Data-driven Market Segmentation Research Articles

Data-Driven Market Segmentation: K-means Clustering and STP Analysis in Mainland China's Sportswear Industry

Post-Covid-19, mainland China's economy is gradually recovering, leading to increased sportswear sales. This study integrates STP theory (segmentation, targeting, positioning) with the K-means clustering algorithm to analyze the sportswear market in mainland China. Consumer data was collected via a questionnaire survey, pre-processed, and cleaned before applying K-means clustering to segment consumers into four distinct groups. The analysis reveals significant differences in purchasing habits across these segments. This data-driven approach offers sportswear companies insights for targeted marketing strategies, enhancing the precision and effectiveness of their marketing efforts in mainland China.

Data-Driven Market Segmentation: The Case of Medical Tourists in Lebanon

Data-Driven Market Segmentation: The Case of Medical Tourists in Lebanon

Data-driven market segmentation in hospitality using unsupervised machine learning

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by clustering guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit.A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one cluster to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning

Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.

On the Heterogeneity of Preferences for Disability Services

ABSTRACT This paper investigates heterogeneity of preferences for disability services within the theoretical framework of consumption values. We conducted interviews with people with a disability and disability service providers to develop survey items, then conducted a survey with 2000 adult Australian residents who either had a disability or were carers of a person with a disability. After conducting descriptive analyses and data-driven market segmentation, findings revealed that, at the aggregate level, basic or functional benefits of disability services are most important. However, when accounting for heterogeneity, very distinct benefit patterns emerge, pointing to the substantial potential for improving disability services by catering to distinct market segment needs. These insights have the potential to improve disability service provision, thus maximally harvesting the opportunities from disability service models that now often include commercial providers, and enabling people with disabilities to make optimal choices in relation to both services and providers.

Data-Driven Market Segmentation in Insurance Industry and Other Related Sectors

This paper talks about various approaches and models on customer segmentation in the insurance industry and other related sectors. In today's business world, especially the customer-centered industry, the most critical task is to find the right customers and serve the customers the way that most suits them. In this paper, we put our focus on the insurance industry for several considerations. One insurance company can possess hundreds of different policies, so it is crucial for policy issuers to find suitable policies for different customers. Considering the complexity and variability of different policies, insurance companies view customer segmentation as necessary and the key point for companies to compete well. Therefore, we select the insurance industry to study the effect of data-driven approaches on customer segmentation. In the first part, we discussed the need for a new approach to classify the customers and several advantages of the data-driven approach over the traditional method. In the second part of the paper, segmentation approaches such as K-means clustering, hybrid clustering, rule mining, and decision tree are discussed respectively about their processes and features. In the third part, we talked about the two current customer segmentation applications that are widely used today. We also talked about the segmentation systems in determining the risk of transmission of COVID-19. In the last part, we conclude the paper with the comparison of different approaches we discussed.

Improving revenue performance with third-degree price discrimination in the cruise industry

Abstract The leisure cruise industry has enjoyed high levels of growth for nearly five decades due in part to traveler interest in the cruise experience, but also to relatively lower pricing. Although revenue management of cruise fares is now standard practice, there are untapped opportunities to improve yields through data-driven market segmentation and third-degree price discrimination. This paper uses a finite mixture modeling approach to develop, empirically validate, and compare pricing models. By unveiling segments of travelers based on individual attributes, third-degree price discrimination can improve target marketing, the timing and appeal of price discounts, and the matching of variable demand with fixed, though differentiated, room supply. Empirical results from running thousands of simulations with pricing data from one of the world’s largest cruise lines show that the segmentation analysis using third-degree price discrimination can increase fare revenue more than four percent. The modeling approach used in this research extends the emerging literature on revenue management in the cruise industry and offers meaningful managerial implications for advanced pricing tactics and revenue management.

Impulse behavior in economic crisis: a data driven market segmentation

PurposeClustering is a highly popular and widely used tool for identifying data-based market segments. The purpose of this paper is to apply cluster analysis to identify homogeneous subgroups among impulse buyers based on their demographic characteristics and their preference of atmospheric elements.Design/methodology/approachBased on a survey approach with 480 fashion consumers, the study discusses impulse purchases during financial crisis. SPSS process is used to determine the effect of atmospherics on impulse purchases as well as the moderating role of demographics. Cluster analysis (k-means method) is used to determine specific segments of impulsive consumption.FindingsDespite a significant effect of atmospherics on consumers’ impulsiveness, the frequency of purchases is not significantly affected indicating that the economic environment may shape impulsive behavior. Gender, age and education seem to moderate the above relationship. Demographics and store atmospherics define specific segments of impulse buyers.Research limitations/implicationsThe study identifies the clusters formed through the interaction of atmospherics with demographics. It further identifies the impact of atmospheric factors in the case of an economy in crisis. In times of financial hardship, store atmospherics, although appealing, do not seem to adequately promote impulse buying behavior. Understanding consumer’s impulsive behavior based on distinctive profiles is of outmost importance to retailers seeking to increase consumption, in particular under conditions of financial hardship.Originality/valueThe present study explores the role of environmental characteristics on consumers’ impulse behavior amidst a financial crisis and identifies the characteristics of specific segments of consumption.

Using segment level stability to select target segments in data-driven market segmentation studies

Market segmentation is widely used by industry to select the most promising target segment. Most organisations are interested in finding one or a small number of target segments to focus on. Yet, traditional criteria used to select a segmentation solution assess the global quality of the segmentation solution. This approach comes at the risk of selecting a segmentation solution with good overall quality criteria which, however, does not contain groups of consumers representing particularly attractive target segments. The approach we propose helps managers to identify segmentation solutions containing attractive individual segments (e.g., more profitable), irrespective of the quality of the global segmentation solution. We demonstrate the functioning of the newly proposed criteria using two empirical data sets. The new criteria prove to be able to identify segmentation solutions containing individual attractive segments which are not detected using traditional quality criteria for the overall segmentation solution.

How to Avoid Random Market Segmentation Solutions

Tourism researchers and the tourism industry rely heavily on data-driven market segmentation analysis for both knowledge development and market insight. Most algorithms used in data-driven market segmentation are exploratory; they do not generate one single stable result. Only when data are well-structured (when very clear, distinct market segments exist in the data) are repeated calculations likely to generate the same segmentation solution. When data lack structure, which is frequently the case in empirical consumer data sets, repeated calculations lead to different solutions. Running a market segmentation analysis once only can therefore lead to an entirely random solution that does not represent a strong foundation for developing a long-term market segmentation strategy. The present study (1) explains the problem, (2) assesses how high the risk is of random solutions occurring in tourism market segmentation studies, and (3) recommends an approach that can be used to avoid random solutions.

Increasing sample size compensates for data problems in segmentation studies

Survey data frequently serve as the basis for market segmentation studies. Survey data, however, are prone to a range of biases. Little is known about the effects of such biases on the quality of data-driven market segmentation solutions. This study uses artificial data sets of known structure to study the effects of data problems on segment recovery. Some of the data problems under study are partially under the control of market research companies, some are outside their control. Results indicate that (1) insufficient sample sizes lead to suboptimal segmentation solutions; (2) biases in survey data have a strong negative effect on segment recovery; (3) increasing the sample size can compensate for some biases; (4) the effect of sample size increase on segment recovery demonstrates decreasing marginal returns; and—for highly detrimental biases—(5) improvement in segment recovery at high sample size levels occurs only if additional data is free of bias.

Using graphical statistics to better understand market segmentation solutions

Market segmentation lies ‘at the heart of successful marketing’ (McDonald 2010), yet market segmentation solutions are not trivial to interpret, especially if consumers are segmented using post hoc or a posteriori or data-driven segmentation, where several consumer characteristics are analysed simultaneously to identify or construct market segments. In fact, 65% of marketing managers admit to having difficulties with the interpretation of data-driven market segmentation solutions. In this study we develop novel ways of visualising segmentation solutions using graphical statistics methodology. The proposed plots help academics and practitioners to interpret complex market segmentation solutions, thus improving the practical usability of market segmentation, reducing the risk of misinterpretation and contributing to closing the much-lamented ‘theory–practice divide’ in market segmentation.

Required Sample Sizes for Data-Driven Market Segmentation Analyses in Tourism

Data analysts in industry and academia make heavy use of market segmentation analysis to develop tourism knowledge and select commercially attractive target segments. Within academic research alone, approximately 5% of published articles use market segmentation. However, the validity of data-driven market segmentation analyses depends on having available a sample of adequate size. Moreover, no guidance exists for determining what an adequate sample size is. In the present simulation study using artificial data of known structure, the impact of the difficulty of the segmentation task on the required sample size is analyzed in dependence of the number of variables in the segmentation base. Under all simulated data circumstances, a sample size of 70 times the number of variables proves to be adequate. This finding is of substantial practical importance because it will provide guidance to data analysts in academia and industry who wish to conduct reliable and valid segmentation studies.

Biclustering

Data-driven market segmentation is a popular and widely used segmentation method in tourism. It aims to identify market segments among tourists who are similar to each other, thus allowing a targeted marketing mix to be developed. Typically data used to segment tourists are characterized by small numbers of respondents and large numbers of survey questions. Small samples and numerous questions cause serious methodological problems that have typically been addressed by using factor-cluster analysis to reduce the dimensionality of data. Recently, factor-cluster analysis has been shown as an unacceptable solution to the problem of high data dimensionality in segmentation. In this article, the authors introduce biclustering, a novel approach to address the problem of high dimensionality in tourism segmentation studies. We discuss the circumstances in which biclustering should be used rather than parametric or nonparametric grouping techniques. An illustrative example of how biclustering is computed is also provided.

What Motivates Which Volunteers? Psychographic Heterogeneity Among Volunteers in Australia

Six psychographic segments of volunteers in Australia are constructed on the basis of their volunteering motivations. The resulting segments include “classic volunteers,” whose motivations are threefold: doing something worthwhile; personal satisfaction; and helping others. “Dedicated volunteers” perceive each one of the motives for volunteering as relevant, while “personally involved volunteers” donate time because of someone they know in the organization, most likely their child. “Volunteers for personal satisfaction” and “altruists” primarily wish to help others, and finally, “niche volunteers” typically have fewer and more specific drivers motivating them to donate time, for example, to gain work experience. The segments are externally validated and demonstrate significantly different socio-demographic profiles. Consequently, it seems that motivation-based data-driven market segmentation represents a useful way of gaining insight into heterogeneity amongst volunteers. Such insight can be used by volunteering organizations to more effectively target segments with customized messages.

A Review of Data-Driven Market Segmentation in Tourism

Clustering has become a very popular way of identifying market segments based on survey data. The number of published segmentation studies has strongly increased since the milestone publication on benefit segmentation by Haley in 1968. Nevertheless, numerous very fundamental weaknesses are permanently encountered when studying segmentation studies in detail, thus making the results reported more than questionable. This article illustrates how data-driven segmentation studies are typically conducted in the field of tourism research, provides a systematic overview of applications published in the last decades, outlines critical issues that often lead to overestimation of the validity of results and offers solutions or recommendations that help both the researcher to keep the critical issues in mind as well as the management to evaluate the validity and usefulness of the study.