A novel data-driven approach to detect and predict customer transitions in the marketing funnel

Authors

Chad Crowe, University of Nebraska at OmahaFollow
Christian Haas, University of Nebraska at OmahaFollow
Margeret Hall, University of Nebraska at OmahaFollow

Document Type

Article

Publication Date

11-12-2025

Publication Title

Social Network Analysis and Mining

Volume

15

Issue

105

DOI

https://doi.org/10.1007/s13278-025-01533-9

Abstract

This study braids together insights from two distinct areas of research: customer engagement within brands and social data analytics. Leveraging a proprietary B2B dataset from LinkedIn comprising over 200 million advertiser interactions over a subset of de-identified members, we present a data-driven exploration of the dynamics of the so-called marketing funnel. By bridging the gap between real-world digital metrics and theoretical marketing funnel models, we offer empirical insights into the interplay between engagement types and customer transitions towards purchase. Our analysis delineates the structural underpinnings of the derived marketing funnel to shed light on the efficacy of various engagement strategies in propelling users towards deeper funnel stages, aiding advertisers in predicting and targeting engagements effectively. We make three contributions: first, we propose a novel data-driven methodology to create a representation of the marketing funnel. Second, we apply the data-driven marketing funnel model on a proprietary dataset from LinkedIn advertisements with a subsequent descriptive and predictive analysis of the funnel stages and transitions to showcase the practicability and usefulness of the approach. Third, we analyze the relationship between user engagement and stage transitions within the created marketing funnel, identifying what user engagements are predictive of future stage transitions.

Comments

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This article was published open access under the Criss Library (Lyrasis member) and Springer open access publishing agreement.

Recommended Citation

Crowe, Chad; Haas, Christian; and Hall, Margeret, "A novel data-driven approach to detect and predict customer transitions in the marketing funnel" (2025). Information Systems and Quantitative Analysis Faculty Publications. 170.
https://digitalcommons.unomaha.edu/isqafacpub/170

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.