Retention of latent segments in regression-based marketing models

Product design and marketing mix decisions for segmented markets depend crucially on the correct specification of marketing models used as input to these decisions. With real-world data, the true number of segments in a market is unknown. Current evidence from simulation studies suggests that the accuracy of commonly used criteria for determining the number of segments in a market depends on the usage context, including the type of distribution being used to describe the data, the model specification, and the characteristics of the market. This study investigates via simulation the performance of seven segment retention criteria used with finite mixture regression models for normal data. This is one of the most important analysis contexts in marketing research since regression models are used, for example, in conjoint analysis and market response analysis, yet no previous study in either the marketing or statistics literatures explores the segment retention problem for mixture regression models. The study shows that one criterion, Akaike's Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications and data configurations, having the highest success rate and producing very low parameter bias. Currently, this criterion is rarely, if ever, used in the marketing literature.     

Do We Fully Understand the Critical Success Factors of Customer Satisfaction with Industrial Goods? - Extending Festge and Schwaiger’s Model to Account for Unobserved Heterogeneity

This paper extends Festge and Schwaiger’s (2007) model of customer satisfaction with industrial goods by accounting for unobserved heterogeneity. The application of a novel response-based segmentation approach in partial least squares path modeling (PLS-PM) - the finite mixture partial least squares (FIMIX-PLS) methodology - opens the way for the effective identification of distinctive customer segments. In comparison to previous studies in this field, group-specific path model estimates reveal each customer segment’s particular characteristics as well as other differentiated findings. Hence, this contribution demonstrates that structural equation modeling studies on the aggregate data level can be seriously misleading and makes a strong case for segment-specific customer satisfaction analyses.     

Revisiting the risk/return relations in the Asian Pacific markets: New evidence from alternative models

This paper examines the risk/return relations in eleven Asian Pacific stock markets and explores if the 1997 Asian financial crisis significantly influenced market behavior in the region. We use a plain vanilla time-series regression approach as well as various GARCH models. Although results significantly vary across model specifications, the overall evidence from GARCH models supports a significantly positive risk/return relation in several markets but only prior to the Asian financial crisis. These results accord with Glosten et al. (1993) and Harvey (2001) and suggest that the relative risk aversion is sensitive to both model specifications and structural breaks.     

Customer Lifetime Value as the Basis of Customer Segmentation

SUMMARY

In this article, we examine current trends in customer life-time value and customer segmentation models and identify key issues for future research. CLV-based segmentation is a segmentation approach that groups customers into meaningful segments based upon customer lifetime value and (potentially) other factors. In the article, we discuss the extent to which CLV-based segmentation meets the criteria for effective segmentation. We also identify six areas for future research: (1) models and management of “micro-segments,” (2) using CLV-based segmentation to improve the efficiency of marketing programs, (3) the need for more dynamic CLV-based segmentation models, (4) applying CLV-based customer segmentation to new products and new customers, (5) challenges associated with implementing CLV-based segmentation, and (6) the need for new models that enable firms to segment customers by response to marketing activities and CLV at different points in the customer decision process.     

Dynamic hedging with futures: A copula‐based GARCH model

In a number of earlier studies it has been demonstrated that the traditional regression‐based static approach is inappropriate for hedging with futures, with the result that a variety of alternative dynamic hedging strategies have emerged. In this study the authors propose a class of new copula‐based GARCH models for the estimation of the optimal hedge ratio and compare their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GARCH, and the dynamic conditional correlation (DCC) GARCH models. With regard to the reduction of variance in the returns of hedged portfolios, the empirical results show that in both the in‐sample and out‐of‐sample tests, with full flexibility in the distribution specifications, the copula‐based GARCH models perform more effectively than other dynamic hedging models. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1095–1116, 2008     

Modeling Methods for Discrete Choice Analysis

This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.     

Response style corrected market segmentation for ordinal data

Survey data collected for market segmentation studies is typically ordinal in nature. As such, it is susceptible to response styles. Ignoring response styles can lead to market segments which do not differ in beliefs, but merely in how segment members use survey answer options and which possibly occur in addition to the belief segments. We propose a finite mixture model which simultaneously segments and corrects for response styles, permits heterogeneity in both beliefs and response styles, accommodates a range of different response styles, does not impose a certain relationship between the response style and belief segments, and is suitable for ordinal data. The performance of the model is tested using both artificial and empirical survey data.     

Conditional market segmentation by neural networks: a Monte-Carlo study

An artificial neural network (ANN) algorithm is proposed that incorporates both market segmentation and discriminant (regression) analysis of the segments. The method simultaneously estimates the models relating consumer characteristics to market segments, i.e., subjects are assigned to (unique) segments so that subjects within a class show similar purchase behavior and share the same characteristics (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm. The performance of the ANN methodology is assessed in a Monte-Carlo study. In contrast to the usual stepwise approach adopted in segmentation studies, our study found that simultaneous segmentation and discrimination are preferable for finding an overall optimum in that this way clusters are formed not only to create homogeneous submarkets but also to show a good dicriminatory behavior.     

Regional Integration and Trade: Controlling for Varying Degrees of Heterogeneity in the Gravity Model

The proliferation of regional trade agreements (RTAs) in the 1990s prompted a renewal of interest in studying the effect of regional integration on trade. Using a panel dataset of bilateral export flows from 12 EU countries to 20 OECD trading partners over the period 1992–2003, the trade effect of European regional integration, denoted by an EU dummy, is examined across a number of fixed effects (FE) specifications, each of which has been claimed as the correct econometric specification of the gravity model. Typically parsimonious in (time‐varying) economic variables and abundant in fixed effects, the FE specifications allow for varying degrees of heterogeneity in the gravity model. Two gravity models are estimated: a gravity model of traditional trade determinants and a gravity model of new trade theory (NTT) determinants. Both gravity models provide reasonable coefficient estimates, although they vary somewhat across the FE specifications for the traditional gravity model. Both gravity models are congruent in suggesting that the coefficient of the EU dummy declines in magnitude and becomes insignificant as an increasing degree of heterogeneity is admitted into the model. This suggests the fundamental importance of the econometric specification when evaluating trade policy effects within a gravity framework.     

A review of industrial market segmentation research and a proposal for an integrated segmentation framework

This article focuses first on a review of published articles on industrial market segmentation. The origin of formal propositions for segmenting industrial markets is presented. Next, industrial buying models and studies are shown to be a source of industrial market segmentation variables. A review of segmentation variables leads to a check list comprising four main classes of variables. Industrial market segmentation can be structured as a two step process with macro-segmentation followed by micro-segmentation or as a multiple step process (nested approach) proceeding from general to specific variables. Industrial market segmentation is then examined regarding its implementation in companies and specific segmentation variables are reviewed with respect to their degree of availability, appropriateness and usage. Finally, multi-participant buying decisions in both the consumer market and industrial market are integrated in a common segmentation framework.     

融合改进FT显著性与Grabcut的图像目标分割算法

针对目前复杂度较大的图像中目标分割速度较慢、显著性边界分割不明确等问题,提出了一种融合改进的FT（Frequency-tuned）显著性检测与Grabcut的图像分割算法。该算法首先通过改进基于频率调谐的FT显著性检测方法得到图像中显著性较高的区域,并利用SLIC（Simple Linear Iterative Clustering）算法对显著图进行预处理得到超像素图,能够有效改善边界的分割效果,然后通过以图论GraphCut算法为基础改进的Grabcut算法建立高斯混合模型。为了提高算法效率,通过聚类以超像素代替原像素,并反复迭代高斯混合模型（Gaussian Mixed Model,GMM）参数,最后利用最大流最小割算法得到最优目标分割结果。实验结果表明所提算法能够更准确更高效率地分割图像中的显著性目标,对高分辨率图像也有很好的适用效果,相比于其他算法在分割精度上提高10%左右,并具有较高的分割效率。     

Market segmentation with choice-based conjoint analysis

Choice-based conjoint analysis has increased in popularity in recent years among marketing practitioners. The typical practice is to estimate choice-based conjoint models at the aggregate level, given insufficient data for individual-level estimation of part-worths. We discuss a method for market segmentation with choice-based conjoint models. This method determines the number of market segments, the size of each market segment, and the values of segment-level conjoint part-worths using commonly collected conjoint choice data. A major advantage of the proposed method is that current (incomplete) data collection approaches for choice-based conjoint analysis can still be used for market segmentation without having to collect additional data. We illustrate the proposed method using commercial conjoint choice data gathered in a new concept test for a major consumer packaged goods company. We also compare the proposed method with ana priori segmentation approach based on individual choice frequencies.     

Speed-profile-based road segmentation for accident occurrence modelling for hilly terrains

Modelling the accident occurrences per unit length of the road requires segmentation of road stretch. In the case of hilly terrains, homogeneity-based approach results in extremely small sections thus creating a biased accident data. This result is due to the high variations in geometry along the length of the road stretch. Constant length sections do not consider the effect of road's characteristics, hence may lead to undesirable results. It is hypothesized that the speed profile of a vehicle moving in free-flow conditions is governed by the road geometry as well as the road side environment. In this paper, a new methodology based on speed profile of a test vehicle (passenger car) has been proposed for segmenting a road stretch passing through hilly terrain. Comparative analysis of the results obtained from the constant length segmentation approach and the proposed approach shows that the later approach is producing statistically better results. Relatively more parameters were found to be statistically significant in the model based on speed-profile-based segmentation. Grade length, which could be one of the major parameters in hilly terrains, was found to be statistically significant only in the model based on speed-profile-based segmentation.     

Closing small open economy models

The small open economy model with incomplete asset markets features a steady-state that depends on initial conditions and equilibrium dynamics that possess a random walk component. A number of modifications to the standard model have been proposed to induce stationarity. This paper presents a quantitative comparison of these alternative approaches. Five different specifications are considered: (1) A model with an endogenous discount factor (Uzawa-type preferences); (2) a model with a debt-elastic interest-rate premium; (3) a model with convex portfolio adjustment costs; (4) a model with complete asset markets; and (5) a model without stationarity-inducing features. The main finding of the paper is that all models deliver virtually identical dynamics at business-cycle frequencies, as measured by unconditional second moments and impulse response functions. The only noticeable difference among the alternative specifications is that the complete-asset-market model induces smoother consumption dynamics.     

Searching for latent class segments in technological services

The focus in this article is on uncovering segments that describe mobile phone users and on investigating the nature of these segments to get some relevant information. We use a phased approach to reach this objective. First, two segmentation bases have been compared in order to select the best variables for the identification of groups. This is done using a latent class modelling approach. Second, two different segmentation techniques are examined looking for the best model fit. Finally, the model that achieves the best results is applied to the most significant segmentation variables with the purpose of uncovering segments that describe mobile phone users.     

Customer segmentation using unobserved heterogeneity in the perceived-value–loyalty–intentions link

Multiple facets of perceived value perceptions drive loyalty intentions. However, this value–loyalty link is not uniform for all customers. In fact, the present study identifies three different segments that are internally consistent and stable across different service industries, using two data sets: the wireless telecommunication industry (sample size 1122) and the financial services industry (sample size 982). Comparing the results of a single-class solution with finite mixture results confirms the existence of unobserved customer segments. The three established segments are “rationalists”, “functionalists” and “value maximizers”. These results point the way for value-based segmentation in loyalty initiatives and reflect the importance of a multidimensional conceptualization of perceived value, comprising cognitive and affective components. The present results substantiate the fact that assuming a homogeneous value–loyalty link provides a misleading view of the market. The paper derives implications for marketing research and practice in terms of segmentation, positioning, loyalty programs and strategic alliances.     

Export Versus FDI and the Communication of Complex Information

Traditional proximity-concentration models of the decision to serve foreign markets through exports or FDI sales tend to overemphasize physical transport costs and market size while underemphasizing the cost of transmitting information. I augment those models with the importance of interacting with customers and communicating complex information within firms and use these characteristics to predict the location of production. Goods and services requiring direct communication with consumers are more likely to be produced in the destination market. Activities requiring complex within firm communication are more likely to occur at the multinational's headquarters for export, especially when the destination market has weak institutions. These predictions are tested using firm-level data from the Bureau of Economic Analysis US Direct Investment Abroad Benchmark Survey of Multinationals combined with task-level data from the Department of Labor's Occupational Information Network. The approach developed in this paper performs well for both manufacturing and service industries and is robust to a variety of specifications.     

A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting

ABSTRACT

This practitioner note proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis and applies it among the Iranian Fintech companies. In this practitioner note, the K-means clustering algorithm and LRFMP model are combined in the customer segmentation process. After initial clustering, for a better understanding of valuable customers, additional clustering is implemented in segments that needed further investigation. This approach contributes to a better interpretation of different customer segments. Customer segments, consisting of 23524 business customers are analysed based on their characteristics and appropriate strategies are recommended accordingly. The first stage clustering result shows that customers are best segmented into four groups. The first and fourth segments are clustered again and the final 11 groups of customers are determined. This note provides a systematic and practical approach for researchers and practitioners for segmentation, interpretation, and targeting of customers especially in the B2B setting and the Fintech industry and helps managers to make effective marketing strategies and enhance customer relationship and marketing intelligence.     

Issues in the estimation and application of latent structure models of choice

Our paper provides a brief review and summary of issues and advances in the use of latent structure and other finite mixture models in the analysis of choice data. Focus is directed to three primary areas: (1) estimation and computational issues, (2) specification and interpretation issues, and (3) future research issues. We comment on what latent structure models have promised, what has been, to date, delivered, and what we should look forward to in the future.