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Modeling independent sales representative performance: application of predictive analytics in direct selling for improved outcomes

Author

Listed:
  • Caroline Glackin

    (Fayetteville State University)

  • Murat Adivar

    (Fayetteville State University)

Abstract

This research supports improved salesforce outcomes for companies with independent sales representatives via the application of emerging technologies. It is framed in expectancy theory (in: Vroom, Work and motivation, Wiley, New York, 1964), Herzberg’s et al. (The motivation to work, Wiley, New York, 1959) two factor theory, and the job demands and resources model (JD-R) (Bakker and Demerouti in J Manag Psychol 22:309–328, 2007). The study analyzes the 2018 National Salesforce Survey (USA) commissioned by the Direct Selling Association (Washington, DC, USA). It identifies key factors in salesforce motivation and performance theories studied within these theoretical frames and channel-specific inputs for direct selling independent representatives. While researchers apply multiple methods to salesforce motivation and performance, the power of machine learning has not been applied to independent representatives and their unique circumstances. This research employs supervised learning algorithms for predictive analytics to create models for recruits and existing independent representatives. It finds that the most crucial factors align with expectancy theory for both. Allocated time for direct selling, ability to find new customers, gender, and adopting direct selling as a career play key roles in predicting sales success for individuals entering direct selling. The highest-performing representatives are characterized by time invested, experience of direct selling, recruitment, tenure, and use of technology. By predicting representative success factors, organizations can tailor recruitment, training, and incentives to maximize performance. Because independent sales representatives are not employees, understanding these factors is critical to firms engaging them. Translating established theory into testing with predictive analytics to identify meaningful success factors for rarely studied independent sales representatives has the potential to change the landscape for recruitment, retention, and success.

Suggested Citation

  • Caroline Glackin & Murat Adivar, 2023. "Modeling independent sales representative performance: application of predictive analytics in direct selling for improved outcomes," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 613-628, December.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:4:d:10.1057_s41270-023-00236-4
    DOI: 10.1057/s41270-023-00236-4
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    References listed on IDEAS

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