-oriented structure. Thus, managers should facilitate structures supporting training and delimiting direct supervision to foster the development of a competitive advantage built on innovation, creativity and business clients’ relationship. Originality/value This study contributes to the existing relationship marketing literature because it introduced Mintzberg’s typology to measure organizational structure and led to the diagnosis of the associations between different dimensions of organizational structure and various aspects of performance in the media and advertising industry, revealing the partial mediating role of customer relationship value between innovation and financial performance in the B2B market.

scholarly journals Intelligent Employee Retention System for Attrition Rate Analysis and Churn Prediction

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Human Resource ◽  
Employee Retention ◽  
Attrition Rate ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Churn Prediction ◽  
Rate Analysis ◽  
Retention System

The paper aims to examine the factors that influence employee attrition rate using the employee records dataset from kaggle.com. It also aims to establish the predictive power of Deep Learning for employee churn prediction over ensemble machine learning techniques like Random Forest and Gradient Boosting on real-time employee data from a mid-sized Fast-Moving Consumer Goods (FMCG) company. The results are further validated through a regression model and also by a multi-criteria Fuzzy Analytical Hierarchy Process (AHP) model which takes into account the relative variable importance and computes weights. The empirical results of the machine learning models indicate that Deep Neural Networks (91.2% accuracy) are a better predictor of churn than Random Forest and Gradient Boosting Algorithm (82.3% and 85.2% respectively). These findings provide useful insights for human resource (HR) managers in an organizational workplace context. The model when recalibrated by the human resource team of organizations helps in better incentivization and employee retention.

Measuring Customer Relationship Value: The Role of Switching Cost*

2007 ◽  
Vol 24 (1) ◽  
pp. 93-132 ◽  
Author(s):  
Shane S. Dikolli ◽  
William R. Kinney ◽  
Karen L. Sedatole
Keyword(s):  
Switching Cost ◽  
Customer Relationship ◽  
Relationship Value

scholarly journals Employee Attrition Dashboard Using Tableau

2021 ◽  
Vol 9 (9) ◽  
pp. 1696-1703
Author(s):  
Kovuri Sanjana
Keyword(s):  
Data Visualization ◽  
Business Intelligence ◽  
Employee Turnover ◽  
Crucial Role ◽  
Attrition Rate ◽  
Visualization Tool ◽  
Job Roles ◽  
Effective System ◽  
Employee Attrition ◽  
Attrition Analysis

Abstract: Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data in a very easily understandable format. It also allows non-technical users to create customized dashboards. The purpose of this paper is to visualize and analyse the employee attrition rate using the Tableau visualization tool by considering various important factors that play crucial role in affecting the attrition rate. In order to visualize and predict the attrition rate of employees of an organization, we proposed an intelligent, flexible and effective system that helps the managers to identify the valuable employee and try to retain them. The attrition rate can be analysed based on various factors such as job roles, years since last promotion, gender and number of companies worked. Keywords: Employee Attrition, Tableau, Attrition Analysis, Visualization, Employee Turnover, Dashboards

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