Inferring salesperson capability using stochastic frontier analysis

Abstract

This paper proposes an easy-to-implement econometric method for inferring salesperson capability from archival panel data, namely stochastic frontier (SF) analysis. We demonstrate this method with a sample of salespersons provided by a life insurance company. Using the proposed SF model, we are able to estimate each salesperson’s capability. Furthermore, we examine the relationship between the estimated salesperson capability and three future outcomes (i.e. future sales performance, future customer attrition, and future salesperson turnover) under different time horizons. We find that, in general, the estimated salesperson capability has a stronger explanatory power for the near than for the more distant future. Since an individual salesperson’s capability cannot be directly observed by researchers (and thus is typically omitted), traditional analyses of sales performance suffer from an omitted-variable problem that can lead to biased estimates of focal variables. The SF model can significantly mitigate this omitted-variable problem. Statistical tests indicate that our sales performance model with estimated salesperson capability results in a statistically significant improvement in model fit. Of note, our model differs methodologically from SF models previously used in the marketing literature in that it is based on a three-component model that disentangles unobserved individual heterogeneity, efficiency, and random shocks.

Keywords:

• salesperson capability
• stochastic frontier panel model
• omitted variables
• sales performance
• customer attrition
• salesperson turnover

Acknowledgments

The authors would like to thank Nick Lee and the four anonymous reviewers for their insightful comments and constructive suggestions. They are also grateful to the company, which wishes to remain anonymous, for providing the data set used in this study.

Notes

1 Please see Kumbhakar and Lovell (2003) and Kumbhakar, Wang, and Horncastle (2015) for a more exhaustive review of previous applications of the SF model.

2 For details, see http://www.limdep.com/features/capabilities/frontier.php (accessed January 2, 2016).

3 This definition was from http://www.merriam-webster.com/dictionary/efficiency (accessed February 24, 2016).

4 As mentioned in the previous section, SF analysis was first applied in firm production studies and a common practice is to assume a multiplicative Cobb-Douglas production function. Therefore, log transformation is a natural next step in order to transform the function to a linear one.

5 This link between turnover and capability is predicated on the assumption that salespersons with higher capabilities are more likely to stay with their current employer. Dickter, Roznowski, and Harrison (1996) provide a similar argument that employees with higher capabilities are more likely to stay with the company because they are more likely to achieve higher proficiency and become adapted to the demands of the job.

6 In particular, the estimated coefficients of Salesperson Capability under different time horizons are 12.05 (p < .01), 6.51 (p < .01), 4.12 (p < .05), and 3.09 (p > .10), respectively, for the “future sales performance” model; −3.06 (p < .10), −0.85 (p > .10), −0.74 (p > .10), and −0.25 (p > .10), respectively, for the “future customer attrition” model; and −7.29 (p < .05), −1.30 (p > .10), and −0.23 (p > .10), respectively, for the “future salesperson turnover” model.

7 In particular, the estimated coefficients of Salesperson Capability under different time horizons are 7.81 (p < .01), 5.13 (p < .01), 4.10 (p < .01), and 2.50 (p < .05), respectively, for the “future sales performance” model; −1.66 (p < .10), −0.80 (p > .10), −0.61 (p > .10), and −0.28 (p > .10), respectively, for the “future customer attrition” model; and −5.68 (p < .05), −1.06 (p > .10), and −0.03 (p > .10), respectively, for the “future salesperson turnover” model.

Additional information

Funding

The first author acknowledges the financial support from the Earl V. Snyder Innovation Management Center, Whitman School of Management, Syracuse University.