DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Data mining techniques are widely used in various areas of marketing management for extracting useful information. Particularly in a business-to-customer (B2C) setting, it plays an important role in customer segmentation. A retailer not only tries to improve its relationship with its customers, but also enhances its business in a manufacturer-retailer-consumer chain with respect to this information. Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis- Bouldin (DB) index and K-Means algorithm. Customer lifetime value (CLV) along four dimensions, viz., Length (L), Recency (R), Frequency (F) and Monetary value (M) are considered for these clusters. Then, we apply Saaty�s analytical hierarchical process (AHP) to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their individual rankings. This information is quite important for a retailer to design promotional strategies for improving relationship between the retailer and its customers. To demonstrate the effectiveness of this methodology, we have implemented the model, taking a real life data-base of customers of an organization in the context of an Indian retail industry.
