The Impact of Coupons on the Visit-to-Purchase Funnel

Gopalakrishnan, Arun; Park, Young-Hoon

doi:10.1287/mksc.2020.1232

Cited by 26 publications

(10 citation statements)

References 27 publications

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“…Another domain of the marketing literature investigates the heterogeneity of marketing effects across customer characteristics and the circumstances under which customers are targeted by coupon and other promotional campaigns. Among them, Gopalakrishnan and Park [ 42 ] analyse whether high- and low-consumption customers, as defined by their purchasing behavior during the 12 months prior to the experiment, differ in their responsiveness to coupon campaigns. Andrews et al [ 43 ] study whether the level of occupancy (or crowdedness) of a subway affects passengers’ response to mobile advertising campaigns and find a statistically significant positive association.…”

Section: Related Literaturementioning

confidence: 99%

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

Langen

Huber

2023

PLoS ONE

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We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retailer. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across different subgroups of customers, e.g., between clients with relatively high vs. low prior purchases. Finally, we use optimal policy learning to determine (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention’s effectiveness in terms of sales. We find that only two out of the five coupon categories examined, namely coupons applicable to the product categories of drugstore items and other food, have a statistically significant positive effect on retailer sales. The assessment of group average treatment effects reveals substantial differences in the impact of coupon provision across customer groups, particularly across customer groups as defined by prior purchases at the store, with drugstore coupons being particularly effective among customers with high prior purchases and other food coupons among customers with low prior purchases. Our study provides a use case for the application of causal machine learning in business analytics to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.

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Section: Related Literaturementioning

confidence: 99%

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

Langen

Huber

2023

PLoS ONE

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show abstract

“…Other contributions analyze the heterogeneity of marketing effects across customer characteristics and the circumstances under which customers are targeted by coupon and other promotion campaigns. Among them, Gopalakrishnan and Park (2021) investigate whether high-and lowconsumption customers, as defined by their purchasing behavior during the 12 months prior to the experiment, differ in their responsiveness to coupon campaigns. Andrews, Luo, Fang, and Ghose ( 2016) study whether the level of capacity utilization (or crowdedness) of a subway affects the response of its passengers to mobile advertising campaigns and find a statistically significant positive association.…”

Section: Causal Inference In Marketingmentioning

confidence: 99%

“…Other contributions as e.g. Gopalakrishnan andPark (2021), Jia, Yang, Lu, andPark (2018), Choi and Coulter (2012), Krishna and Zhang (1999) and Inman and McAlister (1994) analyze how further aspects of the configuration of coupon and discount campaigns affect consumer behavior.…”

Section: Causal Inference In Marketingmentioning

confidence: 99%

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

Langen¹,

Huber²

2022

Preprint

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We apply causal machine learning algorithms to assess the causal effect of a marketing intervention, namely a coupon campaign, on the sales of a retail company. Besides assessing the average impacts of different types of coupons, we also investigate the heterogeneity of causal effects across subgroups of customers, e.g. across clients with relatively high vs. low previous purchases. Finally, we use optimal policy learning to learn (in a data-driven way) which customer groups should be targeted by the coupon campaign in order to maximize the marketing intervention's effectiveness in terms of sales. Our study provides a use case for the application of causal machine learning in business analytics, in order to evaluate the causal impact of specific firm policies (like marketing campaigns) for decision support.

show abstract

“…Other contributions analyze the heterogeneity of marketing effects across customer characteristics and the circumstances under which customers are targeted by coupon and other promotional campaigns. Among them, Gopalakrishnan and Park (2021) investigate whether high-and low-consumption customers, as defined by their purchasing behavior during the 12 months prior to the experiment, differ in their responsiveness to coupon campaigns. Andrews, Luo, Fang, and Ghose (2016) study whether the level of occupancy (or crowdedness) of a subway affects passengers' response to mobile advertising campaigns and find a statistically significant positive association.…”

Section: Causal Inference In Marketingmentioning

confidence: 99%

“…Other contributions as e.g. Gopalakrishnan andPark (2021), Jia, Yang, Lu, andPark (2018), Choi and Coulter (2012), Krishna and Zhang (1999) and Inman and McAlister (1994) analyze how further aspects of coupon and discount campaign design affect consumer behavior.…”

Section: Causal Inference In Marketingmentioning

confidence: 99%

Essays on causal inference

Langen

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This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust w.r.t. misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting. We demonstrate that the effect estimators are asymptotically normal and n −1/2 -consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the U.S. National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect.

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The Impact of Coupons on the Visit-to-Purchase Funnel

Abstract: Using data from a field experiment, we find that mobile coupons lift revenue primarily by increasing visits to the website rather than through redemption.

Cited by 26 publications

References 27 publications

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

Essays on causal inference

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