30 / 2024-04-15 17:20:53

Accelerate or Attenuate? The Impact of First-Meet Coupon in Online Retail

online retail; initial trust; customer acquisition; incentive mechanisms; price promotions; difference-in-differences; causal forest; machine learning; data-driven policy

First-meet coupons, a novel coupon format, distinguish themselves from traditional coupons by combining two influential elements: economic savings (e.g., ¥5 off) and a first-meet threshold (e.g., targeting new customers for specific brands). Can this coupon format aid online platforms in acquiring new consumers? In this study, we investigate the impact of distributing first-meet coupons on such market outcomes using a difference-in-differences design coupled with the causal forest method. This approach leverages a quasi-experiment arising from the retailer's decision to selectively issue first-meet price promotions for certain products. We also explore how first-meet coupon promotions influence product sales and profitability, and identify contexts where this effect is most pronounced. Drawing on a unique dataset comprising 92 days of transaction data on 14,672 products spanning 700 brands and 28,357,502 customers, we find that first-meet coupons significantly increase the acquisition of new customers for promoted products on the day of promotion. Our results indicate that first-meet promotions inadvertently lead to increases in product sales, net sales, and net profit. Furthermore, we demonstrate that this promotional effect is amplified when products offer lower discount depths, belong to well-known brands, and garner more reviews from patrons in online word-of-mouth systems. Additionally, we leverage these empirical results by using the causal forest to prescribe how platforms should optimally set coupon face value to maximize the treatment effects.