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Managing Customer Relationships Using Customer Lifetime Value and Customer Equity Metrics
Published in Madhu Arora, Poonam Khurana, Sonam Choiden, Performance Management, 2020
Kotler (1974) defined CLV as the present value of the expected future profit stream from a customer expected over a given period of time. More recently, Pfeifer, Haskins, and Conroy (2005) defined CLV as the present value of the future cash flows from a customer. In 1998, Berger and Nasr defined CLV as the sum of the discounted cash flows generated from a customer, or customers, as a result of their relationship with the company. CLV is the net present value of benefits associated with each customer, once acquired, and after providing for incremental costs associated with each customer (e.g., marketing, selling, production, and service), over the length of this association with the company (Blattberg, Kim, and Neslin, 2008; Dywer, 1997). Thus, CLV framework measures the changes in customer behaviour (e.g., increased purchase, retention) and its influence on customers' future cash flows, and consequently, the profitability to the firm (Zhang et al., 2010). In this way, CLV allows for segmenting customers on the basis of their profitability and then targeting them appropriately.
New CRM Acronym – “Customer Really Matters”
Published in Flevy Lasrado, Norhayati Zakaria, Internalizing a Culture of Business Excellence, 2018
Flevy Lasrado, Norhayati Zakaria
With this backdrop and inspiring thoughts, for us, Customer Really Matters (CRM) is a key business strategy that will determine the success of the organization. Customer Really Matters signifies ascertaining, meeting, and exceeding customer requirements at every phase of his/her life cycle with the organization. These phases are broadly categorized into five distinct stages: reach, acquisition, conversion, retention, and loyalty/advocacy. A proper and efficient management of expectations throughout the various stages of the customer life cycle results in an increased customer lifetime value (CLV). CLV is the total value or net profit generated by a customer for an organization across the entire customer lifecycle. A long and enduring relationship with the customer not only results in a higher CLV but also yields customer advocacy—that is, the customer acting as an ambassador for the organization, thereby supporting and referring the company’s products and services to others.
Metrics
Published in Gerhard Plenert, Joshua Plenert, Strategic Excellence in the Architecture, Engineering, and Construction Industries, 2018
Gerhard Plenert, Joshua Plenert
The Client Lifetime Value (CLV) is the total amount of revenue predicted to be collected over the lifetime of the organization’s relationship with a specific client. The CLV is typically calculated using historical revenue data projected into the future. Using trends generated by analyzing the historical revenue data can identify trends and possible causes for increases and decreases in revenue. The CLV can provide insight into how many resources should be dedicated to obtaining specific types of clients and which types of clients have the greatest value to the organization. The CLV can also help to clarify the benefits of dedicating resources to the retention of specific types of clients. Although the CLV is a prediction, it can help to direct marketing and business development efforts towards the most valuable clients and prevent dedication of excessive resources on less valuable clients.
Evolutionary multi-objective customer segmentation approach based on descriptive and predictive behaviour of customers: application to the banking sector
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
Chiheb-Eddine Ben Ncir, Mohamed Ben Mzoughia, Alaa Qaffas, Waad Bouaguel
CLV is an important measure for understanding and predicting customer behaviour. An accurate estimation of CLV will maximise the company’s revenue since CLV focuses on finding and retaining the most profitable customers over the long term. However, calculating an accurate CLV is a challenging task since it requires an accurate estimation of future events. For example, it is difficult to estimate how long customers will remain engaged with a firm, how many transactions will make in future periods and how much they will spend, especially when there is not enough historical information about customers. The prediction of a customer behaviour is usually uncertain, and its effectiveness is directly related to the used prediction model. The challenging task of computing CLV has motivated researchers to propose accurate estimation methods based on different hypotheses and models, such as RFM (Recency, Frequency and Monetary), Markov chains, Bayesian inferences, supervised machine learning, and probabilistic models (Kim et al., 2006).
Do online reviews truly matter? A study of the characteristics of consumers involved in different online review scenarios
Published in Behaviour & Information Technology, 2021
Third, when realised the importance of lurkers, we built a regression model for identifying the influences and quantitative relationships between the consumers’ features and the different consumer types. Yao and Xiong (2011) attempted to predict consumer transaction behaviour using the RFM model. Voigt and Hinz (2016) used the CLV model to identify potential heavy users for the creation of special marketing measures. In contrast to the above studies, which used traditional models to predict consumer behaviour, we built a model using unique variables to determine the characteristics of lurkers. We identified that lurkers are the consumers with higher historical transaction contributions, and they have higher purchasing motivation. Additionally, most of them have a clear preference of trading platform for information search and product purchase. They create more profit and should be valued by firms. However, scholars and firms may overlook the importance of lurkers because they do not post online reviews, as firms always attach great important to the role of online reviews. Our results also indicate that the main effect of consumer characteristics on lurkers are consistent with consumer-cluster 3 (NR-cluster) as analyzed in the machine learning analysis. Taken together, our key findings contribute to the emerging literature related to online consumer reviews by demonstrating the importance of lurkers.