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Using Predictive Analytics to Measure Effectiveness of Social Media Engagement: A Digital Measurement Perspective.
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- Author(s): Kennedy, Heather1; Kunkel, Thilo2; Funk, Daniel C.3
- Source:
Sport Marketing Quarterly. 2021, Vol. 30 Issue 4, p265-277. 13p.- Subject Terms:
- Source:
- Additional Information
- Abstract: As social media becomes an increasingly dominant and important component of sport organizations' marketing and communication strategies, effective marketing measurement techniques are required. Using social media data of a Division I football team, this research demonstrates how predictive analytics can be used as a marketing measurement tool. A support vector machine model was compared to a standard linear regression with respect to accurately predicting Facebook posts' total interactions. The predictive model was used as (i) a planning tool to forecast future post engagement based on a variety of post characteristics and (ii) an evaluation tool of a marketing campaign by providing accurate benchmarks to compare against achieved engagement metrics. Results indicated the support vector machine model outperformed the standard linear regression and the marketing campaign was unsuccessful in achieving its goals. This research provides a foundation for future use of predictive analytics in social media and sport management scholarship. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Sport Marketing Quarterly is the property of Fitness Information Technology, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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