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Recommendations for marketing campaigns in telecommunication business based on the footprint analysis: Who is a good client?
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-9947-1088
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2017 (English)In: 2017 8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 513-518Conference paper, Published paper (Refereed)
Abstract [en]

A major investment made by a telecom operator goes into the infrastructure and its maintenance, while business revenues depend on how efficiently it is exploited. We present a data-driven analytic strategy based on combinatorial optimization and analysis of historical data. The data cover historical mobility in one region of Sweden during a week. Applying the proposed method in a case study, we have identified the optimal combination of geodemographic segments in the customer base, developed a functionality to assess the potential of a planned marketing campaign, and investigated how many and which segments to target for customer base growth. A comprehensible summary of the conclusions is created via execution of the queries with a fuzzy logic component. © 2017 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 513-518
Keywords [en]
business intelligence, combinatorial optimization, fuzzy logic, geodemographic segments, mobility data, MOSAIC, Commerce, Competitive intelligence, Computer circuits, Investments, Footprint analysis, Historical data, Marketing campaign, Mobility datum, Optimal combination, Telecom operators, Marketing
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16539DOI: 10.1109/IISA.2017.8316396ISI: 000454859600092Scopus ID: 2-s2.0-85047937690ISBN: 9781538637319 (print)OAI: oai:DiVA.org:bth-16539DiVA, id: diva2:1220128
Conference
8th International Conference on Information, Intelligence, Systems and Applications, IISA 2017, Larnaca
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2019-06-27Bibliographically approved

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Sidorova, YuliaLundberg, LarsGrahn, Håkan

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Citation style
  • apa
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Output format
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