Distinct Customer Groups Research Articles

Improvement of Productivity by Using Means of Lean Manufacturing, Just in Time, and Production Technology in Thai SMEs Manufacturing Industry

This study investigated the current situations, opportunities, and obstacles in manufacturing industry, and to create a model for improving the efficiency while reducing the levels of inventory. This study gathered information from in-depth interview using snowball sampling from 20 manufacturing companies in Thailand. The results revealed that the organization has implemented lean production principles as a strategic initiative to mitigate losses. The implementation of comprehensive work manuals, offered in diverse languages such as Thai, Burmese, Japanese, and English, serves as an additional mechanism to mitigate losses by ensuring clarity and understanding among the workforce. Furthermore, the integration of modern technologies, encompassing machine technology, robotics and artificial intelligence, is actively pursued to streamline production processes and consequently reduce losses. The results presented that companies refrain from adopting Just-In-Time production systems due to operational challenges associated with fulfilling customer orders. Specifically, frequent machine shutdowns for adjustments are necessitated, leading to elevated unit costs. Manufacturing companies in Thailand continue to harbor potential for growth, despite the inevitable rise in employee wages leading to increased operational costs. To navigate this challenging landscape, companies must remain adaptive, responding not only to shifts in customer preferences but also to advancements in technology, proactive examination of customer needs prior to initiating the production process, a paradigm shift from low-cost production to the premium-grade products addresses the demands of customers seeking superior product quality, and the process of broadening customer bases both overseas and globally, and the analysis of distinct customer groups in identifying new target demographics.

Discussion of Consumers’ Preference for Food Product Traceability Information: Beijing Traceable Tomato Case Study

The paper exemplifies a practical application of combining MNL, RPL and LCM econometric models to study consumer preference heterogeneity in the multi-attributive setting, by analyzing a case study of information traceability preferences of Beijing consumers who buy fresh tomatoes in the post-COVID period. Methodologically, such application of different models (MNL, RPL, LCM) has initially allowed to identify general patterns in Chinese consumers’ preference in the tomato traceability information, then to identify and categorize distinct groups of customers and finally to provide details to their ‘marketing’ profiles towards their willingness to pay. As a result, consumer groups in this study were classified around three key attributes of tomato traceability information which reflect their priorities: consumers from “Price sensitivity” group demonstrated a higher willingness to pay for information on how products were produced (production condition) and products’ certification; “Testing Information Preference” group was willing to pay for the information about tomato’s product quality detection, and “Official Authority Approval Preference” group has developed priority for information on production condition. Such methodological approach provides rather precise characteristics about three different consumer groups, and thus fills in the existing lacunae in the literature and can serve a guiding tool for designing a regional food safety policy. The suggested methodology is transferrable for analyzing consumers’ choices for traceability information about other food products and beyond China.

Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis

The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover, the analysis of customer transitions between different groups has largely been overlooked. This study addresses these gaps by developing an efficient framework that enables businesses to forecast customer behavior, assess the impact of various strategies on each customer separately, and analyze customer transition between segments. This can facilitate providing personalized marketing strategies, fostering a gradual transition toward a desired customer status, and enhancing the overall marketing precision. In this study, we employ time series feature vectors encompassing recency, frequency, monetary value, and lifespan, applying the K-means algorithm with a range of distance metrics for customer segmentation along with classification algorithms to predict customer behavior. Leveraging counterfactual analysis, we establish a solution for analyzing customer transitions between groups and evaluating personalized marketing strategies. Our findings underscore the superior performance of the Euclidean distance metric, closely followed by the Manhattan distance, in distinguishing the patterns in time series customer behavior, with logistic regression excelling in predicting customer status. This study enables decision-makers to forecast the impact of diverse marketing strategies on customer behavior which facilitates customer retention and engagement through well-informed decisions.

Research on the Influencing Factors of Consumer Buying Behavior

Today's businesses depend greatly on the buying habits of its customers. Understanding consumer behavior is essential for businesses to optimize their marketing strategies and boost customer satisfaction in today's fiercely competitive industry. This study examines customer personalities and their influence on purchasing behavior by using the purchase histories and demographic information of 2,240 clients. 10 research sources from Google Scholar are used to analyze the key factors that affect consumer behavior and the effectiveness of different clustering algorithms in identifying distinct customer groups in order to improve the accuracy of the study. The study emphasizes how important lifestyle, psychographic, and demographic characteristics are for affecting customer behavior. Businesses may better understand their clients and create marketing strategies that are tailored to their requirements by utilizing these characteristics. The work also raises the possibility of using clustering techniques to improve marketing efforts and increase client loyalty. Businesses may more effectively target their marketing efforts and boost customer satisfaction by categorizing customers into several categories based on their preferences and behavior patterns. As a result, this research offers insightful information about customer purchase habits. Businesses may better understand their clients and target their marketing initiatives to fit their demands by employing demographic and behavioral data. In doing so, companies may boost client pleasure and loyalty, eventually leading to long-term success.

A combination of forensic-based investigation algorithm and density peak-based fuzzy clustering for custom segmentation

Custom segmentation is a process of classifying potential customers based on their mutual features such as shopping habits, consumption trends, and demand to provide an effective marketing campaign for each customer group. Data clustering is one of the most common methods for custom segmentation. This study proposed a novel clustering method that employs density peak-based fuzzy c-means (DP-FCM) and forensic-based investigation (FBI) algorithms. The proposed method (denoted as DP-FBI-FCM) aims to provide an effective clustering technique that can exploit the global optimal solution for custom segmentation problems. Besides, the proposed DP-FBI-FCM is used to segment wholesale customer data of a supermarket. As a result, four distinct customer groups are classified. Businesses can implement different strategies in each cluster to retain and attract their customers.

Dynamic Currency Conversion Payment Options Specifically Harm Less Financially Literate Customers

International customers are increasingly exposed to dynamic currency conversion, that is, the option during checkout to proceed with a transaction in the home currency instead of a foreign currency. As conversion markups can be sizable, it is relevant to gain insight into how distinct groups of customers react at point-of-sale terminals to different markups and different ways of presenting information. The authors build on research on effort–accuracy trade-offs to theorize how international customers with different levels of financial literacy react to conversion markups, different degrees of information transparency, and default settings. The authors find that financial literacy moderates the effect of markups, information, and defaults, and they design an intervention that eradicates the effect of financial literacy on dynamic currency conversion usage. The results contribute to our understanding of how customers with different levels of financial literacy respond to conversion markups, to public policies that are intended to protect customer interests, and to evil defaults used by commercial parties that seek to steer customers to noneconomical options. The authors discuss the implications of these findings for public policy making and research.

An embedded deep-clustering-based load profiling framework

• A novel load profiling framework using deep clustering to for feature reduction. • Framework eliminates preprocessing in hybrid load profiling algorithms. • Autoencoders capture main non-linear features to separate different load groups. • Improved SI on real-world big data compared to classical clustering techniques. • Framework produces robust load profiles that describe distinct customer groups. Load profiling is an essential step in several smart meter analytics tasks, such as forecasting and planning, that directly impact sustainable energy management. As most real-world smart meter data are unlabeled, the unsupervised learning clustering-driven load profiling approach aims to group related customers based on usage trends. However, due to the curse of dimensionality, conventional clustering algorithms perform poorly and often lead to inadequate load curves. The proposed load profiling framework’s novelty is twofold. First, using an autoencoder the framework represents data as a hierarchy within the layers of the deep network, allowing for dimensionality reduction and highly nonlinear decision separation between clusters at the autoencoder bottleneck level. This is achieved by implementing autoencoder-based clustering that automatically converts smart meter data into more clustering-friendly representations that retain the original data characteristics. Second, the framework integrates dimensionality reduction and clustering into a single end-to-end unsupervised learning framework. The deep clustering framework is then tested on two real-world smart meter datasets, and the experimental results show that the proposed framework produces significantly better load profiles when compared to classical clustering algorithms as well as previous hybrid frameworks proposed in the literature. The paper concludes with a discussion of the results and a set of future directions for improving the load profiling task.

An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation

Nowadays, banks use data mining and business intelligence tools and techniques to analyze their customers’ behavior. Customer segmentation is a widely adopted analytical tool to identify distinct groups of customers. Most studies have adopted a static segmentation approach, leading to missing important trends and patterns. In this study, we propose a framework that represents each customer behavior as a time-series sequence of the Recency, Frequency, and Monetary variables, and then exploits time-series clustering algorithms to carry out customer segmentation. The framework consists of state-of-the-art clustering algorithms, including hierarchical, spectral, and k-shape algorithms to divide customers into homogeneous groups and implement point of sale devices transaction data of grocery and appliance retailers. We divide customers into four segments, analyze the behavioral trends, and provide marketing suggestions for each segment. Our results show that, in terms of the computed validity indices, the best clustering model for grocery retailers is reached using hierarchical clustering with the Complexity-Invariant Distance measure, and for appliance retailers, the best segmentation is achieved by applying the spectral clustering with the Complexity-Invariant Distance measure.

Digital platforms’ boundaries: The interplay of firm scope, platform sides, and digital interfaces

This article explores what factors drive digital platform firms to set or modify their boundaries. Building on economics, strategic management, and information systems research, I suggest that digital platforms make strategic decisions over three distinct types of interrelated boundaries: (1) the scope of the platform firm (what assets are owned, what labor is employed, and what activities are performed by the firm), (2) the configuration and composition of the platform's sides (which distinct groups of customers have access to the platform), and (3) the digital interfaces (that specify the 2-way exchange of data between the platform firm and each of its sides). In this article, I explore the interdependence between these seemingly separate decisions and the role of some important moderating variables. These moderators include whether the platform is a transaction or an innovation platform, and the extent to which the platform has developed from its initial formation stage. My work explains why we see so much variation in boundaries across platforms and over time.

Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids

The extensive deployment of smart meters in millions of households provides a huge amount of individual electricity consumption data for demand side analysis at a fine granularity. Different from traditional aggregated system-level data, smart meter data is more irregular and unpredictable. As a result, probabilistic load forecasting (PLF), which can provide a better understanding of the uncertainty and volatility in future demand, is critical to constructing energy-efficient and reliable smart grids. In this article, a recently developed technique called Bayesian deep learning is employed to solve this challenging problem. In particular, a novel multitask PLF framework based on Bayesian deep learning is proposed to quantify the shared uncertainties across distinct customer groups while accounting for their differences. Further, a clustering-based pooling method is designed to increase the data diversity and volume for the framework. This not only addresses the problem of overfitting but also improves the predictive performance. Numerical results are presented which demonstrate that the proposed framework provides superior probabilistic forecasting accuracy over conventional methods.

Two-Sidedness, Product Complementarity and the Platform-Specific Nature of Externalities: A Comment on Filistrucchi

In his article “Two-Sided Vs. Complementary Products” Filistrucchi explains in what respect two-sided platforms are different from firms producing complementary products, in spite of the similarity in pricing strategies. For him, the distinguishing feature of two-sided platforms is the existence of two distinct customer groups and externalities between those groups that remain uncompensated. Pricing practices by two-sided platforms internalize such externalities. In my view, it is not so much the internalization of externalities that explains pricing practices, but rather the platform-specific nature of the externalities – that is, that more customers on one side of a platform only benefit customers on the other side of the same platform, not those on the other side of other platforms. I believe that there is no essential difference with firms producing firm-specific complements. In this article I try to explain why.

Fishing for whales: a segmentation model for social casinos

PurposeThe purpose of this paper is to introduce a new customer segmentation model for the social casino industry. The key contribution of this model is the introduction of original psychographic/taste data, including a player emotions scale.Design/methodology/approachThe data for this research are based on player feedback from 22 countries, with evaluations of the top 100 social casino titles (apps). The new segmentation model splits the industry into distinct customer groups based on spending patterns, behavioral dimensions and attitudinal dimensions.FindingsThe results provide insight into game mechanics, social dynamics, player emotions, spend, price sensitivity, loyalty and other elements that impact monetization. Critical behaviors and preferences of social casino players that will help companies better understand and connect with their target customers are described.Originality/valueThis is the first study to develop a rigorous segmentation model of social casino games based on behavioral and psychographic data.

Exploring loyalty to specialty foods among tourists and residents

PurposeThe purpose of this paper is to examine a specialty food loyalty model which includes perceived quality, satisfaction, and motivation, and to assess the model’s applicability in two distinct groups of customers: tourists and residents.Design/methodology/approachData were collected from 455 specialty food customers in Bandung, Indonesia. Variance-based structural equation modeling (partial least squares (PLS)) was used to examine the relationship between the drivers (perceived quality, satisfaction and motivation) and loyalty according to the data presented by tourists and residents.FindingsThis study indicates that the perception of quality is an important factor affecting tourists’ and residents’ satisfaction with, and loyalty to, a product. Furthermore, this study suggests that motivation factors are important for tourists and residents in regard to developing loyalty to specialty foods.Practical implicationsThis study provides a venue for retail managers and producers to improve their business performance by developing specialty foods of high quality. To improve their quality, this research suggests that managers and retailers focus on innovation based on exotic and unique traditional food reflecting the richness of local culture. To ensure their loyalty, customers of specialty foods need to be motivated by internal and external factors.Originality/valueThis study is one of the first to examine the formation of specialty food loyalty in two distinct groups of customers: tourists and residents.

Investigating the segment specific preferences for hedonic and utilitarian online-shop characteristics: the case of German online wine shops

Recently the market for grocery online-shopping is expanding. Therefore, this study analyse the influencing factors of online shoppers' preferences for online wine shops with an adaptive choice-based conjoint analysis (ACBC). As these factors in general differ for hedonic and utilitarian goods, we investigated what characteristics presented in the online-shop are most important to customers of a primarily hedonic product like wine. Surprisingly, respondents in general favoured utilitarian over hedonic characteristic levels. In addition, we identified two distinct customer groups by applying a preference-based market segmentation (CCEA). The price/efficiency-seeking group favoured utilitarian characteristics. The quality-seeking group favoured hedonic over utilitarian characteristics, but with a strong importance of some utilitarian website characteristics. Finally management implications are presented.

Market Definition for Multi-Sided Platforms: A Legal Reappraisal

It is frequently assumed that the outcome of today’s most discussed competition law cases hinges on market definition. In the context of multi-sided platforms, delimiting the relevant product market is a particularly delicate and complex task. Thus, this article seeks to analyse, from both an economic and a legal perspective, how relevant product markets for multi-sided platforms are properly defined under European competition law. Multi-sided platforms act as intermediaries which enable distinct groups of customers (also called platform-sides) to interact with each other. Multi-sided platforms have the possibility to set different prices for each group of customers, therefore econometric tools such as the small-but-significant-non-transitory increase-in-price test (SSNIP-test) must be applied with caution. It is argued that multi-sidedness can be considered a question of degree, therefore, for the purpose of market definition, it is necessary to distinguish different types of multi-sided platforms. The stronger the indirect network effects involved, the more the market definition deviates from traditional approaches. Indeed, the established legal framework allows a sound categorization of various forms of multi-sided platforms. In the context of multi-sided platforms, markets are regularly closely interrelated, therefore, the competitive assessment would be incomplete without an intermarket-analysis.

Yours, mine, and ours: A user-centric analysis of opportunities and challenges in peer-to-peer asset sharing

The sharing economy is growing globally in terms of user numbers, service providers, and novel concepts. Peer-to-peer (P2P) asset sharing, or asset rental between private individuals, has attracted the attention of entrepreneurs and researchers alike. P2P asset-sharing networks need to focus on two distinct customer groups: (1) asset owners willing to rent out their assets, and (2) renters interested in renting others’ assets. Despite consumers’ high interest in P2P asset sharing, participation rates lag projections, which is mainly attributable to lack of participating asset owners. This could be problematic for P2P networks as they do not own assets; instead, they rely on a sufficient number of asset owners to participate. Detailed indications on the participation motives of users are required to distinctly position P2P asset sharing and enhance communication of consumer-relevant benefits. To this end, we have engaged in a detailed investigation of participation motives in the P2P car-sharing context. We have conducted in-depth interviews with car owners and renters to derive usage types that represent consumer decision profiles that participate in P2P car-sharing services. Based on our findings, we provide extensive recommendations to entrepreneurs in the P2P asset-sharing market.

The Emerging High-Court Jurisprudence on the Antitrust Analysis of Multisided Platforms

Between September 2014 and September 2016 high courts in multiple jurisdictions released five decisions that address applying competition law to matchmakers that operate virtual or physical platforms for connecting multiple groups of customers. The decisions rely, directly or indirectly, on the economic literature on multisided platforms that commenced around 2000. The high courts all recognize that it is necessary to consider the several distinct groups of customers and their interactions in evaluating whether business practices are anticompetitive.

Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms

Media content distribution has changed extensively in the past decade. Content that was once distributed through traditional formats such as television, radio, and print is now available through contemporary digital formats such as smartphone and tablet apps, with many possible versions (e.g., presence or absence of ads). Consequently, many media firms that face heterogeneous markets of consumers with varying content consumption preferences are now offering “menus” of multiformat and multiversion subscription bundles for their customers to choose from. Yet, little systematic model-based guidance exists for configuring and pricing menu options. Moreover, most media firms are audience-building platforms that serve at least two distinct customer groups (content consumers and advertisers) with interrelated demands. Therefore, constructing a menu of content subscription bundles that maximizes total profit from both consumers and advertisers is a formidable challenge. This research proposes a theory-driven implementable model-based approach that can aid media platforms in addressing this challenge. The proposed approach is then demonstrated for a U.S. newspaper, and insights into profit-maximizing menus under various business model and format strategies are provided.

How to sell refurbished smartphones? An investigation of different customer groups and appropriate incentives

As smartphones are being replaced at an increasing rate, it becomes more and more important to find ways to reuse (parts of) existing phones. This research investigates the potential of selling refurbished smartphones using a quantitative study. An online survey was conducted questioning 250 respondents, in which the perceived consumer risks and benefits associated with refurbished phones were investigated. Furthermore, we studied the impact of various incentives that companies can employ to improve consumers’ purchase intention of refurbished phones. Through a cluster analysis six distinct customer groups were identified. Our sample demonstrated a positive attitude towards refurbished smartphones, with most potential for three out of our six customer groups (46% of our sample). This indicates that refurbished smartphones can be a market success. It was found that perceived environmental benefits and awareness of refurbishing have a positive impact on the consumers’ purchase intention for refurbished smartphones. Most of the highest scoring incentives for purchasing refurbished smartphones were product-related and included improved battery life, guaranteed software updates and upgraded performance, indicating a need for continued performance. This research contributes to our theoretical understanding of consumer responses to refurbished smartphones and can help companies in launching these products successfully.

Optimizing a Menu of Multi Format Subscription Plans for Advertising Supported Media Platforms: A Model and Application in the Daily Newspaper Industry

Distribution of content by media companies has greatly transformed in the past decade. Content, which was once distributed through e.g., television, radio and print formats, is now available through contemporary digital formats with many possible versions, such as smartphone and tablet apps with and without ads etc. Consequently, many media firms facing markets comprised of heterogeneous consumers with varying content consumption preferences are offering ‘menus’ of multi format-version subscription bundles for their consumers to choose from. So far, however, there is little systematic model-based guidance on configuring and pricing menu options available to such content marketers. Moreover, most media firms are ‘audience-building platforms’ that serve at least two distinct customer groups (content consumers and advertisers) with inter-related demands. Therefore, constructing a menu of content subscription bundles that maximizes total profit from both consumers and advertisers becomes an even more complex problem, not yet addressed in the marketing literature. Therefore, this article proposes a theory-driven implementable model-based approach that can aid media platforms in optimally designing their subscription menus. The proposed framework is demonstrated for a U.S. newspaper and profit maximizing menus for various strategies are determined.