Clustering on Ranked Data for Campaign Selection

Umang Gupta,Vandana Bhattacherjee,Partha Sarathi Bishnu

doi:10.1109/access.2020.3019394

Umang Gupta, Vandana Bhattacherjee + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3019394

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Abstract

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.

Highlights

Target marketing is a popular tool by which companies are competing more effectively [1]
Market targeting and market positioning are the basic components of target marketing
The outline of this paper is as follows: in Section 2 we present related work, in Section 3 we describe clustering algorithms for consumer ranked data and campaign selection process

Summary

Introduction

Target marketing is a popular tool by which companies are competing more effectively [1]. Companies are focusing to identify the group of those consumers they have the greatest chance of satisfying. Dividing a market into well defined groups (where each group of consumers exhibits a similar set of tastes and desire) is called market segmentation. The task of the managers is to identify the right number of groups (segments) and the task of the datamining tool is to create the quality groups. Marketers split the consumers into groups on the basis of their products buying behavior, opinion and feedback towards the products, and knowledge and attitude toward the use of the products [1], [2]. E.g., clustering algorithm, a manager can group those consumers who have similar taste and opinion. A campaign is a grouping of advertisement and each campaign can have multiple

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