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The current research aims to explore variables that explain the differences in online reviewers' behavior.

The authors' panel dataset is compiled from Amazon.com's book section and uses publicly available information about reviewers in combination with the reviews they wrote. The authors utilize the Pareto/NBD model with time-invariant covariates. The model's parameters are estimated using maximum likelihood estimates (MLE) with MATLAB software.

This study contributes to the literature by exploring how the characteristics of reviews and the reviewers might shape consumer review frequency and continuity. Specifically, the authors' results show that review ratings, comments on a review, and helpful votes have a positive association with review frequency and continuity. Furthermore, the length of the textual review has a positive relationship with review frequency, but a negative relationship with review continuity. Relative to anonymous reviewers, people who write reviews and use their real names post reviews less often, but their review continuity is longer.

This paper is the first to identify empirically variables that explain review frequency and continuity, thus enabling companies to gain a better understanding of their reviewer base and hence to manage online word-of-mouth more efficiently. It is the first study to apply a well-known behavioral model to address online review behavior.

The purpose of this paper is to examine how installed base information could help servitizing original equipment manufacturers (OEMs) forecast and support their industrial service sales, and thus increase OEMs’ understanding regarding the dynamics of their customers lifetime values (CLVs).

This work constitutes a constructive research aiming to arrive at a practically relevant, yet scientific model. It involves a case study that employs statistical methods to analyze real-life quantitative data about sales and the global installed base.

The study introduces a forecasting model for industrial service sales, which considers the characteristics of the installed base and predicts the number of active customers and their yearly volume. The forecasting model performs well compared to other approaches (Croston’s method) suitable for similar data. However, reliable results require comprehensive, up-to-date information about the installed base.

The study contributes to the servitization literature by introducing a new method for utilizing installed base information and, thus, a novel approach for improving business profitability.

OEMs can use the forecasting model to predict the demand for – and measure the performance of – their industrial services. To-the-point predictions can help OEMs organize field services and service production effectively and identify potential customers, thus managing their CLV accordingly. At the same time, the findings imply new requirements for managing the installed base information among the OEMs, to understand and realize the industrial service business potential. However, the results have their limitations concerning the design and use of the statistical model in comparison with alternative approaches.

The study presents a unique method for employing installed base information to manage the CLV and supplement the servitization literature.

Marketing/finance interface and application of its new insights in marketing decisions have recently found great interest among marketing researchers and practitioners. There is a relatively large body of marketing literature about incorporating modern portfolio theory (MPT) into customer portfolio context and taking advantage of it in marketing resource allocation decisions. Previous studies have modelled customer portfolio risk in the form of historical return/profitability volatility of customer base. However, the risk is a future-oriented measure, and deals with future volatility associated with return stream. This study aims to address this research problem.

The well-known Pareto/non-binomial distribution (NBD) approach is used to model customer purchases in a non-contractual setting of research practice. Then, the results were used to simulate the customers’ future buying behaviour and associated returns via the Monte Carlo simulation approach. Subsequently, the mean-variance portfolio optimization model was applied to find the optimal customer portfolio mix.

The results illustrated the better performance of the proposed efficient portfolio versus the current customer portfolio. These results are applicable in analyzing customer portfolio composition, and can be used as a guidance to make decisions about marketing resource allocation in different segments.

This study proposes a new approach to analyze customer portfolio by using the customers’ future buying behaviour. Taking advantage of rich marketing literature about statistical assumptions describing the customers’ buying behaviour, this study tries to take some steps forward in the application of the MPT theory in customer portfolio management context.

The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM) emphasizes the importance of measuring customer value. Analytics has paved the way for innovation by providing companies valuable insights into the behavior of customers. Earlier models used to measure customer value do not take into account the types and level of risk posed by customers, such as probability of churn, regularity of purchases, etc. The authors put forth a new and innovative approach to measuring customer value while, at the same time, adjusting for customer riskiness.

Using a non-parametric approach used in the operations research area, the authors create a risk-adjusted regency, frequency, monetary value (RARFM) score for each customer. These scores are used to segment the customers into two groups – customers with high and low RARFM scores. The authors then identify the underlying demographics and behavioral characteristics that separate the two groups.

Findings of this paper indicate that customers who perform the best on the RARFM metric tend to be more experienced, and are more likely to exhibit behavioral tendencies that help them perform well in their jobs, such as purchasing promotional goods that act as sales aid and enhance their performance.

The paper is innovative in its approach in terms of creating a new metric for calculating customer value. Few papers have proposed ways to handle and adjust for customer riskiness. Here, the authors propose three kinds of customer risk. Current paper provides a twist to traditional RFM analysis by creating a RARFM score for each customer, and provides a scientific way of assigning weights to RFM.

This paper aims to extend the known boundary conditions of the negative binomial distribution (NBD) model, and to test the applicability of conditional trend analysis (CTA) – a key method to identify whether changes in overall sales are accounted for by previous non-buyers, light buyers or heavy buyers – in industrial purchasing situations.

The study tested the NBD model and CTA in an industrial marketing context using a 12-month data set of purchases from an Australian supplier of a range of industrial plastic resins.

The purchase data displayed a good NBD fit; the study therefore extends the known boundary conditions of the model. The application of CTA provided second-period purchasing frequency estimates showing no significant difference from actual data, indicating the applicability of this method to industrial purchasing.

Data relate to just one supplier. Further research across several industries is required to confirm the generalizability and robustness of NBD and CTA to industrial markets.

Marketing decisions can be improved through appropriate analysis of customer purchasing data. However, without access to equivalent competitor data, industrial marketers are constrained in benchmarking the purchasing patterns of their own customers. The results indicate that use of the NBD model enables valid benchmarking for industrial products, while CTA would enable appropriate analysis of purchases by different classes of customer.

This paper extends the known boundary conditions of the NBD model and provides the first published results, indicating the appropriateness of CTA to predict purchasing frequencies of different industrial customer classes.

Predicting the inactivity and the repeat transaction frequency of a firm's customer base is critical for customer relationship management. The literature offers two main approaches to such predictions: stochastic modeling efforts represented by Pareto/NBD and machine learning represented by neural network analysis. As these two approaches have been developed and applied in parallel, this study systematically compares the two approaches in their prediction accuracy and defines the relatively appropriate implementation scenarios of each model.

By designing a rolling exploration scheme with moving calibration/holdout combinations of customer data, this research explores the two approaches' relative performance by first utilizing three real world datasets and then a wide range of simulated datasets.

The empirical result indicates that neither approach is dominant and identifies patterns of relative applicability between the two. Such patterns are consistent across the empirical and the simulated datasets.

This study contributes to the literature by bridging two previously parallel analytical approaches applicable to customer base predictions. No prior research has rendered a comprehensive comparison on the two approaches' relative performance in customer base predictions as this study has done. The patterns identified in the two approaches' relative prediction performance provide practitioners with a clear-cut menu upon selecting approaches for customer base predictions. The findings further urge marketing scientists to reevaluate prior modeling efforts during the past half century by assessing what can be replaced by black boxes such as NNA and what cannot.

The purpose of this paper is to present a model that retailers engaged in e‐commerce (e‐tailers) can use for determining the optimal mix of customer segments within a customer portfolio from an integrated risk and return perspective.

Portfolio Selection Theory of Markowitz is applied to find the optimal composition of customer portfolios. The model is developed and discussed for two customer segments (relationship‐ and transaction‐oriented customers) and exemplarily applied to a data set of an e‐tailer.

Portfolio Selection Theory of Markowitz is well‐suited and promising for determining an optimal customer portfolio from a risk‐return perspective. However, since customers vary from financial assets in several aspects, the results of the model have to be interpreted conscientiously and the resulting action options have to be interpreted within the context of customer relationship management (CRM).

The model proposes to carry out a sequential set of one‐period optimizations. To reduce complexity, several simplifying assumptions were made within the model regarding the characteristics of customer segments and portfolio as well as the expected risk and return.

A current survey among German companies indicates that companies already have broad experiences in customer evaluation. However, it also turned out that evaluating customers' potential and risk simultaneously is still a major challenge. Our new approach facilitates the making of sound investment decisions into single customer relationships with respect to an overall optimal customer portfolio. Thus, a formal link to value‐based management is established.

Using CRM for a value‐based management of customer portfolio according to a superordinated risk management objective has so far received little attention in literature. This paper's model is a new approach in customer portfolio management for e‐tailers taking customers' risk and return characteristics simultaneously and in real‐time into consideration.

Data mining registers of transactions allows for benchmarking customer's evaluation strategies. The purpose of this paper is to provide information on the application of different approaches to explore this kind of data.

Traditionally, heuristics based on variables such as recency, frequency, and monetary (RFM) value of transactions are used to determine the best customers. In this paper, a new form of directly combining the values of these variables is compared to an approach based on fitting a stochastic model. This last model is a mixture of a model for the number of transactions and another for the value spent. The new direct form of evaluation is based on computing the joint probability of maximizing quality indicators.

Good fit of the different models tested to the series of individual data as well as coherent predictions are registered. Patterns found provide empirical confirmation of results that theoretically should be expected.

These results are valid for a particular supermarkets network in a Brazilian city. The inner consistency of the results, nevertheless, and the coherence of the results obtained with what was expected, encourage application to other places and sectors of activity.

The results obtained show clearly the effectiveness of the approach based on RFM value measurement.

The models studied are applied for the first time for the kind of data treated, where determination of which customers remain active is a problem of special interest.

In this paper the authors aim to review the state-of-the-art literature on online review systems and their impacts on consumer behavior and retailers' performance with the aim of identifying research gaps related to different design features of review systems and developing future research agenda.

The authors conducted a systematic review based on PRISMA 2020 protocol, focusing on studies published in the domains of retailing and marketing. This procedure resulted in 48 selected papers investigating the design features of retailer online review systems.

The authors identify eight design features that are controllable by retailers in an online review system. The design features have been researched independently in previous literature, with some features receiving more attention. Most selected studies focus on the design features adapted metrics and review presentations, while other features are generally neglected (e.g. rating dimensions). Previous literature argues that design features affect consumer behaviors and retailers' performance. However, the interactions among the features are still neglected in the literature, creating a relevant gap for future research.

This paper distinguishes between different types of retailer online review systems based on how they are implemented. The authors summarize the state-of-the-art of relevant literature on design features of online review systems and their effects on consumer- and retailer-related outcome variables. This systematic literature review distinguishes between online reviews provided on websites controlled by retailers (internal systems) and third-party websites (external systems).