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Semantically-Enabled Optimization of Digital Marketing Campaigns

Part of the Lecture Notes in Computer Science book series (LNISA,volume 11779)

Abstract

Digital marketing is a domain where data analytics are a key factor to gaining competitive advantage and return of investment for companies running and monetizing digital marketing campaigns on, e.g., search engines and social media. In this paper, we propose an end-to-end approach to enrich marketing campaigns performance data with third-party event data (e.g., weather events data) and to analyze the enriched data in order to predict the effect of such events on campaigns’ performance, with the final goal of enabling advanced optimization of the impact of digital marketing campaigns. The use of semantic technologies is central to the proposed approach: event data are made available in a format more amenable to enrichment and analytics, and the actual data enrichment technique is based on semantic data reconciliation. The enriched data are represented as Linked Data and managed in a NoSQL database to enable processing of large amounts of data. We report on the development of a pilot to build a weather-aware digital marketing campaign scheduler for JOT Internet Media—a world leading company in the digital marketing domain that has amassed a huge amount of data on campaigns performance over the years—which predicts the best date and region to launch a marketing campaign within a seven-day timespan. Additionally, we discuss benefits and limitations of applying semantic technologies to deliver better optimization strategies and competitive advantage.

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The work in this paper is partly funded by the EC H2020 projects EW-Shopp (732590) and euBusinessGraph (732003). Authors are listed in alphabetical order.

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Notes

  1. 1.

    Revenue in the Digital Advertising market amounts to US $63,469m in 2019, according to https://www.statista.com/outlook/216/100/digital-advertising/worldwide.

  2. 2.

    https://www.jot-im.com.

  3. 3.

    https://adwords.google.com.

  4. 4.

    Meteorological Archival and Retrieval System. https://software.ecmwf.int/wiki/display/UDOC/MARS+user+documentation.

  5. 5.

    General Regularly-distributed Information in Binary form. http://www.wmo.int/pages/prog/www/DPS/FM92-GRIB2-11-2003.pdf.

  6. 6.

    http://www.eventregistry.org.

  7. 7.

    http://inside.disco.unimib.it/index.php/asia.

  8. 8.

    http://qminer.ijs.si.

  9. 9.

    https://rancher.com.

  10. 10.

    https://www.gluster.org.

  11. 11.

    See video at https://youtu.be/4amLd4biYcs and the Semantic Data Enrichment for Data Scientists tutorial at https://ew-shopp.github.io/eswc2019-tutorial.

  12. 12.

    The line comparison in Fig. 5 shows a comparison of the actual and predicted level of impressions for an anecdotal example. Its purpose is more illustrative as it does not reflect global performance of the approach, though it does suggest what level of possible deviation a marketing professional has to take into account when using the model.

  13. 13.

    http://usc-isi-i2.github.io/karma.

  14. 14.

    https://www.adequate.at/odalic.

  15. 15.

    http://openrefine.org/my%20category/2018/07/16/2018-survey-results.html.

  16. 16.

    http://silkframework.org.

  17. 17.

    http://aksw.org/Projects/LIMES.html.

  18. 18.

    https://openweathermap.org.

  19. 19.

    OWM explicitly recommends to call OWM API by city ID to get unambiguous result for cities. In our pilot we need weather for regions (not available in OWM). In fact, obtaining an ID and hence coordinates from an (ambiguous) toponym is the enrichment problem addressed in our pipeline.

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Correspondence to Matteo Palmonari or Dumitru Roman .

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Cutrona, V. et al. (2019). Semantically-Enabled Optimization of Digital Marketing Campaigns. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11779. Springer, Cham. https://doi.org/10.1007/978-3-030-30796-7_22

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  • DOI: https://doi.org/10.1007/978-3-030-30796-7_22

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