How to induce workers to sell high-margin products? A field experiment
Last registered on February 07, 2020

Pre-Trial

Trial Information
General Information
Title
How to induce workers to sell high-margin products? A field experiment
RCT ID
AEARCTR-0005347
Initial registration date
February 05, 2020
Last updated
February 07, 2020 1:56 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
2020-02-01
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 assigning higher weights to high-margin products in the determination of workers' compensation. We compare two compensation systems. The first system is solely based on workers' individual performance (i.e., their revenue). In the second system, revenues of high-margin products are assigned higher weights than revenues of low-margin products, and the commission is based on weighted revenue. We study how the different compensation systems affect revenues from the different types of products, 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. "How to induce workers to sell high-margin products? A field experiment." AEA RCT Registry. February 07. https://doi.org/10.1257/rct.5347-1.0.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2020-02-01
Intervention End Date
2020-05-31
Primary Outcomes
Primary Outcomes (end points)
Revenue (from different types of product)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Conversion rate by product type, discounts, turnover
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We collaborate with a retailer operating 69 stores in Australia and New Zealand. The retailer employs salesworkers whose main task is to sell the firm's products in the stores. In an RCT, two (new) compensation systems are implemented in (about) 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 solely based on workers' individual performance (i.e., their revenue). In the second system, revenues of different types of products are assigned different weights, and commission payments are based on weighted revenue. Both compensation schemes are piecewise linear; they have kinks at two pre-specified targets and become steeper once a target is reached. The commission systems are designed such that the average commission is about the same across systems (as calibrated with the 2019 revenue distribution).

Treatment A: Salesworkers' monthly compensation depends on their (unweighted) revenue in that month.

Treatment B: Salesworkers' monthly compensation depends on their weighted revenue in that month. Different weights are assigned to revenues of different types of products, with high-margin products receiving higher weights than low-margin products.

We study how the different compensation systems affect revenues from different types of product, turnover, conversion rates (i.e., the rate at which customers being served by salesworkers buy the firm's products), and granted discounts. We also study heterogeneous treatment effects with respect to workers’ pre-treatment performance (high- vs. low-performers) and tenure. We further conduct employee surveys to measure employee attitudes.
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
69 stores (51 stores in Australia and 18 stores in New Zealand)
Sample size: planned number of observations
Actual headcount on the 1st of February (we do not yet have the precise data)
Sample size (or number of clusters) by treatment arms
About half of the stores (35 and 34) 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