Identifying prospective customers

Paul B. Chou · Edna K. Grossman · Dimitrios Gunopulos · Pasumarti V. Kamesam

41 citations0/0 files storedACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

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 Data Mining Algorithms and Applications

Abstract

We describe data mining techniques designed to address the problem of selecting prospective customers from a large pool of candidates.These techniques cover a number of different scenarios, namely whether the marketing researchers have demographic information on the current customers, or the general market population, or people with propensity to become customers We also present a novel approach to the problem by exploiting the availability of a data sample from the general market population.Finally, we describe an on-line lead management and delivery system that uses the mining approach described in this paper for insurance agents to obtain qualified customer leads.

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 — 41 citations exist upstream.

Cited by

2022Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm 2010Application of data mining techniques for customer lifetime value parameters: a review 2016Buyer targeting optimization: A unified customer segmentation perspective 2021Identifying customer priority for new products in target marketing: Using RFM model and TextRank

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