Customer Segmentation via Data Mining Techniques: State-of-the-Art Review

Das, Saumendra; Nayak, Janmenjoy

doi:10.1007/978-981-16-9447-9_38

Cited by 13 publications

(8 citation statements)

References 69 publications

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“…Researchers should consider utilizing an unsupervised or supervised technique to avoid this problem. The inputs and outputs of the supervised approach classification algorithm are correctly mapped [5].…”

Section: Segmentation Techniques: Techniques For Segmenting Informationmentioning

confidence: 99%

Data Mining for Dynamic Customer Segmentation: Unraveling Insights

Srinivasa,

Bargavi

2024

IJMRSET

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Effective decision-making is essential for every firm to earn high income. These days, there is intense rivalry, and every company is advancing using a unique set of techniques. We ought to make an informed choice based on evidence. Since each client is unique, we have no idea what they enjoy or what they purchase. But by using a variety of algorithms on the dataset, one may use machine learning techniques to filter through the data and identify the target group. In the absence of this, identifying a group of individuals with like interests and personalities within a sizable dataset will be exceedingly challenging and no better methods exist. The use of K-Mean clustering for customer segmentation aids in grouping data with comparable characteristics, which benefits the firm the most. We will use the elbow approach to determine the number of clusters, and then we will visualize the results.

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Section: Segmentation Techniques: Techniques For Segmenting Informationmentioning

confidence: 99%

Data Mining for Dynamic Customer Segmentation: Unraveling Insights

Srinivasa,

Bargavi

2024

IJMRSET

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“…Most segmentation algorithms have significant advantages and disadvantages, so it is challenging, even for marketing professionals, to consider the right technique or algorithm (Das & Nayak, 2022). Based on the surveys of Sari et al (2016), Cooil et al (2008), McKernan (2018), and Das and Nayak (2022), the current segmentation methods can be categorized as follows. Ranking—methods that provide a full ranking of customers, versus methods that provide only the division of groups and segments without ranking. Updating—methods that are updated yearly or quarterly, versus dynamic methods that are updated frequently by demand, or continuously. Objectivity—subjective methods based on expert evaluation and salespeople (for example, AHP, Delphi), versus objective methods that are based on firmed past data, so everyone who uses the method will get the same result. Number of customers—methods suitable for classifying a relatively small number of customers with a small number of orders per year, versus methods that are appropriate to handle a high volume of data and an unlimited number of customers and orders. Technique—simple statistical methods such as descriptive analyses, versus more rigorous segmentation methods; these methods include k‐means clustering, decision trees, cluster analysis, cluster‐wise regression, multiple regression, linear programming, discrimination analysis, latent class structure, inductive learning techniques, soft computing techniques, Chi‐Square automatic interaction detector, and data mining. Techniques related to data mining include artificial neural networks, fuzzy logic, machine learning, and evolutionary methods. Forecasting—methods that can forecast the segment where a new customer will belong, versus methods that classify only customers with enough purchase history. Criteria—methods that classify customers according to a single criterion such as profit or purchasing volume, versus methods that classify customers according to multi‐criteria analysis (MCDA).…”

Section: Background On Customer Segmentationmentioning

confidence: 99%

“…As was presented before, many methods, techniques, and algorithms exist for customer segmentation and ranking. Most segmentation algorithms have significant advantages and disadvantages, so it is challenging, even for marketing professionals, to consider the right technique or algorithm (Das & Nayak, 2022). Based on the surveys of Sari et al (2016), Cooil et al (2008), McKernan (2018), and Das and Nayak (2022), the current segmentation methods can be categorized as follows.…”

Section: Background On Customer Segmentationmentioning

confidence: 99%

A decision‐making support system module for customer segmentation and ranking

Hadad

Keren

2022

Expert Systems

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Many businesses have their own method of segmentation and of customer evaluation to help them give the appropriate managerial attention to each segment and each customer. This paper proposes a more refined and objective decision‐making support system module that allows segmentation and full ranking of customers. It offers several improvements and advantages over the state‐of‐the‐art methods. The module is based on customer criteria with quantitative values that can be extracted from the organizational information system. Customer scores are calculated objectively on the basis of measurable criteria without the need for human evaluation. The module determines the relative location of each customer within that customer's segment (for example, Platinum, Gold, Silver, and Bronze), tracks changes that occur over time, and enables a full and precise ranking of the customers according to company‐defined criteria. The module can be computerized and results can be generated quickly at any time, using up‐to‐date data. The module's design was based on feedback from a survey conducted among 39 managers and its applicability was successfully demonstrated in a real‐world case study. The decision‐makers in the organization where the case study was conducted stated that they would apply the proposed method quarterly (four times a year) instead of once a year. It was also found that the proposed method saves about 90% of the time and resources required to prepare a customer portfolio management and customer ranking compared to the subjective method that was used.

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“…The main result of this study is the creation of a customer profile and forecast for the sale of goods, which will assist decision-makers in making strategic marketing decisions. The study is expected to provide valuable insights for companies looking to improve their direct marketing efforts and increase sales performance through data mining-based customer profiling [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Customer profiling, segmentation, and sales prediction using AI in direct marketing

Kasem,

Hamada,

Taj-Eddin

2023

Neural Comput & Applic

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In the current business environment, where the customer is the primary focus, effective communication between marketing and senior management is vital for success. Effective customer profiling is a cornerstone of strategic decision-making for digital start-ups seeking sustainable growth and customer satisfaction. This research investigates the clustering of customers based on recency, frequency, and monetary (RFM) analysis and employs validation metrics to derive optimal clusters. The K-means clustering algorithm, coupled with the Elbow method, Silhouette coefficient, and Gap Statistics method, facilitates the identification of distinct customer segments. The study unveils three primary clusters with unique characteristics: new customers (Cluster A), best customers (Cluster B), and intermittent customers (Cluster C). For platform-based Edutech start-ups, Cluster A underscores the importance of tailored learning content and support, Cluster B emphasizes personalized incentives, and Cluster C suggests re-engagement strategies. By understanding and addressing the diverse needs of these clusters, digital start-ups can forge enduring connections, optimize customer engagement, and fuel sustainable business growth.

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Customer Segmentation via Data Mining Techniques: State-of-the-Art Review

Cited by 13 publications

References 69 publications

Data Mining for Dynamic Customer Segmentation: Unraveling Insights

Data Mining for Dynamic Customer Segmentation: Unraveling Insights

A decision‐making support system module for customer segmentation and ranking

Customer profiling, segmentation, and sales prediction using AI in direct marketing

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