Machine Learning-Based Customer Segmentation and Personalised Marketing in Financial Services

Chandra Srinivas Potluri · L.V.R Manoj Kumar · Mr.Kedir Geletu Allo · Yaregal Awoke · Abdilkerim Asrar Seman

8 citations0/0 files storedInternational Conference on Communication, Computer Sciences and Engineering (IC3SE)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Customer churn and segmentation Banking Sector Performance and Management Data Mining and Machine Learning Applications

Abstract

Customer segmentation is a way of dividing a clientele into groups, or “the consumer segments,” with the intention of assembling customers with similar characteristics in each section. Gender, age, interests, and different spending patterns are just a few of the marketing-relevant characteristics that serve as the foundation for segmentation. Segmentation solutions enable banks to provide more individualized products and services. Marketers can select the best channels of advertising for their intended consumer base, establish profitable new market niches, launch novel products and services, and decide what promotional content to offer by getting a deeper grasp of the characteristics of their target audience. Machine learning can help people make more informed choices in the future is used in this situation. Different algorithms are used to uncover concealed trends in the data. Applying segmentation makes it clear which of the eluded segments to target. Machine learning can help people make more informed choices in the future is used in this situation. Different algorithms are used to uncover undetected trends in the data. Applying segmentation makes it clear which of the eluded segments to target. This study segmented the data regarding cardholders of different banks using silhouette approaches, elbow approaches, and K-means cluster analysis.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 8 citations exist upstream.

References

2018Customer Segmentation using K-means Clustering 2011The basis of market segmentation: a critical review of literature 2013Soft computing applications in customer segmentation: State-of-art review and critique 2003Segmentation: Identification, intuition, and implementation 2017A comparative study of customer churn prediction in telecom industry using ensemble based classifiers 2013Handbook of Market Segmentation 2019Customer Segmentation Using Hierarchical Agglomerative Clustering 2023A Study on the Application of Expert Systems as a Support System for Business Decisions

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.