Improving measurement with Big Data: Variety-seeking and survival
Abstract
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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Author's Biography
Mihaela Alina Nastasoiu is a data scientist at Booking.com. A former assistant professor at the DeGroote School of Business, McMaster University, she has a PhD from the Ivey Business School, Western University, Canada.
Neil Bendle is an associate professor at the Ivey Business School and co-author of ‘Marketing Metrics: The Manager’s Guide to Measuring Marketing Performance’ (3rd edn). He has a PhD from the Carlson School of Management, University of Minnesota and an MBA from the Darden School, University of Virginia. He was formerly the Finance Director at the British Labour Party.
Mark Vandenbosch is Acting Dean of the Ivey Business School. He is the Kraft Professor in Marketing and earned his PhD from the University of British Columbia. He has held visiting professorships at IMD in Switzerland and INSEAD in France, and has provided consultancy services in marketing research and marketing strategy to such leading companies as Tetra Pak, National Semiconductor, Hewlett Packard, Allied Signal, Bank of Montreal, Medtronic and Nestlé.
Citation
Nastasoiu, Mihaela Alina, Bendle, Neil and Vandenbosch, Mark (2019, February 6). Improving measurement with Big Data: Variety-seeking and survival. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 4, Issue 3. https://doi.org/10.69554/UUNH7282.
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