Campaign management design based on segmentation by rank clusters

Paetz, Jürgen

doi:10.1057/jma.2015.16

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author proposed a generic algorithm select appropriate consumers for each campaign strategy. Paetz [19], proposed a process for product rankings for each consumer using rule-based classification, genetic algorithm and k-NN algorithm. The basic differences between this work and our proposed work are firstly, the authors have proposed a technique using different classification techniques for the identification of ranks of the product for each consumer, but in our case consumers give their preferences on items and from the rank data we identify the clusters.…”

Section: Introductionmentioning

confidence: 99%

Clustering on Ranked Data for Campaign Selection

2020

View full text Add to dashboard Cite

Recently the ranked data are commonly seen in the era of Internet and e-commerce where the consumers give their opinion in the form of ranks of a set of items online. The consumers are asked to put the ranks on items according to their order of preference. The applications of clustering ranked data are target marketing, campaign selection, top k-items, etc. The objective of this paper is to implement campaign selection process using clustering feedback data which are in rank ordered. To implement the proposed method we divide our experiments into two parts first, group the ranked data (consumer feedback), by applying different distance calculations, e.g., Kendall's tau, Spearman's rho square, Spearman's footrule, Cayley's distance. Second, use the knowledge derived from the groups in campaign selection process. We have compared our proposed clustering algorithm with existing algorithms on different real datasets, and results showed the effectiveness of our proposed algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%