Customer Segmentation Using Multiple Instance Clustering and Purchasing Behaviors

Pricing and revenue management (RM) techniques have become a popular field of research in hotel management literature. The sector’s background framework and evolution and the widespread use of new technologies have allowed a customer-oriented approach to be taken to pricing and the development of RM tools, while also contributing to better processes in hotel management performance at individual hotel level. Thus, price optimization (PO) methods that seek to maximize hotel revenue are based on inventory scarcity, customer segmentation and pricing. In the hotel sector, as in the airline industry, different pricing policies have a greater impact than competition measurement effects. This is mainly as differentiation strategies and specific policies at hotels can reduce the pressure of a competitive environment. The main contributions of the article are the presentation, description and classification of the principal RM and PO techniques in hotel sector literature.

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Dynamic fuzzy clustering algorithm using cumulative cost curve with application to dynamic customer segmentation

International Journal of Business Information Systems ◽

10.1504/ijbis.2021.10039499 ◽

2021 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Punniyamoorthy Murugesan ◽

Sivaguru Munusamy

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Customer Segmentation ◽

Cost Curve ◽

Fuzzy Clustering Algorithm ◽

Cumulative Cost ◽

Dynamic Fuzzy Clustering

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Customer Segmentation Using Various Machine Learning Techniques

International Journal of Business Intelligence and Data Mining ◽

10.1504/ijbidm.2022.10036753 ◽

2022 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

SAMYUKTHA PALANGAD OTHAYOTH ◽

Raja Muthalagu

Keyword(s):

Machine Learning ◽

Customer Segmentation ◽

Machine Learning Techniques ◽

Learning Techniques

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Fishing for whales: a segmentation model for social casinos

International Journal of Quality and Service Sciences ◽

10.1108/ijqss-06-2017-0053 ◽

2018 ◽

Vol 10 (4) ◽

pp. 400-421 ◽

Cited By ~ 1

Author(s):

Jonathan Barsky

Keyword(s):

Design Methodology ◽

Social Dynamics ◽

Customer Segmentation ◽

Price Sensitivity ◽

Content Type ◽

Casino Industry ◽

Social Casino Games ◽

Spending Patterns ◽

The Social ◽

Insight Into

Purpose The 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/approach The 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. Findings The 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/value This is the first study to develop a rigorous segmentation model of social casino games based on behavioral and psychographic data.

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Ontela PicDeck (B): Customer Segmentation, Targeting, and Positioning

Kellogg School of Management Cases ◽

10.1108/case.kellogg.2016.000248 ◽

2017 ◽

pp. 1-6

Author(s):

Mohanbir Sawhney ◽

Kent Grayson ◽

Patrick Dupree ◽

Christine Hsu ◽

Ryan Metzger ◽

...

Keyword(s):

Quantitative Data ◽

Customer Segmentation ◽

Data Driven ◽

Qualitative Information ◽

Communication Plan ◽

Customer Preferences ◽

Positioning Strategy ◽

Start Up ◽

Average Revenue ◽

Media Habits

Ontela, a technology start-up company, has introduced an innovative service called PicDeck that improves the mobile imaging experience for wireless subscribers. Ontela sells PicDeck to wireless carriers, who in turn private-label the service to their subscribers. Ontela must decide which customer segments it should target for the service and how to create a positioning strategy and a marketing communication plan to promote it. It must also consider the value proposition of the PicDeck service for wireless carriers (its direct customers), who need to be convinced that the service will lead to higher monthly average revenue per user (ARPU) and/or increased subscriber loyalty. Part A of the case provides qualitative information on customer personae that represent different customer segments. Students are asked to develop a targeting and positioning strategy based on this qualitative information. Part B provides quantitative data on customer preferences that can be used to identify response-based customer segments, as well as demographic and media habits information that can be used to profile the segments. Students are asked to revise their recommendations based on the additional quantitative data.The case reinforces the principles of data-driven customer segmentation, discusses the appropriate criteria for selecting segments, and provides a deeper understanding of the benefits and drawbacks of different approaches to identifying and evaluating segments. The case illustrates how the results of data-driven segmentation may run counter to approaches that rely on “gut feel” or qualitative information alone.

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