Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2017

Abstract

In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, presenting a holistic picture of the customer base of an organization. We validate our methodology via the implementation of a working system that rapidly and in near real-time processes tens of millions of online customer interactions with content posted on major social media platforms in order to identify both the distinct behavioral segments and corresponding impactful demographic segments. We illustrate the functionality of the methodology with real data from a major online content provider with millions of online interactions from more than thirty countries. We further show one possible use for such information via the automatic generation of personas for an organization, which can be used for the formulation of marketing strategy, implementation of advertising plans, or development of products. The research results offer insights into competitive marketing and product preferences for the consumers of online digital content. We conclude with a discussion of areas for future work.

Keywords

customer segmentation, digital marketing, online news, social analytics, social media, web analytics

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)

Identifier

10.1109/AICCSA.2017.64

City or Country

Hammamet, Tunisia

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