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Journal articles on the topic 'Lifetime value'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Park, Dae-Yun, and Shijin Yoo. "The Redemption Behavior of Loyalty Points and Customer Lifetime Value." Journal of the Korean Operations Research and Management Science Society 39, no. 3 (2014): 63–82. http://dx.doi.org/10.7737/jkorms.2014.39.3.063.

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2

Gupta, Sunil, Dominique Hanssens, Bruce Hardie, et al. "Modeling Customer Lifetime Value." Journal of Service Research 9, no. 2 (2006): 139–55. http://dx.doi.org/10.1177/1094670506293810.

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3

Winkel, Susanne. "Customer Lifetime Value (CLV)." Controlling 22, no. 4-5 (2010): 275–76. http://dx.doi.org/10.15358/0935-0381-2010-4-5-275.

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4

Borle, Sharad, Siddharth S. Singh, and Dipak C. Jain. "Customer Lifetime Value Measurement." Management Science 54, no. 1 (2008): 100–112. http://dx.doi.org/10.1287/mnsc.1070.0746.

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5

Stahl, Heinz K., Kurt Matzler, and Hans H. Hinterhuber. "Linking customer lifetime value with shareholder value." Industrial Marketing Management 32, no. 4 (2003): 267–79. http://dx.doi.org/10.1016/s0019-8501(02)00188-8.

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6

Berger, Paul D., Naras Eechambadi, Morris George, Donald R. Lehmann, Ross Rizley, and Rajkumar Venkatesan. "From Customer Lifetime Value to Shareholder Value." Journal of Service Research 9, no. 2 (2006): 156–67. http://dx.doi.org/10.1177/1094670506293569.

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7

Feiz, Setareh, Ali Ramezani Ghotbabadi, and Zainab Bte Khalifah. "Customer Lifetime Value in Organizations." Asian Journal of Research in Social Sciences and Humanities 6, no. 5 (2016): 53. http://dx.doi.org/10.5958/2249-7315.2016.00103.9.

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8

Lohonauman, Hans. "IPTEKS PERHITUNGAN COSTUMER LIFETIME VALUE." Jurnal Ipteks Akuntansi Bagi Masyarakat 4, no. 1 (2020): 19. http://dx.doi.org/10.32400/jiam.4.1.2020.29236.

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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.

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9

Chang, Wen, Chen Chang, and Qianpin Li. "Customer Lifetime Value: A Review." Social Behavior and Personality: an international journal 40, no. 7 (2012): 1057–64. http://dx.doi.org/10.2224/sbp.2012.40.7.1057.

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The concept of regarding customers as assets that should be managed and whose value should be measured is now accepted and recognized by academics and practitioners. This focus on customer relationship management makes it extremely important to understand customer lifetime value (CLV) because CLV models are an efficient and effective way to evaluate a firm's relationship with its customers. Assessment of CLV is especially important for firms in implementing customer-oriented services. In this paper we provide a critical review of the literature on the development process and applications of CLV.

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10

Souček, Martin, and Jana Turčínková. "Lifetime value in business process." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 59, no. 2 (2011): 291–98. http://dx.doi.org/10.11118/actaun201159020291.

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The paper focuses on lifetime value assessment and its implementation and application in business processes. The lifetime value is closely connected to customer relationship management. The paper presents results of three consecutive researches devoted to issues of customer relationship management. The first two from 2008 and 2010 were conducted as quantitative ones; the one from 2009 had qualitative nature. The respondents were representatives of particular companies. The means for data collection was provided by ReLa system. We will focus on individual attributes of lifetime value of a customer, and relate them to approaches of authors mentioned in introduction. Based on the qualitative research data, the paper focuses on individual customer lifetime value parameters. These parameters include: the cost to the customer relationship acquisition and maintenance, profit generated from a particular customer, customer awareness value, the level of preparedness to adopt new products, the value of references and customer loyalty level. For each of these parameters, the paper provides specific recommendations. Moreover, it is possible to learn about the nature of these parameter assessments in the Czech environment.

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11

Estrella-Ramón, A. M., M. Sánchez-Pérez, G. Swinnen, and K. VanHoof. "A marketing view of the customer value: Customer lifetime value and customer equity." South African Journal of Business Management 44, no. 4 (2013): 47–64. http://dx.doi.org/10.4102/sajbm.v44i4.168.

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Throughout this research the customer valuation trend in marketing is going to be reviewed, emphasizing Customer Lifetime Value and Customer Equity measures. The main theoretical contributions in the development and evolution of the Customer Lifetime Value concept are analysed. Customer Lifetime Value is also differentiated from Customer Equity and Customer Profitability analysis to estimate customer value in terms of firm profitability. Customer Lifetime Value and Customer Equity concepts are formally defined. Additionally, a classification of a set of published researches into Customer Lifetime Value and/or Customer Equity is developed. This classification has been posited according to several criteria that serves as a guide to key requirements for developing these types of models. Finally,several conclusions, suggestions and future research streams are highlighted.

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12

Mishra, Seeta. "Customer Lifetime Value in Telecommunication Industry." Review of Professional Management- A Journal of New Delhi Institute of Management 4, no. 1 (2006): 27. http://dx.doi.org/10.20968/rpm/2006/v4/i1/100994.

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13

Baumeister, Alexander, and Thomas Alt. "Customer Lifetime Value-Berechnung beiMarkov-Prozessen." WiSt - Wirtschaftswissenschaftliches Studium 39, no. 10 (2010): 521–28. http://dx.doi.org/10.15358/0340-1650-2010-10-521.

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14

Nikkhahan, B., and M. J. Tarokh. "Citizen Lifetime Value in E-Government." International Journal of Customer Relationship Marketing and Management 2, no. 3 (2011): 26–41. http://dx.doi.org/10.4018/jcrmm.2011070102.

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Customer Lifetime Value (CLV) is one of the most important measures of valuing the customers in private sector. CLV calculates customer contribution in the profits of organization. In this paper Citizen Lifetime Value (CzLV) is introduced to measure the financial value of citizens for the government. CzLV evaluates citizen contribution in cost reduction of the organization. This measure can be calculated based on past behavior of citizens in using the service and cost reduction of using online services rather than offline ones. Logistic regression is employed as a data mining technique to predict future use of online services by citizens. A service of Tehran.ir called “137,” one of the most important portals of Iran’s E-government, is considered as a case study. CzLV for the citizens of this service is calculated and four citizen segments are specified. Then each segment is evaluated based on different characteristic of citizens, and suitable strategies are presented to build more financial values for the organization.

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15

Ching, W.-K., M. K. Ng, K.-K. Wong, and E. Altman. "Customer lifetime value: stochastic optimization approach." Journal of the Operational Research Society 55, no. 8 (2004): 860–68. http://dx.doi.org/10.1057/palgrave.jors.2601755.

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16

Mason, Charlotte H. "Tuscan lifestyles: Assessing customer lifetime value." Journal of Interactive Marketing 17, no. 4 (2003): 54–60. http://dx.doi.org/10.1002/dir.10066.

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17

Malthouse, Edward C., and Robert C. Blattberg. "Can we predict customer lifetime value?" Journal of Interactive Marketing 19, no. 1 (2005): 2–16. http://dx.doi.org/10.1002/dir.20027.

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18

Sargeant, Adrian. "Donor lifetime value: An empirical analysis." International Journal of Nonprofit and Voluntary Sector Marketing 3, no. 4 (1998): 283–92. http://dx.doi.org/10.1002/nvsm.6090030403.

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19

Gupta, Sunil, and Donald R. Lehmann. "Customer Lifetime Value and Firm Valuation." Journal of Relationship Marketing 5, no. 2-3 (2006): 87–110. http://dx.doi.org/10.1300/j366v05n02_06.

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20

Glady, Nicolas, Bart Baesens, and Christophe Croux. "Modeling churn using customer lifetime value." European Journal of Operational Research 197, no. 1 (2009): 402–11. http://dx.doi.org/10.1016/j.ejor.2008.06.027.

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21

Coyle, A. J., and P. G. Taylor. "Tight bounds on the sensitivity of generalised semi-Markov processes with a single generally distributed lifetime." Journal of Applied Probability 32, no. 1 (1995): 63–73. http://dx.doi.org/10.2307/3214921.

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There are some generalised semi-Markov processes (GSMP) which are insensitive, that is the value of some performance measures for the system depend only on the mean value of lifetimes and not on their actual distribution. In most cases this is not true and a performance measure can take on a number of values depending on the lifetime distributions. In this paper we present a method for finding tight bounds on the sensitivity of performance measures for the class of GSMPs with a single generally distributed lifetime. Using this method we can find upper and lower bounds for the value of a function of the stationary distribution as the distribution of the general lifetime ranges over a set of distributions with fixed mean. The method is applied to find bounds on the average queue length of the Engset queue and the time congestion in the GI/M/n/n queueing system.

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22

Coyle, A. J., and P. G. Taylor. "Tight bounds on the sensitivity of generalised semi-Markov processes with a single generally distributed lifetime." Journal of Applied Probability 32, no. 01 (1995): 63–73. http://dx.doi.org/10.1017/s0021900200102578.

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There are some generalised semi-Markov processes (GSMP) which are insensitive, that is the value of some performance measures for the system depend only on the mean value of lifetimes and not on their actual distribution. In most cases this is not true and a performance measure can take on a number of values depending on the lifetime distributions. In this paper we present a method for finding tight bounds on the sensitivity of performance measures for the class of GSMPs with a single generally distributed lifetime. Using this method we can find upper and lower bounds for the value of a function of the stationary distribution as the distribution of the general lifetime ranges over a set of distributions with fixed mean. The method is applied to find bounds on the average queue length of the Engset queue and the time congestion in theGI/M/n/nqueueing system.

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23

DOBROLIUBOV, M. I., S. N. GNINENKO, A. YU. IGNATIEV, and V. A. MATVEEV. "ORTHOPOSITRONIUM LIFETIME PROBLEM." International Journal of Modern Physics A 08, no. 17 (1993): 2859–74. http://dx.doi.org/10.1142/s0217751x93001156.

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We review the present status of the problem of the six standard deviation discrepancy between the theoretical and experimental values of the orthopositronium lifetime. We discuss possible ways of its explanation and show that those invoking new decay modes are apparently closed. So the solution of this problem most probably lies either in large numerical value of the next, yet uncalculated, coefficient in the perturbative expansion of the orthopositronium decay width or in shortcomings of the standard treatment of the bound state problem in quantum field theory.

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24

Kumar, V. "Customer Lifetime Value – The Path to Profitability." Foundations and Trends® in Marketing 2, no. 1 (2007): 1–96. http://dx.doi.org/10.1561/1700000004.

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25

Baumeister, Alexander, and Thomas Alt. "Customer Lifetime Value-Berechnung bei Markov-Prozessen." WiSt - Wirtschaftswissenschaftliches Studium 39, no. 9 (2010): 471–76. http://dx.doi.org/10.15358/0340-1650-2010-9-471.

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26

Sridhar, Ashok, and Michael Corbey. "Customer Profitability Analyses and Customer Lifetime Value." Maandblad Voor Accountancy en Bedrijfseconomie 89, no. 7/8 (2015): 265–73. http://dx.doi.org/10.5117/mab.89.31335.

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The main objective of this paper is to compare two key approaches in the !eld of Customer Accounting (CA), namely Customer Pro!tability Analysis (CPA) and Customer Lifetime Value (CLV). While CPA is a retrospective analysis of past accruals that represent the results of doing business with a customer over a certain, mostly single-period of time, CLV is a predictive measure of future customer-related cash "ows over a certain (multi-)period of time. This paper draws on the state-ofthe- art knowledge in the Customer Accounting (CA) literature to identify the impacts of CPA and CLV on managerial decision-making. It also offers recommendations as to the scenarios in which these metrics should be deployed in order to arrive at meaningful managerial decisions, and highlights their collective limitations.

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27

Emokiniovo, Aganbi Victor. "Building Consumer Lifetime Value through Brand Communications." Singaporean Journal of Business Economics and Management Studies 5, no. 12 (2017): 22–29. http://dx.doi.org/10.12816/0039978.

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28

Berger, Paul D., and Nada I. Nasr. "Customer lifetime value: Marketing models and applications." Journal of Interactive Marketing 12, no. 1 (1998): 17–30. http://dx.doi.org/10.1002/(sici)1520-6653(199824)12:1<17::aid-dir3>3.0.co;2-k.

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29

Iwata, T., K. Saito, and T. Yamada. "Recommendation Method for Improving Customer Lifetime Value." IEEE Transactions on Knowledge and Data Engineering 20, no. 9 (2008): 1254–63. http://dx.doi.org/10.1109/tkde.2008.55.

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30

Heitz, Christoph, Marcel Dettling, and Andreas Ruckstuhl. "Modelling customer lifetime value in contractual settings." International Journal of Services Technology and Management 16, no. 2 (2011): 172. http://dx.doi.org/10.1504/ijstm.2011.042595.

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31

Heidemann, Julia, Nora Kamprath, and Quirin Görz. "Customer Lifetime Value: Entwicklungspfade, Einsatzpotenziale und Herausforderungen." Journal für Betriebswirtschaft 59, no. 4 (2009): 183–99. http://dx.doi.org/10.1007/s11301-009-0052-z.

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32

Crowder, Martin, David J. Hand, and Wojtek Krzanowski. "On optimal intervention for customer lifetime value." European Journal of Operational Research 183, no. 3 (2007): 1550–59. http://dx.doi.org/10.1016/j.ejor.2006.08.062.

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33

Ho, Teck-Hua, Young-Hoon Park, and Yong-Pin Zhou. "Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value." Marketing Science 25, no. 3 (2006): 260–77. http://dx.doi.org/10.1287/mksc.1050.0158.

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34

Singh, Siddharth S., Sharad Borle, and Dipak C. Jain. "A generalized framework for estimating customer lifetime value when customer lifetimes are not observed." Quantitative Marketing and Economics 7, no. 2 (2009): 181–205. http://dx.doi.org/10.1007/s11129-009-9065-0.

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35

Keeler, Emmett B. "The value of remaining lifetime is close to estimated values of life." Journal of Health Economics 20, no. 1 (2001): 141–43. http://dx.doi.org/10.1016/s0167-6296(00)00070-9.

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36

Sodemann, Harald. "Beyond Turnover Time: Constraining the Lifetime Distribution of Water Vapor from Simple and Complex Approaches." Journal of the Atmospheric Sciences 77, no. 2 (2020): 413–33. http://dx.doi.org/10.1175/jas-d-18-0336.1.

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Abstract The time water vapor spends in the atmosphere from evaporation to precipitation, termed here the water vapor lifetime, is of fundamental relevance for characterizing the water cycle, for the turnover of mass and energy, causes of precipitation extremes, and the recycling of precipitation over land. While the global average lifetime of water vapor is commonly considered as about 8–10 days, recent work indicates that the distribution of water vapor lifetimes is highly skewed, and that a large part of the water vapor could have average lifetimes of about 4–5 days. Besides calling for scrutiny of these new estimates, these findings also prompt an investigation of the factors shaping the distribution of the lifetime of water vapor. Using idealized setups and reanalysis data, I explore the influence of heterogeneity and nonstationarity on water vapor age and lifetime. The combination of nonstationarity and heterogeneity allows for short and long local lifetimes and water vapor ages, while maintaining the global average mass balance and corresponding mean water vapor lifetime. A plausibility argument based on humidity-weighted winds suggests that median lifetimes of 4–5 days are more consistent with weather system patterns in the extratropics. I propose that the median of the lifetime is more representative, since its mean value is affected by uncertainty originating from a long, thin tail. To more comprehensively understand the water vapor lifetime, methods will need to report the full lifetime distribution. Simulations with artificial water tracers could thereby provide the framework to compare different methods consistently in the future, while stable water isotopes could serve as an observational constraint.

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37

KORDONSKY, KH B., and I. GERTSBAKH. "BEST TIME SCALE FOR AGE REPLACEMENT." International Journal of Reliability, Quality and Safety Engineering 01, no. 02 (1994): 219–29. http://dx.doi.org/10.1142/s0218539394000167.

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The lifetime of a system is measured in two principal time scales, L and H. We consider a family of time scales of type Ta=(1−a) · L+a · H, for which the lifetime is T=(1−a) L+aH, a∈[0, 1], where L and H are the lifetimes in the principal time scales. The optimal time scale, by the definition, provides the minimal value of the coefficient of variation of system lifetime. We consider the age replacement model in time scale Ta and develop a nonparametric numerical procedure for finding the optimal weighting parameter a* that provides the smallest value of the corresponding cost function. This procedure is applied to fatigue test data, and it is demonstrated that the best time scale with respect to the age replacement cost function is very close to the optimal time scale. The same is true for a modified age replacement scheme in which the replacement age equals the p-quantile of system lifetime.

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38

Čermák, Petr. "Analysis of customer lifetime value model: Literature review." Český finanční a účetní časopis 2013, no. 4 (2013): 84–95. http://dx.doi.org/10.18267/j.cfuc.355.

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39

Bayanjargal, Darkhijav, Batsukh Davaasuren, and Rentsen Enkhbat. "A Numerical Approach to the Customer Lifetime Value." iBusiness 10, no. 02 (2018): 85–91. http://dx.doi.org/10.4236/ib.2018.102005.

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40

Bayanjargal, Darkhijav, and Batsukh Davaasuren. "An Optimal Control Approach to Customer Lifetime Value." iBusiness 12, no. 04 (2020): 150–59. http://dx.doi.org/10.4236/ib.2020.124011.

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41

Lin, Chia Chia, and Dong Her Shih. "Data Mining Techniques to Enhance Customer Lifetime Value." Advanced Materials Research 225-226 (April 2011): 3–7. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.3.

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It is proved by many studies that it is more costly to acquire than to retain customers. Consequently, evaluating current customers to keep high value customers and enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on RFM model to do clustering analysis to recognize high value customer groups for cross-selling promotions.

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42

Cannon, Hugh M., James N. Cannon, and Manfred Schwaiger. "Incorporating Customer Lifetime Value Into Marketing Simulation Games." Simulation & Gaming 41, no. 3 (2009): 341–59. http://dx.doi.org/10.1177/1046878109341311.

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43

Safari, Fariba, Narges Safari, and Gholam Ali Montazer. "Customer lifetime value determination based on RFM model." Marketing Intelligence & Planning 34, no. 4 (2016): 446–61. http://dx.doi.org/10.1108/mip-03-2015-0060.

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Purpose – One of the salient challenges in customer-oriented organizations is to recognize, segment and rank customers. Customer segmentation is usually based on customer lifetime value (CLV) measured by three purchase variables: “Recency,” “Frequency” and “Monetary.” However, due to the ambiguity of these variables, using deterministic approach is not appropriate. For tackling this matter, the purpose of this paper is to propose a new method of customer segmentation and ranking by combining fuzzy clustering (as a segmentation method) and fuzzy AHP (as a ranking method). Design/methodology/approach – First, customers are classified based on purchase variables using fuzzy c-means clustering algorithm. Second, the variables are weighed applying an optimized version of AHP method. Considering the derived weights and customer groups, this paper follows to ranks segments based on CLV. The developed methodology has been implemented for a large IT company in Iran. Findings – The results show a tremendous capability to the company to evaluate his customers by dividing them into nine ranked segments. The validity of clusters has been submitted. Research limitations/implications – For researchers, this study provides a useful literature by combining FCM and an optimized version of fuzzy AHP in order to cover the limitations of previous methodologies. For organizations, this study clarifies the procedure of customer segmentation by which they can improve their marketing activities. Practical implications – Managers can consider the proposed CLV calculation methodology for selling the next best services/products to the group of customers that are more valuable, by calculating the entire lifetime value of the customers. Originality/value – This study contributes to the process of customer segmentation based on CLV, proposing a new method which covers the limitations of previous customer segmentation methods.

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44

Hajipour, Bahman, and Molud Esfahani. "Delta model application for developing customer lifetime value." Marketing Intelligence & Planning 37, no. 3 (2019): 298–309. http://dx.doi.org/10.1108/mip-06-2018-0190.

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Purpose The purpose of this paper is to evaluate the relationship between strategy and customer lifetime value (CLV). A new model was proposed for defining customers’ values based on the RFM model and segmenting bank customers using the K-means algorithm. In addition, the authors combined a new category with the delta model in order to analyze the behavior of each cluster. Design/methodology/approach This case study was based on an applied method following its objectives and a descriptive-analytic method in terms of data collection. In this research, the AHP, data mining and K-means clustering methods, as well as the discriminant analysis were applied for computing the weights of the indices, examining the relationship between the identified variables, clustering the records and ensuring the clustering accuracy based on the RFM model, respectively. Findings The paper confirmed the relationship between the strategies and CLV. For a cluster whose strategy was the best product, customers had a minimal CLV. For a cluster whose strategy was based on total customer solutions, customers had a median CLV. For a cluster whose strategy was a lock-in system, customers had a maximal CLV. The results suggested that the delta model with these three strategies could act as the CLV developers in two stages: conversion of transient customers to attached customers and conversion of attached customers to locked-in customers. Research limitations/implications One of the limitations of this study was the lack of access to all the bank accounts and assessment of only the strategy type, while highlighting the exact association between every component of the strategies (e.g. structure, environment, etc.) and CLV as a dependent variable deemed to be of a great necessity. Hence, it is recommended that several studies on the relationships presented in this paper be performed to provide further insights into and guidelines on this issue in the future. Practical implications This study emphasized the relevance of strategy and CLV. Managers should differently treat customers in distinct CLV and loyalty levels. In other words, managers must segment their customers based on CLV and apply appropriate strategies for each segment. Originality/value This research tried to fulfill an identified need to study how strategy can be effect CLV through the application of the delta model with three strategic options.

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45

Kehl, Roger E. "Customer Lifetime Value und Churn Management im Kundenbeziehungsmanagement." Controlling 13, no. 4-5 (2001): 203–10. http://dx.doi.org/10.15358/0935-0381-2001-4-5-203.

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46

Meyer, Matthias, and Nina Shaffu. "Customer Lifetime Value mit oder ohne indirekte Werttreiber?" Controlling 19, no. 1 (2007): 31–38. http://dx.doi.org/10.15358/0935-0381-2007-1-31.

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47

Kumar, V., Girish Ramani, and Timothy Bohling. "Customer lifetime value approaches and best practice applications." Journal of Interactive Marketing 18, no. 3 (2004): 60–72. http://dx.doi.org/10.1002/dir.20014.

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48

Pfeifer, Phillip E., and Heejung Bang. "Non-parametric estimation of mean customer lifetime value." Journal of Interactive Marketing 19, no. 4 (2005): 48–66. http://dx.doi.org/10.1002/dir.20049.

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49

Madhani, Pankaj M. "Compensation, Ethical Sales Behavior and Customer Lifetime Value." Compensation & Benefits Review 46, no. 4 (2014): 204–18. http://dx.doi.org/10.1177/0886368714560286.

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50

Porter, Keith A., James L. Beck, Rustem V. Shaikhutdinov, et al. "Effect of Seismic Risk on Lifetime Property Value." Earthquake Spectra 20, no. 4 (2004): 1211–37. http://dx.doi.org/10.1193/1.1810536.

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We examine seismic risk from the commercial real estate investor's viewpoint. We present a methodology to estimate the uncertain net asset value ( NAV) of an investment opportunity considering market risk and seismic risk. For seismic risk, we employ a performance-based earthquake engineering methodology called assembly-based vulnerability (ABV). For market risk, we use evidence of volatility of return on investment in the United States. We find that uncertainty in NAV can be significant compared with investors’ risk tolerance, making it appropriate to adopt a decision-analysis approach to the investment decision, in which one optimizes certainty equivalent, CE, as opposed to NAV. Uncertainty in market value appears greatly to exceed uncertainty in earthquake repair costs. Consequently, CE is sensitive to the mean value of earthquake repair costs but not to its variance. Thus, to a real estate investor, seismic risk matters only in the mean, at least for the demonstration buildings examined here.

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