Improving comparison shopping agents’ competence through selective price disclosure

Highlights

• A new approach for increasing the attractiveness of comparison shopping agents.

• The approach, selective price-disclosure, affects the buyer’s decision-making.

• Two methods for selective price-disclosure for fully-rational agents are given.

• The effectiveness and efficiency of the methods are demonstrated using real data.

• A third method is experimentally shown to be highly effective with people.

Abstract

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed “selective price disclosure”, which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer’s beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well; however, the disclosed subset of prices should be extracted in a different (simplistic) manner.

Introduction

Comparison shopping is the practice of comparing the prices of items from different sources in order to find the best deal. Yet comparison-shopping is time consuming and requires resourcefulness. In today’s online world, comparison shopping can be substantially facilitated through the use of commercial comparison shopping agents (CSAs) such as PriceGrabber.com, bizrate.com and Shopper.com. These web-based intelligent software applications allow consumers to compare many online stores’ prices, saving their time and money (Pathak 2010). According to Consumer Futures’ report from 2014 (Downer et al. 2013), $56\%$ of the costumers in the UK declared that they have used a CSA in the last 2 years. The 17th annual release of ShoppingBots and Online Shopping Resources (shoppingbots.info) lists more than 350 different CSAs that are currently available online. This rich set of comparison-shopping offerings available over the Internet as well as the fact that each CSA covers only a small portion of the sellers offering a given product, allow prospective buyers to query more than a single CSA for comparison shopping. This way they are more likely to find a good price prior to making a purchase. This poses a great challenge to CSAs, as most of them do not charge consumers for accessing their web sites, and therefore the bulk of their profits is obtained, potentially alongside sponsored links or sponsored ads, via commercial relationships with the sellers they list (most commonly in the form of a fixed payment paid every time a consumer is referred to the seller’s website from the CSA) (Moraga-Gonzalez and Wildenbeest 2012). Therefore, if a CSA could influence buyers to avoid querying additional CSAs, it would certainly improve its expected revenue. In the CSA-buyer setting, the buyer’s decision of whether or not to resume exploration is based primarily on the best price obtained thus far, her expectations regarding the prices that are likely to be obtained through further CSA-querying, and the intrinsic cost of querying additional CSAs (e.g., cost of time). Influencing the best price presented to the buyer can be achieved by increasing the number of sellers whose prices are being retrieved in response to the buyer’s query. Yet, this requires consuming more resources and the expected marginal improvement in the best price decreases as a function of the set size.

In this paper we take a different approach to influence the buyers’ decision whether or not to query additional CSAs, in a way that discourages further querying. The idea is that by disclosing only a subset of all the prices collected by the CSA, one can influence the buyer’s expectations regarding the prices she is likely to encounter if she queries additional CSAs. The underlying assumption is that the buyer is a priori unfamiliar with the market price distribution of the specific product she wants to buy, and her expectations are updated each time she obtains an additional set of prices from a queried CSA (Bikhchandani and Sharma 1996). An intelligent price disclosure strategy can thus decrease the buyer’s confidence in obtaining a better price from the next CSA queried and, as a result, discourage her from any additional querying. We emphasize that this new approach does not conflict with, but rather complements, the idea of increasing the number of sellers whose prices are checked in order to increase the chance of finding a more appealing (lower) price. The paper focuses primarily on situations where the buyer queried a single CSA and needs to decide whether to query more. This is because, as argued later on, this is the common setting, and overall the ability to influence the buyer’s beliefs concerning the market price distribution decreases as a function of the number of prices gathered by the buyer, i.e., the number of CSAs already queried.

The contributions of the paper are threefold. To begin with, we are the first to introduce the idea of selective price disclosure in order to influence buyers to avoid querying additional CSAs. We formally analyze the incentive of buyers to query additional CSAs and CSAs’ benefit in selectively disclosing the prices with which they are acquainted whenever queried. Choosing the best subset of prices to disclose from the original set of prices is computationally intractable, therefore our second contribution is in presenting two price disclosure methods that CSAs can use. These methods are aimed to improve the probability that a buyer will terminate her price-search process and buy the product through the CSA applying the selective price disclosure. Both methods disclose the minimum price known to the CSA, thus the benefit from the partial price disclosure does not conflict with increasing the number of prices the CSA initially obtains to potentially find a more appealing (lower) price. The effectiveness of the methods when the buyer is fully rational is evaluated using real data collected from five comparison shopping agents for four products. The evaluation demonstrates the effectiveness of the resulting subsets of prices achieved with these methods and the tradeoff between their performance and the time they are allowed to execute. Finally, we evaluate the methods using human subjects, to possibly discover that the best solution for fully-rational buyers is less effective with people. This is partially explained by our experimental findings, whereby people’s tendency to terminate their search increases as a function of the number of prices they obtain from the CSA, even if the minimum price remains the same. For the latter population we suggest a simple price disclosing method that has been shown to be highly effective in deterring people from querying additional CSAs.

The rest of the paper is organized as follows: in Section 2 we review related work regarding CSAs, dynamic pricing and selective information disclosure. We formally present the model in Section 3. In Section 4, we analyze the CSA and buyer’s strategies, and the effect of selective price disclosing on the buyer’s decision to query additional CSAs. Later, in Sections 5 Methods, 6 Evaluation with people, 7 When the CSA is not the first to be queried, we discuss and evaluate the selective price disclosing methods for fully rational agents and provide experimental results exemplifying the applicability of the proposed methods with people. Finally, we conclude with a discussions and directions for future research in Section 8.