Customer Behavior Mining Framework (CBMF) using clustering and classification techniques

Abdi, Fatemeh; Abolmakarem, Shaghayegh

doi:10.1007/s40092-018-0285-3

Cited by 20 publications

(3 citation statements)

References 38 publications

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“…The proposed methodology partitions each individual object into a distinct cluster, ensuring that no object is assigned to more than a single cluster. Cluster analysis is used to segment consumers for various purposes, including categorizing their behavior (Abdi & Abolmakarem, 2019;Andini & Famiola, 2019;Tabianan et al, 2022).…”

Section: Cluster Analysismentioning

confidence: 99%

Behavior Analysis and Clustering of Consumers Towards Halal Awareness

Dewi Intan Sari,

Irman Hermadi,

Nur Hasanah

2023

JCS

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Due to Indonesia's large Muslim population, the issue of halal is contentious for Indonesians, and many restaurants in Indonesia do not have halal certification. This study investigates the various factors that influence customer behavior and identifies managerially implementable consumer clusters. This study used a quantitative methodology, focusing on a sample of consumers residing in the Greater Jakarta area. Lemeshow sampling was used to gather data from 196 respondents via an online survey. The analysis technique consisted of Structural Equation Modeling (SEM)-Partial Least Square and cluster analysis using the K-means algorithm with the Waikato Environment for Knowledge Analysis (WEKA). Subjective norms, perceived behavioral control, halal awareness, and intention factors have been proven to have positive and significant effects on consumer behavior when choosing a halal restaurant. The findings on consumer clusters that have evolved provide significant managerial implications. Assistance is required from halal institutions, businesses, and consumers with a high level of halal awareness to raise their awareness of halal.

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Section: Cluster Analysismentioning

confidence: 99%

Behavior Analysis and Clustering of Consumers Towards Halal Awareness

Dewi Intan Sari,

Irman Hermadi,

Nur Hasanah

2023

JCS

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“…On the other hand, the study concluded that k-means is very sensitive to the initial random position of each clusters' centroids [11]. Abdi and Abolmakarem [12] proposed a customer behavior mining framework in a telecom company using data mining techniques. The dataset of this work contains 1,000 records and includes information about the customers of a telecom company.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Fraihat

Awad

et al. 2022

IJECE

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<span>Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.</span>

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“…Another study examined the transport industry using the K-Means clustering and CLV models to group customers [10] with the same research objective [13]. It also has similar goals and models [13] to marketing research in telecommunication companies [14]. However, they do not use the CLV model but the neural network to classify priority customers after clustering results.…”

Section: Bmentioning

confidence: 99%

Segmentation using Customers Lifetime Value: Hybrid K-means Clustering and Analytic Hierarchy Process

Rahmadhan

Wasesa

2022

J. Inf. Syst. Eng. Bus. Intell.

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Background: Understanding customers’ electricity consumption patterns is essential for developing predictive analytics, which is needed for effective supply and demand management. Objective: This study aims to understand customers’ segmentation and consumption behaviour using a hybrid approach combining the K-Means clustering, customer lifetime value concept, and analytic hierarchy process. Methods: This study uses more than 16 million records of customers’ electricity consumption data from January 2019 to December 2020. The K-Means clustering identifies the initial market segments. The results were then evaluated and validated using the customer lifetime value concept and analytical hierarchy process. Results: Three customer segments were identified. Segment 1 has 282 business customers with a total capacity of 938,837 kWh, peak load usage of 27,827 kWh, and non-peak load usage of 115,194 kWh. Segment 2 has 508,615 business customers with a total capacity of 4,260 kWh, a peak load of 35 kWh, and a non-peak load of 544 kWh. Segment 3 has 37 business customers with a total capacity of 2,226,351 kWh, a peak load of 123.297 kWh, and a non-peak load of 390,803. Conclusion: A business strategy that could be taken is to base customer relationship management (CRM) on the three-customer segmentation. For the least profitable segment, aside from retail account marketing, a continuous partnership program is needed to increase electricity consumption during the non-peak period. For the highly and moderately profitable segments, a premium business-to-business approach can be applied to accommodate their increasing energy consumption without excessive electricity use in the peak period. Special account executives need to be deployed to handle these customers.

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Customer Behavior Mining Framework (CBMF) using clustering and classification techniques

Cited by 20 publications

References 38 publications

Behavior Analysis and Clustering of Consumers Towards Halal Awareness

Behavior Analysis and Clustering of Consumers Towards Halal Awareness

An efficient enhanced k-means clustering algorithm for best offer prediction in telecom

Segmentation using Customers Lifetime Value: Hybrid K-means Clustering and Analytic Hierarchy Process

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