Monthly versus yearly targets - a field experiment
Last registered on February 07, 2020

Pre-Trial

Trial Information
General Information
Title
Monthly versus yearly targets - a field experiment
RCT ID
AEARCTR-0005346
Initial registration date
February 05, 2020
Last updated
February 07, 2020 1:57 PM EST
Location(s)

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Primary Investigator
Affiliation
University of Cologne
Other Primary Investigator(s)
PI Affiliation
University of Cologne
PI Affiliation
University of Cologne
PI Affiliation
University of Cologne
Additional Trial Information
Status
In development
Start date
2020-02-03
End date
2020-12-31
Secondary IDs
Abstract
We collaborate with a retail company and investigate changes in the salesworkers' compensation system. In an RCT, we study the effect of yearly target setting on performance. To that end, we introduce two new compensation systems. The first system is reset every month, whereas the second is reset only at the end of the year, leading to increased levels of commissions over time. We study how the different compensation systems affect revenues, turnover, conversion rates (i.e., the rate at which customers being served by salesworkers buy the firm's products), and granted discounts.
External Link(s)
Registration Citation
Citation
Gürtler, Oliver et al. 2020. "Monthly versus yearly targets - a field experiment." AEA RCT Registry. February 07. https://doi.org/10.1257/rct.5346-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-02-03
Intervention End Date
2020-05-31
Primary Outcomes
Primary Outcomes (end points)
Revenue, turnover
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Conversion rate, discounts
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We collaborate with a retailer operating 52 stores in the UK. The retailer employs salesworkers whose main task is to sell the firm's products in the stores. In an RCT, we implement two new compensation systems in half of the firm's stores, respectively, and the company has agreed to keep the treatments in the field for at least four months (i.e., until the end of May). The first system is reset every month, whereas the second is reset only at the end of the year, leading to increased levels of commissions over time:

Treatment A: Salesworkers' monthly compensation depends only on their own revenue in that month. The corresponding compensation scheme is piecewise linear; it has kinks at two pre-specified monthly targets and it becomes steeper once a target is reached.

Treatment B: Salesworkers' monthly compensation depends on their accumulated revenue up to (and including) that month. The corresponding compensation scheme is piecewise linear; it has kinks at two pre-specified yearly targets and it becomes steeper once a target is reached.

We study how the different compensation systems affect revenues, turnover, conversion rates (i.e., the rate at which customers being served by salesworkers buy the firm's products), and granted discounts. The discontinuity in the commission rate between the end of one month and the start of the next in the monthly scheme (which is absent in the yearly scheme) will also allow us to study gaming effects. We further study heterogeneous treatment effects with respect to workers’ pre-treatment performance (high- vs. low-performers) and tenure. Finally, we conduct employee surveys to measure employee attitudes. In particular, we explore heterogeneous treatment effects with respect to time preferences (as elicited by a survey).
Experimental Design Details
Not available
Randomization Method
Pairwise matching by revenues in 2019
Randomization Unit
The randomization was implemented on the store level.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
52 stores
Sample size: planned number of observations
Actual headcount on the 3rd of February (we do not yet have the precise data)
Sample size (or number of clusters) by treatment arms
Half of the stores (26) in each of the two treatments, respectively
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number