GPHC: A heuristic clustering method to customer segmentation

Sun, Poly Z. H.; Zuo, Tian-Yu; Liang, Di; Ming, Xinguo; Chen, Zhihua; Qiu, Siqi

doi:10.1016/j.asoc.2021.107677

Cited by 22 publications

(8 citation statements)

References 41 publications

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“…The research could be expanded in the future to include derived clustering algorithms that improve segmentation performance while requiring fewer computational resources and taking less time. Furthermore, in the future, the segmentation can be implemented using the Stream Clustering algorithm, which tracks segments over time without costly recalculations and handles continuous streams of new observations without recompiling the entire model(s); GPHC, a heuristic clustering method to customer segmentation [53]; and a K-means clustering with an adaptive particle swarm optimization algorithm [54]. This research can also be improved with the Improved Augmented Regression Method [55].…”

Section: Discussionmentioning

confidence: 99%

A Novel Approach for Send Time Prediction on Email Marketing

et al. 2022

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In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions’ results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R2 score of 0.91 and a Mean Absolute Error of 0.204. This allows us to estimate the week’s days with a half-day error difference. This work presents promising results for subscriber segmentation based on profile information for predicting the best period for email marketing. In the future, subscribers can be segmented using the Recency, Frequency and Monetary value, the Lifetime Value, or Stream Clustering approaches that allow more personalized and tailored experiences for subscribers. The latter tracks segments over time without costly recalculations and handles continuous streams of new observations without the necessity to recompile the entire model.

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Section: Discussionmentioning

confidence: 99%

A Novel Approach for Send Time Prediction on Email Marketing

et al. 2022

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“…They provide a holistic view of using AI to clustering algorithms implemented in the energy sector (Bogensperger & Fabel, 2021). Sun et al (2021) develop a heuristic clustering method for customer segmentation, termed Gaussian Peak Heuristic Clustering (GPHC) dealing with customer requirement data. They present a practical case to illustrate the effectiveness of GPHC in solving the customer segmentation problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Segmentation With Machine Learning for Online Retail Industry

Turkmen¹

2022

EJSBS

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In this study, a comparative analysis of various techniques is presented on customer segmentation methods based on online retail data. A few unsupervised machine learning (ML) clustering models such as K-means clustering model, hierarchical clustering model, Density-based Spatial Clustering of Applications with Noise (DBSCAN) model and a traditional model based on recency, frequency and monetary (RFM) clustering are evaluated in terms of the insight each model offers. The traditional model is included in the analysis since clustering models are not optimization models and the goodness of unsupervised models could only be evaluated with a practical business approach. The results are shared, and each model is assessed in terms of usability for marketing and communication strategies. At the end, the strengths and weaknesses of each model are discussed, and a methodology is proposed for selecting the best clustering method when facing the customer segmentation problem. A detailed literature review is also presented covering the developments in the field of artificial intelligence, clustering models in ML and examples of customer segmentation implementations in various industries.

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“…Sivaguru et al [ 50 ] focused on dynamic customer segmentation through the use of modified dynamic fuzzy c-means clustering. Sun et al [ 51 ] developed a heuristic clustering method for customer segmentation. They used Gaussian peak heuristic-based clustering (GPHC) and a standardized Gaussian distribution to perform numerical experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

Alsayat

2022

Neural Comput & Applic

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Big social data and user-generated content have emerged as important sources of timely and rich knowledge to detect customers’ behavioral patterns. Revealing customer satisfaction through the use of user-generated content has been a significant issue in business, especially in the tourism and hospitality context. There have been many studies on customer satisfaction that take quantitative survey approaches. However, revealing customer satisfaction using big social data in the form of eWOM (electronic word of mouth) can be an effective way to better understand customers’ demands. In this study, we aim to develop a hybrid methodology based on supervised learning, text mining, and segmentation machine learning approaches to analyze big social data on travelers’ decision-making regarding hotels in Mecca, Saudi Arabia. To do so, we use support vector regression with sequential minimal optimization (SMO), latent Dirichlet allocation (LDA), and k -means approaches to develop the hybrid method. We collect data from travelers’ online reviews of Mecca hotels on TripAdvisor. The data are segmented, and travelers’ satisfaction is revealed for each segment based on their online reviews of hotels. The results show that the method is effective for big social data analysis and traveler segmentation in Mecca hotels. The results are discussed, and several recommendations and strategies for hotel managers are provided to enhance their service quality and improve customer satisfaction.

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GPHC: A heuristic clustering method to customer segmentation

Cited by 22 publications

References 41 publications

A Novel Approach for Send Time Prediction on Email Marketing

A Novel Approach for Send Time Prediction on Email Marketing

Customer Segmentation With Machine Learning for Online Retail Industry

Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca

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