scholarly journals Examining Selected Theoretical Distributions of Life Expectancy to Analyse Customer Loyalty Durability. The Case of a European Retail Bank

2020 ◽  
Vol 4 (349) ◽  
pp. 81-92
Author(s):  
Dominik Kubacki ◽  
Robert Kubacki
Keyword(s):  
Survival Analysis ◽  
Customer Loyalty ◽  
Cox Model ◽  
Survival Function ◽  
Parametric Models ◽  
Explanatory Variables ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Common Problems ◽  
Log Normal

One of the key elements related to calculating Customer Lifetime Value is to estimate the duration of a client’s relationship with a bank in the future. This can be done using survival analysis. The aim of the article is to examine which of the known distributions used in survival analysis (Weibull, Exponential, Gamma, Log‑normal) best describes the churn phenomenon of a bank’s clients. If the aim is to estimate the distribution according to which certain units (bank customers) survive and the factors that cause this are not so important, then parametric models can be used. Estimation of survival function parameters is faster than estimating a full Cox model with a properly selected set of explanatory variables. The authors used censored data from a retail bank for the study. The article also draws attention to the most common problems related to preparing data for survival analysis.

Survival analysis

2020 ◽  
pp. 181-218
Author(s):  
Bendix Carstensen
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cox Model ◽  
Survival Function ◽  
Event Analysis ◽  
Time To Event ◽  
Poisson Models ◽  
Kaplan Meier ◽  
Analysis Survival ◽  
Event Rates

This chapter describes survival analysis. Survival analysis concerns data where the outcome is a length of time, namely the time from inclusion in the study (such as diagnosis of some disease) till death or some other event — hence the term 'time to event analysis', which is also used. There are two primary targets normally addressed in survival analysis: survival probabilities and event rates. The chapter then looks at the life table estimator of survival function and the Kaplan–Meier estimator of survival. It also considers the Cox model and its relationship with Poisson models, as well as the Fine–Gray approach to competing risks.

scholarly journals Switching Costs and Customer Loyalty: A Review of literature

2021 ◽  
Vol 10 (05) ◽  
pp. 7-14
Author(s):  
Tawseef Ahmad Ganaie ◽  
Mushtaq Ahmad Bhat
Keyword(s):  
Customer Loyalty ◽  
Word Of Mouth ◽  
Switching Costs ◽  
Business Activity ◽  
Review Of Literature ◽  
Customer Base ◽  
Customer Lifetime ◽  
Loyal Customer ◽  
Lifetime Value ◽  
Free Word

Retaining customers in a competitive-value driven era is seen as a herculean task. Retaining customers for a longer time and reaping the benefit of customer lifetime value is seen as a major business activity that has a manifold influence on profitability, customer base, share of wallet, free word of mouth besides customer suggestions and feedback. In this direction, switching costs play an important role in influencing the customer loyalty. Switching cost acts as switching barrier which forces the customer to remain with the existing company and strengthens the customer-company longevity relationship. In view of the growing importance of switching costs and its influence on customer loyalty, this paper attempts to review switching costs and customer loyalty with a view to make switching costs barrier an effective tool to maintain a loyal customer base.

scholarly journals IPTEKS PERHITUNGAN COSTUMER LIFETIME VALUE

2020 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Hans Lohonauman
Keyword(s):  
Customer Loyalty ◽  
Customer Lifetime Value ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Good Attitude

Everyone has a goal to make high profitsin business,. Therefore, every entrepreneur requires value that need to be maintained. This value gives a good attitude to the customers to build a long-term relationship. One of the factors that determine the success of a business is the customers. Customer lifetime value should be noticed by the entrepreneur since it becomes a ways to measure customer's profitability, to analyze marketing, and it is also can be used to create a mindset in running a business. Customer Lifetime Value (CLV) also can create the customer loyalty which can impact for profitability.

scholarly journals A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement

2019 ◽  
Vol 39 (8) ◽  
pp. 899-909 ◽  
Author(s):  
Helen Bell Gorrod ◽  
Ben Kearns ◽  
John Stevens ◽  
Praveen Thokala ◽  
Alexander Labeit ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Model Selection ◽  
Proportional Hazards ◽  
Survival Function ◽  
Progression Free Survival ◽  
Parametric Models ◽  
Survival Functions ◽  
Cancer Treatments ◽  
Hazard Functions ◽  
Support Unit

Objectives. In June 2011, the National Institute for Health and Care Excellence (NICE) Decision Support Unit published a Technical Support Document (TSD) providing recommendations on survival analysis for NICE technology appraisals (TAs). Survival analysis outputs are influential inputs into economic models estimating the cost-effectiveness of new cancer treatments. Hence, it is important that systematic and justifiable model selection approaches are used. This study investigates the extent to which the TSD recommendations have been followed since its publication. Methods. We reviewed NICE cancer TAs completed between July 2011 and July 2017. Information on survival analyses undertaken and associated critiques for overall survival (OS) and progression-free survival were extracted from the company submissions, Evidence Review Group (ERG) reports, and final appraisal determination documents. Results. Information was extracted from 58 TAs. Only 4 (7%) followed all TSD recommendations for OS outcomes. The vast majority (91%) compared a range of common parametric models and assessed their fit to the data (86%). Only a minority of TAs included an assessment of the shape of the hazard function (38%) or proportional hazards assumption (40%). Validation of the extrapolated portion of the survival function using external data was attempted in a minority of TAs (40%). Extrapolated survival functions were frequently criticized by ERGs (71%). Conclusions. Survival analysis within NICE TAs remains suboptimal, despite publication of the TSD. Model selection is not undertaken in a systematic way, resulting in inconsistencies between TAs. More attention needs to be given to assessing hazard functions and validation of extrapolated survival functions. Novel methods not described in the TSD have been used, particularly in the context of immuno-oncology, suggesting that an updated TSD may be of value.

scholarly journals Measure of CLV Toward Market Segmentation Approach in the Telecommunication Sector (Vietnam)

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110215
Author(s):  
Tu Van Binh ◽  
Ngo Giang Thy ◽  
Ho Thi Nam Phuong
Keyword(s):  
Survival Function ◽  
Telecommunications Industry ◽  
Telecommunication Sector ◽  
Customer Lifetime ◽  
Market Competitiveness ◽  
Important Message ◽  
Lifetime Value ◽  
Segmentation Approach ◽  
New Finding ◽  
The Individual

This study is based on data used by a telecommunications industry over a period of 6 months, beginning July 1, 2018, until December 31, 2018. The approached model of customer lifetime value (CLV) was concerned on the sample size of 245,355 records made by cell phones of active prepaid subscribers. The aim of this article is to estimate CLV through survival function with adjusted variables enclosed, for example, marketing cost. To do this, a statistical model was used to predict the individual CLV and confirmed a different finding to some previous study, in which this new finding brings an important message that the telecom providers should take care of customer earlier before too late because after the 10th of tenure, the subscribers can churn the telecom operator’s service. The result of CLV is well supported to segment market. It will also benefit the telecom providers to maintain their market competitiveness.

Can Non-tiered Customer Loyalty Programs Be Profitable?

2021 ◽  
Author(s):  
Arun Gopalakrishnan ◽  
Zhenling Jiang ◽  
Yulia Nevskaya ◽  
Raphael Thomadsen
Keyword(s):  
Customer Loyalty ◽  
Customer Retention ◽  
Customer Lifetime Value ◽  
Loyalty Programs ◽  
Loyalty Program ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Customer Loyalty Programs

We show that a simple, nontiered loyalty program can substantially increase customer lifetime value and that most of this benefit comes from increasing customer retention.

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