The Experts below are selected from a list of 2751 Experts worldwide ranked by ideXlab platform
Ellen Roemer - One of the best experts on this subject based on the ideXlab platform.
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A typology of Customer Lifetime values in buyer–seller relationships
Journal of Strategic Marketing, 2007Co-Authors: Ellen RoemerAbstract:In the past, marketing researchers have proposed the use of simple net present value analyses to assess Customer Lifetime values (CLVs). However, simple net present values disregard two important aspects: (1) environmental risks affecting Customer cash flows and (2) a firm's flexibility in reacting to these risks. Consequently, they are inappropriate for assessing CLVs in relationships, in which risks affect Customer cash flows and suppliers are able to react. This paper suggests a typology of CLV models in accordance with the degree of environmental risk and the supplier's flexibility. The paper thus contributes to a more differentiated Customer Lifetime valuation and, consequently, to a more accurate basis for decision making in relationships. The use of real options analysis is recommended for relationships which are affected by environmental risks and in which suppliers are flexible. By applying real options analysis to Customer Lifetime valuation, the paper offers a new methodological approach, thus ...
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the impact of dependence on the assessment of Customer Lifetime value in buyer seller relationships
Journal of Marketing Management, 2006Co-Authors: Ellen RoemerAbstract:This paper links two important areas of relationship marketing: dependence between buyers and sellers and the assessment of Customer Lifetime value in business-to-business relationships. It suggests the use of specific Customer valuation models in four different types of dependence in buyer-seller relationships. Numerical examples and practical cases are provided to support the argument. For one particular relationship type, the author applies real options analysis to account for the value of the supplier's independence (or, alternatively, flexibility) in the Customer Lifetime value. The paper contributes to a differentiated and thus better assessment of Customer Lifetime values in buyer-seller relationships. Managers can use the suggested typology and its metrics to improve decision-making in buyer-seller relationships.
Robert F Dwyer - One of the best experts on this subject based on the ideXlab platform.
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Customer Lifetime valuation to support marketing decision making
Journal of Direct Marketing, 1997Co-Authors: Robert F DwyerAbstract:Customer Lifetime value (LTV) is an important construct in designing and budgeting for Customer acquisition programs and a number of other decision areas. This article adapts a simple taxonomy of buyer-seller relationships to distinguish basic approaches for LTV estimation.
Daniel Garcia - One of the best experts on this subject based on the ideXlab platform.
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ICMLA - Maximizing Customer Lifetime Value using Stacked Neural Networks: An Insurance Industry Application
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Co-Authors: Gaddiel Desirena, Armando Diaz, Jalil Desirena, Ismael Moreno, Daniel GarciaAbstract:This paper proposes a recommender system based on two-stage neural network architecture that maximizes Customer Lifetime Value (CLV). The Stage-I neural network uses a self-attention mechanism and a Collaborative Metric Learning (CML) to generate product recommendations. The Stage-II neural network uses a neural network-based survival analysis to infer insurance product recommendations that maximize Customer Lifetime. The proposed stacked neural network model can be used as a generative model to explore different cross-sell scenarios. The applicability of the proposed recommendation system is evaluated using transactional data from an Australian insurance company. We validated our results against a state of the art self-attention recommendation system, successfully extending its functionality to include Lifetime value.
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Maximizing Customer Lifetime Value using Stacked Neural Networks: An Insurance Industry Application
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Co-Authors: Gaddiel Desirena, Armando Diaz, Jalil Desirena, Ismael Moreno, Daniel GarciaAbstract:This paper proposes a recommender system based on two-stage neural network architecture that maximizes Customer Lifetime Value (CLV). The Stage-I neural network uses a self-attention mechanism and a Collaborative Metric Learning (CML) to generate product recommendations. The Stage-II neural network uses a neural network-based survival analysis to infer insurance product recommendations that maximize Customer Lifetime. The proposed stacked neural network model can be used as a generative model to explore different cross-sell scenarios. The applicability of the proposed recommendation system is evaluated using transactional data from an Australian insurance company. We validated our results against a state of the art self-attention recommendation system, successfully extending its functionality to include Lifetime value.
Malliga Marimuthu - One of the best experts on this subject based on the ideXlab platform.
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Customer Lifetime value as a predictor for future prospect of retailer's survival: A review of Customer Lifetime value model
2020Co-Authors: Abdul Manaf Bohari, Ruslan Rainis, Malliga MarimuthuAbstract:Customer Lifetime value is the top priority issues in every segment of business and it’s become more significance during the world wide economy crisis. Estimating Customer Lifetime value of retailer’s business plays important roles in determine the most profitable Customer’s to the business, as well as sustaining the performance of business, both short and long run operation. In perspective of global meltdown economy, specifically, the use of Customer Lifetime value will became as central issues because it has major influence on the strategy that adopted by the business. As a predictor of future prospect of retailer’s survival, Customer Lifetime value are developed based on financial items/method, as well as payback period, net present value, return on investment, return on equity, and so on. Thus, the objective of the paper is to review’s the model of Customer Lifetime value in-prospecting the future prospect of retailer’s survival in the marketplace. For that, advantages and disadvantages of models are discussed. In addition, to the best of knowledge, there are limited discussions on the reasons of adopting the Customer Lifetime approach for prospecting the Lifetime value of retailer’s business, includes hypermarket business. Thus, discussion on the reasons of adopting the model was made with specific reference to hypermarket business. Moreover, it is important to the manager to understand the capabilities and constrains of those methods because it can affect the financial strategy of the business. At the end, suggestion was made on how to improvise the performance of estimates the Customer Lifetime value, accordingly to the chance of Customer value in the geographical marketplace.
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Customer Lifetime Value Model in Perspective of Firm and Customer: Practical Issues and Limitation on Prospecting Profitable Customers of Hypermarket Business
The International Journal of Business and Management, 2011Co-Authors: Abdul Manaf Bohari, Ruslan Rainis, Malliga MarimuthuAbstract:Most of business scholars claim Customer Lifetime value is the top priority issues in the world wide business operation includes for hypermarket business setting. Theoretically, prospecting Lifetime value of Customers in the marketplace is the main platform for determine how long the hypermarket can survive, at lease 3 to 5 years in future. In fact, Customer Lifetime value is become top strategic issues during the global meltdown economy because of it major affects on hypermarket profitability, both for current and future of business. Practically, there are two main streams for estimating Lifetime value of Customers, as identified as firm perspective and Customer perspectives. As implication, there are arising some practical and implementation issues on it. The paper is purposely for discussed Customer Lifetime value model issues faced by the two main streams perspectives. Specifically, limitation of both perspectives is highlighted by using the hypermarket business as environment setting of discussion. In addition, some suggestion was point-out to tackle the problems.
Gaddiel Desirena - One of the best experts on this subject based on the ideXlab platform.
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ICMLA - Maximizing Customer Lifetime Value using Stacked Neural Networks: An Insurance Industry Application
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Co-Authors: Gaddiel Desirena, Armando Diaz, Jalil Desirena, Ismael Moreno, Daniel GarciaAbstract:This paper proposes a recommender system based on two-stage neural network architecture that maximizes Customer Lifetime Value (CLV). The Stage-I neural network uses a self-attention mechanism and a Collaborative Metric Learning (CML) to generate product recommendations. The Stage-II neural network uses a neural network-based survival analysis to infer insurance product recommendations that maximize Customer Lifetime. The proposed stacked neural network model can be used as a generative model to explore different cross-sell scenarios. The applicability of the proposed recommendation system is evaluated using transactional data from an Australian insurance company. We validated our results against a state of the art self-attention recommendation system, successfully extending its functionality to include Lifetime value.
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Maximizing Customer Lifetime Value using Stacked Neural Networks: An Insurance Industry Application
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Co-Authors: Gaddiel Desirena, Armando Diaz, Jalil Desirena, Ismael Moreno, Daniel GarciaAbstract:This paper proposes a recommender system based on two-stage neural network architecture that maximizes Customer Lifetime Value (CLV). The Stage-I neural network uses a self-attention mechanism and a Collaborative Metric Learning (CML) to generate product recommendations. The Stage-II neural network uses a neural network-based survival analysis to infer insurance product recommendations that maximize Customer Lifetime. The proposed stacked neural network model can be used as a generative model to explore different cross-sell scenarios. The applicability of the proposed recommendation system is evaluated using transactional data from an Australian insurance company. We validated our results against a state of the art self-attention recommendation system, successfully extending its functionality to include Lifetime value.
