Skip to main content

Improving Ads-Profitability Using Traffic-Fingerprints

  • Conference paper
  • First Online:
Data Mining (AusDM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1741))

Included in the following conference series:

  • 346 Accesses


This paper introduces the concept of traffic-fingerprints, i.e., normalized 24-dimensional vectors representing a distribution of daily traffic on a web page. Using specially tuned k-means clustering we show that similarity of traffic-fingerprints is related to the similarity of profitability time patterns for ads shown on these pages. In other words, these fingerprints are correlated with the conversions rates, thus allowing us to argue about conversion rates on pages with negligible traffic. By blocking or unblocking whole clusters of pages we were able to increase the revenue of online campaigns by more than 50%.

This work was supported by the National Centre for Research and Development (NBBR) grant no. POIR.01.01.01-00-0945/19, the National Science Center (NCN) grant no. 2020/37/B/ST6/04179 and the ERC CoG grant TUgbOAT no 772346.

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

Institutional subscriptions

Similar content being viewed by others


  1. Agarwal, D., Ghosh, S., Wei, K., You, S.: Budget pacing for targeted online advertisements at linkedin. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1613–1619 (2014)

    Google Scholar 

  2. Dave, V., Guha, S., Zhang, Y.: Measuring and fingerprinting click-spam in ad networks. ACM SIGCOMM Comput. Commun. Rev. 42 (2012).

  3. Dixit, V.S., Gupta, S.: Personalized recommender agent for E-commerce products based on data mining techniques. In: Thampi, S.M., et al. (eds.) Intelligent Systems, Technologies and Applications. AISC, vol. 910, pp. 77–90. Springer, Singapore (2020).

    Chapter  Google Scholar 

  4. D’Silva, K., Noulas, A., Musolesi, M., Mascolo, C., Sklar, M.: Predicting the temporal activity patterns of new venues. EPJ Data Sci. 7(1), 1–17 (2018).

    Article  Google Scholar 

  5. Fabra, J., Álvarez, P., Ezpeleta, J.: Log-based session profiling and online behavioral prediction in e-commerce websites. IEEE Access 8, 171834–171850 (2020).

    Article  Google Scholar 

  6. Ihm, S., Pai, V.S.: Towards understanding modern web traffic. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 295–312. IMC 2011, Association for Computing Machinery, New York, NY, USA (2011).

  7. Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manag. J. 17(6), 441–458 (1996).<441::AID-SMJ819>3.0.CO;2-G

    Article  Google Scholar 

  8. Kleppe, M., Otte, M.: Analysing and understanding news consumption patterns by tracking online user behaviour with a multimodal research design. Digital Sch. Humanit. 32(2), ii158–ii170 (2017).

  9. Luh, C.J., Wu, A.: Is it worth to deliver display ads on content farm websites. J. Comput. 30, 279–289 (2019)

    Google Scholar 

  10. Mungamuru, B., Garcia-Molina, H.: Managing the quality of CPC traffic. In: Proceedings of the 10th ACM Conference on Electronic Commerce, pp. 215–224. EC 2009, Association for Computing Machinery, New York, NY, USA (2009).

  11. Nasir, V.A., Keserel, A.C., Surgit, O.E., Nalbant, M.: Segmenting consumers based on social media advertising perceptions: how does purchase intention differ across segments? Telematics and Inform. 64, 101687 (2021).,

  12. Pai, D., Sharang, A., Yadagiri, M.M., Agrawal, S.: Modelling visit similarity using click-stream data: a supervised approach. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8786, pp. 135–145. Springer, Cham (2014).

    Chapter  Google Scholar 

  13. Singh, H., Kaur, P.: An effective clustering-based web page recommendation framework for e-commerce websites. SN Comput. Sci. 2, 339 (2021).

    Article  Google Scholar 

  14. Su, Q., Chen, L.: A method for discovering clusters of e-commerce interest patterns using click-stream data. Electron. Commer. Res. Appl. 14(1), 1–13 (2015).,

  15. Thiyagarajan, R., Kuttiyannan, D.T., Ramalingam, R.: Recommendation of web pages using weighted k-means clustering. Int. J. Comput. Appl. 86 (2013).

  16. Thompson, K., Miller, G.J., Wilder, R.: Wide-area internet traffic patterns and characteristics. IEEE Netw. 11, 10–23 (1997)

    Article  Google Scholar 

  17. Thorndike, R.L.: Who belongs in the family? Psychometrika 18(4), 267–276 (1953).

  18. Vanessa, N., Japutra, A.: Contextual marketing based on customer buying pattern in grocery e-commerce: the case of (India). Asean Market. J. 56–67 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Piotr Sankowski .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dobrakowski, A.G., Pacuk, A., Sankowski, P., Mucha, M., Brach, P. (2022). Improving Ads-Profitability Using Traffic-Fingerprints. In: Park, L.A.F., et al. Data Mining. AusDM 2022. Communications in Computer and Information Science, vol 1741. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8745-8

  • Online ISBN: 978-981-19-8746-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics