Using Social Network Activity Data to Identify and Target Job Seekers

Matthias Efing, Harald Hau, Patrick Kampkktter, Jean-Charles Rochet (), Peter Ebbes and Oded Netzer
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Matthias Efing: HEC Paris - Ecole des Hautes Etudes Commerciales
Jean-Charles Rochet: GREMAQ - Groupe de recherche en économie mathématique et quantitative - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique

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Abstract: An important challenge for many firms is to identify the life transitions of its customers, such as job searching, being pregnant, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firm opportunities to be more relevant to its customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, our modeling approach combines cross-sectional survey responses to a job seeking status question with longitudinal user activity data. Thus, in some time periods, and for some users, we observe the "true" job seeking status. We fuse the observed state information into the HMM likelihood, resulting in a partially HMM. We demonstrate that the proposed model can not only predict which users are likely to be job seeking at any point in time, but also what activities on the platform are associated with job search, and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 42% increase in profits of a targeting campaign relative to the approach that was used at the time of the data collection.

Keywords: Hidden Markov Models; Data Fusion; Targeting; Customer Analytics (search for similar items in EconPapers)
Date: 2018-06-29
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-01933858

DOI: 10.2139/ssrn.3200214

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