Recommendations for Marketing Campaigns in Telecommunication Business based on the footprint analysis

TL;DR

This paper introduces a data-driven, combinatorial optimization approach for telecom marketing, analyzing user mobility data to optimize customer segmentation and campaign strategies, with automated natural language recommendations.

Contribution

It presents a novel analytic strategy combining optimization, mobility analysis, and fuzzy logic to improve marketing campaign targeting in telecoms.

Findings

Identified optimal geo-demographic segment proportions

Developed a tool to assess campaign potential

Automated recommendations in natural language

Abstract

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues are proportional to how big and good the customer base is. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility of the users in one region of Sweden during a week. Applying the proposed method to the case study, we have identified the optimal proportion of geo-demographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and explored the problem of an optimal number and types of the geo-demographic segments to target through marketing campaigns. With the help of fuzzy logic, the conclusions of data analysis are automatically translated into comprehensible recommendations in a natural language.