Skip to main content
UNITED STATES DEPARTMENT OF LABOR

Managing laggards: The importance of a deep sales bench (Boichuk et al., 2019)

  • Findings

    See findings section of this profile.

    Evidence Rating

    Low Causal Evidence

Review Guidelines

Causal Evidence Guidelines

Absence of conflict of interest.

Citation

Boichuk, J. P., Bommaraju, R., Ahearne, M., Kraus, F., & Steenburgh, T. J. (2019). Managing laggards: The importance of a deep sales bench. Journal of Marketing Research, 56(4), 652–665.

Highlights

  • The study’s objective was to examine the impact of a disincentive program for low-performing salespeople known as a “bench program” on productivity.  

  • The study used a difference-in-differences study design to compare the outcomes of salespeople in districts that did and did not use the bench program. The data used in this study were performance data provided by the company implementing the bench program and a survey administered by the authors.  

  • The study found a positive statistically significant relationship between the bench program and the overall district productivity as measured by percentage of quota.  

  • The quality of causal evidence presented in this report is low because the authors did not ensure that the groups being compared were similar before the start of the intervention. This means we are not confident that the estimated effects are attributable to the bench program; other factors are likely to have contributed. 

Intervention Examined

Bench Program

Features of the Intervention

The bench program was introduced to some districts in a Fortune 500 company in an effort to determine if this strategy could be used companywide to improve the performance of low-performing salespeople. The bench program is a strategy an employer can use to disincentivize poor performance of salespeople with the threat of replacement for the lowest-performing salespeople over a given period.   

Features of the Study

The study compared the overall performance of salespeople from two groups of districts in a Fortune 500 company—one group of 58 districts that introduced the bench program and another group of 82 districts that did not. The 286 salespeople in the bench program districts comprised the treatment group, and the authors selected a matched sample of 188 salespeople from the other districts to serve as the comparison group. The variables used for matching included two years of preprogram performance, variability in preprogram performance, tenure, and district size. 

The authors used a difference-in-differences model to compare the groups’ outcomes before and after the intervention was introduced. The outcome of interest was salesperson performance, defined as percentage of quota. The authors used data provided by the company implementing the bench program and a survey administered by the authors before the intervention was introduced, after it was introduced, and after it was discontinued.  

Findings

The study found a positive statistically significant relationship between the bench program and the overall district productivity as measured by percentage of performance quota. The productivity of salespeople in the treatment group increased by more than 4 percentage points than that of salespeople in the comparison group.  

Considerations for Interpreting the Findings

Although the study used a design that matched the treatment and comparison group in several dimensions and accounted for differences in outcomes before the intervention, the authors did not account for other factors that could have affected the difference between the treatment and comparison groups. These preexisting differences between the groups—and not the intervention—could explain the observed differences in outcomes. 

Causal Evidence Rating

The quality of causal evidence presented in this report is low because the authors did not ensure that the groups being compared were similar before the intervention. This means we are not confident that the estimated effects are attributable to the bench program; other factors are likely to have contributed. 

Reviewed by CLEAR

May 2021