Data collection: Central frameworks for localised customer lifetime value
نویسندگان
چکیده
منابع مشابه
CUSTOMER CLUSTERING BASED ON FACTORS OF CUSTOMER LIFETIME VALUE WITH DATA MINING TECHNIQUE
Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM,...
متن کاملCustomer Lifetime Value Models: A literature Survey
Abstract Customer Lifetime Value (CLV) is known as an important concept in marketing and management of organizations to increase the captured profitability. Total value that a customer produces during his/her lifetime is named customer lifetime value. The generated value can be calculated through different methods. Each method considers different parameters. Due to the industry, firm, business...
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Today, people are increasingly connected and extensively interact with each other using technology-enabled media. Hence, customers are more frequently exposed to social influence of other customers when making purchase decisions. However, established approaches for customer valuation most widely neglect network effects based on social influence leading to a misallocation of resources. Therefore...
متن کاملMeasuring Customer Lifetime Value
Being able to measure customer value is a prerequisite for effective customer relationship management and data-driven marketing strategy, as it allows to maximize return on marketing investment, particularly when resources are limited. While past profitability is certainly a useful metric, it is insufficient when trying to predict which customers are going to be most valuable in the future so a...
متن کاملCustomer Lifetime Value Modeling
Customer lifetime value (LTV) estimation involves two parts: the “survival” probabilities and profit margins. This article describes the estimation of those probabilities using discrete-time logistic hazard models and that of profit margins is based on linear regression. In the scenario when outliers are present among margins, we suggest applying robust regression with PROC ROBUSTREG.
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ژورنال
عنوان ژورنال: Interactive Marketing
سال: 2005
ISSN: 1463-5178,1478-0844
DOI: 10.1057/palgrave.im.4340302