The Impact of Increasing Search Frictions on Online Shopping Behavior: Evidence from a Field Experiment

Donald  Ngwe; Kris J. Ferreira; Thales  Teixeira

Article | Journal of Marketing Research (JMR) | December 2019

The Impact of Increasing Search Frictions on Online Shopping Behavior: Evidence from a Field Experiment

by Donald Ngwe, Kris J. Ferreira and Thales Teixeira

Abstract

Many online stores are designed such that shoppers can easily access any available discounted products. We propose that deliberately increasing search frictions by placing small obstacles to locating discounted items can improve online retailers’ margins and even increase conversion. We demonstrate using a simple theoretical framework that inducing consumers to inspect higher-priced items first may simultaneously increase the average price of items sold and the overall expected purchase probability by inducing consumers to search more products. We test and confirm these predictions in a series of field experiments conducted with a dominant online fashion and apparel retailer. Furthermore, using information in historical transaction data about each consumer, we demonstrate that price-sensitive shoppers are more likely to incur search costs to locate discounted items. Our results show that increasing search frictions can be used as a self-selecting price discrimination tool to match high discounts with price-sensitive consumers and full-priced offerings with price-insensitive consumers.

Keywords: e-commerce; online retailing; friction; effor; search costs; price discrimination; Marketing; Consumer Behavior; Strategy; Price; Retail Industry; Fashion Industry;

Format: Print

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Citation:

Ngwe, Donald, Kris J. Ferreira, and Thales Teixeira. "The Impact of Increasing Search Frictions on Online Shopping Behavior: Evidence from a Field Experiment." Journal of Marketing Research (JMR) 56, no. 6 (December 2019): 944–959.

As more commerce and media consumption are being conducted online, a wealth of new opportunities are emerging for personalized advertising. We propose a general methodology, Model Trees for Personalization (MTP), for tackling a broad class of personalized decision-making problems including personalized advertising. The MTPs learn an interpretable market segmentation driven by differences in user behavior. Using this methodology, we design two new algorithms for fundamental problems in personalized advertising – Choice Model Trees (CMTs) for the user ad-response prediction problem, and Isotonic Regression Model Trees (IRMTs) for the bid landscape forecasting problem. We provide a customizable, computationally-efficient, and open-source code base for training MTPs in Python. We train our IRMT algorithm on historical bidding data from three different ad exchanges and show that the IRMT achieves 5-29% improvement in bid landscape forecasting accuracy over the model that a leading demand-side platform (DSP) provider currently uses in production.

The CEO of OLX Brazil, an online classifieds platform business, is debating among multiple paths to grow sustainably (i.e., profitably) without the need for investor money. The options under consideration are (1) penetration growth by focusing on the core, (2) new markets growth by entering adjacencies, or (3) new products growth by developing new services. Each of these options carries a series of benefits and risks. How should OLX’s CEO compare the options and choose the best path to grow his platform business?

Working Paper | HBS Working Paper Series | 2019

The Effect of Payment Choices on Online Retail: Evidence from the 2016 Indian Demonetization

Chaithanya Bandi, Antonio Moreno, Donald Ngwe and Zhiji Xu

The Indian banknote demonetization in 2016 was one of the most significant international events of that year. Overnight, 86% of Indian currency in circulation was declared invalid unless exchanged for new bills. The sudden and unexpected demonetization constituted a large shock to the entire Indian economy. One effect of the ensuing cash shortage was a large and sustained increase in the adoption and usage of digital payments. We use detailed sales data consisting of more than two and a half million transactions from a leading Indian online retailer to empirically investigate the effects of payment digitization on the online retail industry. We take advantage of the demonetization as a source of exogenous variation that induced a subset of consumers to switch to digital payments from more commonly used cash-on-delivery payments. We show that consumers who switch to digital payments maintain their purchase frequency but spend more and are less likely to return their purchases. Our findings show that firms in emerging markets may enjoy gains from consumer demand in addition to operational gains resulting from payment digitization.

Keywords: cash on delivery; online retail; product returns; payment methods; digitization; Emerging Markets; Currency; Online Technology; Demand and Consumers; Retail Industry; India;

Citation:

Bandi, Chaithanya, Antonio Moreno, Donald Ngwe, and Zhiji Xu. "The Effect of Payment Choices on Online Retail: Evidence from the 2016 Indian Demonetization." Harvard Business School Working Paper, No. 19-123, June 2019. View Details

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The Impact of Increasing Search Frictions on Online Shopping Behavior: Evidence from a Field Experiment

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