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Article

Digital Marketing Attribution: Understanding the User Path

1
Department of Telematics Engineering, Universidad Carlos III de Madrid, 28911 Leganés, Spain
2
Department of Advanced Analytics Neo Media World, 28003 Madrid, Spain
3
UC3M-Santander Big Data Institute, 28903 Getafe, Spain
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(11), 1822; https://doi.org/10.3390/electronics9111822
Received: 6 October 2020 / Revised: 22 October 2020 / Accepted: 29 October 2020 / Published: 2 November 2020
(This article belongs to the Section Computer Science & Engineering)
Digital marketing is a profitable business generating annual revenue over USD 200B and an inter-annual growth over 20%. The definition of efficient marketing investment strategies across different types of channels and campaigns is a key task in digital marketing. Attribution models are an instrument used to assess the return of investment of different channels and campaigns so that they can assist in the decision-making process. A new generation of more powerful data-driven attribution models has irrupted in the market in the last years. Unfortunately, its adoption is slower than expected. One of the main reasons is that the industry lacks a proper understanding of these models and how to configure them. To solve this issue, in this paper, we present an empirical study to better understand the key properties of user-paths and their impact on attribution models. Our analysis is based on a large-scale dataset including more than 95M user-paths from real advertising campaigns of an international hoteling group. The main contribution of the paper is a set of recommendation to build accurate, interpretable and computationally efficient attribution models such as: (i) the use of linear regression, an interpretable machine learning algorithm, to build accurate attribution models; (ii) user-paths including around 12 events are enough to produce accurate models; (iii) the recency of events considered in the user-paths is important for the accuracy of the model. View Full-Text
Keywords: measurement; performance analysis; predictive models; digital marketing; user path; attribution model; data-driven attribution measurement; performance analysis; predictive models; digital marketing; user path; attribution model; data-driven attribution
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MDPI and ACS Style

Romero Leguina, J.; Cuevas Rumín, Á.; Cuevas Rumín, R. Digital Marketing Attribution: Understanding the User Path. Electronics 2020, 9, 1822. https://doi.org/10.3390/electronics9111822

AMA Style

Romero Leguina J, Cuevas Rumín Á, Cuevas Rumín R. Digital Marketing Attribution: Understanding the User Path. Electronics. 2020; 9(11):1822. https://doi.org/10.3390/electronics9111822

Chicago/Turabian Style

Romero Leguina, Jesús, Ángel Cuevas Rumín, and Rubén Cuevas Rumín. 2020. "Digital Marketing Attribution: Understanding the User Path" Electronics 9, no. 11: 1822. https://doi.org/10.3390/electronics9111822

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