Skip to main content

Semantically-Enabled Optimization of Digital Marketing Campaigns

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


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.


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.

This is a preview of subscription content, log in via an institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.

    Revenue in the Digital Advertising market amounts to US $63,469m in 2019, according to

  2. 2.

  3. 3.

  4. 4.

    Meteorological Archival and Retrieval System.

  5. 5.

    General Regularly-distributed Information in Binary form.

  6. 6.

  7. 7.

  8. 8.

  9. 9.

  10. 10.

  11. 11.

    See video at and the Semantic Data Enrichment for Data Scientists tutorial at

  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.

  14. 14.

  15. 15.

  16. 16.

  17. 17.

  18. 18.

  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.


  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  2. Chen, J., Jimenez-Ruiz, E., Horrocks, I., Sutton, C.: ColNet: embedding the semantics of web tables for column type prediction. In: AAAI (2019)

    Google Scholar 

  3. Erragcha, N., Romdhane, R.: New faces of marketing in the era of the web: from marketing 1.0 to marketing 3.0. J. Res. Mark. 2(2), 137–142 (2014)

    Google Scholar 

  4. Fortuna, B., et al.: QMiner: data analytics platform for processing streams of structured and unstructured data (2014)

    Google Scholar 

  5. Frick, T.: Return on Engagement: Content, Strategy and Design Techniques for Digital Marketing. Routledge, Abingdon (2013)

    Book  Google Scholar 

  6. Garcia-Crespo, A., Colomo-Palacios, R., Gomez-Berbis, J.M., Ruiz-Mezcua, B.: SEMO: a framework for customer social networks analysis based on semantics. J. Inf. Technol. 25(2), 178–188 (2010)

    Article  Google Scholar 

  7. Hoppe, A., Nicolle, C., Roxin, A.: Automatic ontology-based user profile learning from heterogeneous web resources in a big data context. Proc. VLDB Endow. 6(12), 1428–1433 (2013)

    Article  Google Scholar 

  8. Hoppe, A., Roxin, A., Nicolle, C.: Customizing semantic profiling for digital advertising. In: Meersman, R., et al. (eds.) OTM 2014. LNCS, vol. 8842, pp. 469–478. Springer, Heidelberg (2014).

    Chapter  Google Scholar 

  9. Isele, R., Bizer, C.: Active learning of expressive linkage rules using genetic programming. Web Semant.: Sci. Serv. Agents World Wide Web 23, 2–15 (2013)

    Article  Google Scholar 

  10. Pham, M., Alse, S., Knoblock, C.A., Szekely, P.: Semantic labeling: a domain-independent approach. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 446–462. Springer, Cham (2016).

    Chapter  Google Scholar 

  11. Roman, D., et al.: DataGraft: one-stop-shop for open data management. Semant. Web 9(4), 393–411 (2018)

    Article  Google Scholar 

  12. Spahiu, B., Porrini, R., Palmonari, M., Rula, A., Maurino, A.: ABSTAT: ontology-driven linked data summaries with pattern minimalization. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 381–395. Springer, Cham (2016).

    Chapter  Google Scholar 

  13. Sukhobok, D., et al.: Tabular data cleaning and linked data generation with grafterizer. In: Sack, H., Rizzo, G., Steinmetz, N., Mladenić, D., Auer, S., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9989, pp. 134–139. Springer, Cham (2016).

    Chapter  Google Scholar 

  14. Wertime, K., Fenwick, I.: DigiMarketing: The Essential Guide to New Media and Digital Marketing. Wiley, Hoboken (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Matteo Palmonari or Dumitru Roman .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30795-0

  • Online ISBN: 978-3-030-30796-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics