Customer Behavior Analysis Using Rough Set Approach

D. Prabha · Ilango Krishnamurthi

17 citations0/0 files storedJournal of theoretical and applied electronic commerce research

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 Data Mining and Machine Learning Applications Rough Sets and Fuzzy Logic

Abstract

The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors.The customer behavior is analyzed by choosing important attributes in the customer database.The customers are then segmented into groups according to their attribute values.The rules are generated using rule induction algorithms to describe the customers in each group.These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers.In this paper a new rule algorithm has been proposed based on the concepts of rough set theory.Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm.Real data set of the customer transaction is used for analysis.Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data.The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm.

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 — 17 citations exist upstream.

References

2008Application of data mining techniques in customer relationship management: A literature review and classification 2004Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management 1999Building Data Mining Applications for CRM 2008Classifying the segmentation of customer value via RFM model and RS theory 2008Knowledge discovery on RFM model using Bernoulli sequence 2001Customer Relationship Management: An Analysis Framework and Implementation Strategies 1975Successful direct marketing methods 2004A purchase-based market segmentation methodology

Cited by

2016Prediction of the Shoppers Loyalty with Aggregated Data Streams

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.