Comparing alternative approaches to estimate customer equity

Abstract

Purpose The purpose of this paper is to explore the differences and similarities between two methods/models for estimating customer equity (CE): one using behavior-based data and one using market-based data. Design/methodology/approach Two separate analyses of the same market scenario (telecom industry) were conducted, by applying the CE estimation method from Rust et al. (2004) and the CE model from Gupta et al. (2004). Findings Different methods/models can produce similar estimates, which corroborates the defense of an integrated multi-method approach to evaluating CE. In addition, they each provide different benefits. The behavioral data model provides identification of CE drivers and assists in the task of marketing resource allocation, the market-based data model is simple and easy to implement and is recommended in cases when CE is used as a financial indicator. Originality/value This paper contributes to the CE literature in the following ways. First, it demonstrates the possibility of obtaining similar estimates of CE using distinct types of data and data collection procedures, and with two different estimation methods/models. Second, it confirms that either model allows firms to compute the expected market capitalization at any given time using customer and financial information. Third, it demonstrates the convergent validity of these two methods/models for estimating CE for either public or private companies, thus legitimizing the comparison of their respective CE values, regardless of the type of source data or estimation formula used.

Engaging in customer citizenship behaviours to predict customer lifetime value

“…‘Good’ consumers are evaluated on financial metrics such as sales volume, revenue, profit, or CLV. Silveira et al ( 2017 ) claim that a static model of CLV can be equivalent to a dynamic model to achieve these commonly accepted financial metrics for CLV. The marketing literature recognises that both types of models are good proxies for the market value of firms.…”

“…The consequences of CCB in the developed model are Intentional loyalty and CLV. We measure Intentional loyalty using the same measurement scale as Segarra-Moliner and Moliner-Tena ( 2016 ) and calculate CLV based on Silveira et al ( 2017 ) and Segarra-Moliner and Moliner-Tena ( 2016 ).…”

“…The questions are preceded by the following question: ‘Based on your past experience with this firm, how likely are you to …?’. Silveira et al ( 2017 ) describe a continuous scale in the formula CLV = (m ∗ r) / (1 + i − r). Two items define the composite of CLV, where the variable ‘margin’ is the only one calculated differently to achieve two reflective indicators.…”

“…The remaining parameters of the equation are the same. The retention rate (r) is measured in the same way as Silveira et al ( 2017 ) in the static model. That is, for the CLV, over n periods, the margin is assumed to be fixed over time (as in cell phone contracts, for instance).…”

Measuring Customer Equity in Noncontractual Settings Using a Diffusion Model: An Empirical Study of Mobile Payments Aggregator

“…The income and cost data that determine profits were directly obtained from annual reports. Silveira et al [44] believed that the method proposed by Gupta, Lehmann, and Stuart [8] requires a constant retention rate and contribution margin, and the method thus appeared more suitable to the calculation of customer equity under contractual settings. Additionally, researchers have also studied customer churn using diffusion models.…”