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Improving Ads-Profitability Using Traffic-Fingerprints

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Data Mining (AusDM 2022)

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

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Abstract

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.

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Correspondence to Piotr Sankowski .

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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. https://doi.org/10.1007/978-981-19-8746-5_15

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  • DOI: https://doi.org/10.1007/978-981-19-8746-5_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8745-8

  • Online ISBN: 978-981-19-8746-5

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