V(CLV): Examining Variance in Models of Customer Lifetime Value

McCarthy, Daniel; Fader, Peter S.; Hardie, Bruce G. S.

doi:10.2139/ssrn.2739475

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…Although our model is more flexible than previously published customer-based corporate valuation models (e.g., in terms of the dynamics that it can accommodate), it has nevertheless remained parametrically parsimonious because the available data are limited and will likely stay that way for the foreseeable future. For example, it is highly unlikely that firms will begin to disclose the kinds of data required to properly account for other sources of customer value, such as the referral value of a customer (Kemper 2010; Kumar et al 2010; Kumar, Petersen, and Leone 2007), the impact of social media (Luo, Zhang, and Duan 2013; Yu, Duan, and Cao 2013), customer satisfaction (Anderson, Fornell, and Mazvancheryl 2004; Homburg, Koschate, and Hoyer 2005; Luo and Bhattacharya 2006), or heterogeneity in the spend per customer (McCarthy, Fader, and Hardie 2016). At the same time, indirect proxies for these factors may be obtainable in some cases through external data sources for a small subset of companies.…”

Section: General Discussion and Future Workmentioning

confidence: 99%

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Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

McCarthy

Fader

Hardie

2017

Journal of Marketing

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Valuing Subscription-Based Businesses Using Publicly Disclosed Customer DataThe growth of subscription-based commerce has seen a change in the types of data firms report to external shareholders. More than ever before, companies are discussing and disclosing data on the number of customers acquired and lost, customer lifetime value, and more. This has fueled an increasing interest in linking the value of a firm's customers to the overall value of the firm, with the term customer-based corporate valuation being using to describe such efforts.While a number of researchers in the fields of marketing and accounting have explored this idea, their underlying models of customer acquisition and retention do not adequately reflect the empirical realities associated with these behaviors, and the associated valuation models do not live up to the standards of finance professionals. We develop a framework for valuing subscription-based firms that addresses both issues, and apply it to data from Dish Network and Sirius XM Holdings.

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Section: General Discussion and Future Workmentioning

confidence: 99%

“…We infer a long right tail to Longtime Larry's pretax RLV, which drives up Longtime Larry's expected pretax RLV but also implies a much higher variance about that expectation. Longtime Larry is more valuable but is also more risky (McCarthy, Fader, and Hardie 2016).…”

Section: Figurementioning

confidence: 99%

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

McCarthy

Fader

Hardie

2017

Journal of Marketing

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“…We infer a long right tail to Longtime Larry's pre-tax RLV -this drives up Longtime Larry's expected pre-tax RLV, but also implies a much higher variance about that expectation. Longtime Larry is more valuable but is also more risky (McCarthy, Fader, and Hardie 2016).…”

Section: Comparison Of Residual Value By Tenurementioning

confidence: 99%

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

2015

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“…Estimating the variance of the customers' CLV is important because the customer base of most companies is by no means uniform, and customers of different levels have different needs, which should be addressed at an individual level for proper customer relationship management. 13,14 The EMP measure, as proposed by Verbraken et al, assumes a fixed and equal CLV for all customers.…”

Section: Introductionmentioning

confidence: 99%

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values

2018

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The goal of customer retention campaigns, by design, is to add value and enhance the operational efficiency of businesses. For organizations that strive to retain their customers in saturated, and sometimes fast moving, markets such as the telecommunication and banking industries, implementing customer churn prediction models that perform well and in accordance with the business goals is vital. The expected maximum profit (EMP) measure is tailored toward this problem by taking into account the costs and benefits of a retention campaign and estimating its worth for the organization. Unfortunately, the measure assumes fixed and equal customer lifetime value (CLV) for all customers, which has been shown to not correspond well with reality. In this article, we extend the EMP measure to take into account the variability in the lifetime values of customers, thereby basing it on individual characteristics. We demonstrate how to incorporate the heterogeneity of CLVs when CLVs are known, when their prior distribution is known, and when neither is known. By taking into account individual CLVs, our proposed approach of measuring model performance gives novel insights when deciding on a customer retention campaign. The method is dependent on the characteristics of the customer base as is compliant with modern business analytics and accommodates the data-driven culture that has manifested itself within organizations.

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V(CLV): Examining Variance in Models of Customer Lifetime Value

Cited by 7 publications

References 30 publications

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

Valuing Subscription-Based Businesses Using Publicly Disclosed Customer Data

Profit-Based Model Selection for Customer Retention Using Individual Customer Lifetime Values

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