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Improving measurement with Big Data: Variety-seeking and survival

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Big Data can be used to make sense of highly unstructured consumer data, and improve both the reliability and validity of measures that have historically required manual coding. Furthermore, using available secondary data allows for much faster coding. This research proposes a new and more robust way to measure the degree of variety-seeking exhibited by consumers. It employs the Million Song Dataset, a database of consumergenerated tags describing musical styles, to create measures of musical variety with minimal manual coding. Using a sample of 10,511 SiriusXM subscribers, the research compares this novel method of measuring variety-seeking behaviours with a more simple model, and finds that the novel method is a more accurate predictor of churn.
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Keywords: Big Data; construct validity; data fusion; survival; variety-seeking

Document Type: Research Article

Publication date: January 1, 2019

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  • Applied Marketing Analytics is the major new professional journal publishing in-depth, peer-reviewed articles on all aspects of marketing analytics. Guided by an expert Editorial Board each quarterly 100-page issue - published both in print and online - features detailed, practical articles written by and for marketing analytics professionals on innovative thinking, strategies, techniques, software and applied research showing how major brands are collecting, interpreting and acting on marketing analytics, both around the world and across varied digital and non-digital marketing channels. Learn how to measure the effectiveness of your marketing initiatives more accurately, how this compares to your competitors, identify gaps in your marketing analytics program and what metrics that support sound marketing decision making - and add to the bottom line.
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