Arvind K. Tripathi

Design of a shopbot and recommender system for bundle purchases

The increasing proliferation of online shopping and purchasing has naturally led to a growth in the popularity of comparison-shopping search engines, popularly known as shopbots. We extend the one-product-at-a-time search approach used in current shopbot implementations to consider purchasing plans for a bundle of items. Our approach leverages bundle-based pricing and promotional deals frequently offered by online merchants to extract substantial savings. Interestingly, our approach can also identify freebies that consumers can obtain at no extra cost. We also develop a model to extend the capability of the current recommendation algorithms that are mainly based on collaborative filtering and item-to-item similarity techniques, to incorporate product price and savings as an additional important factor in making recommendations to shoppers. We develop a practical algorithm that can be employed when the number of items is large or when the real-time nature of shopbot applications dictates quick response rates to consumer queries. A detailed experimental analysis with real-world data from major retailers suggests that the proposed models can provide significant savings for bundle purchasing consumers, and frequently identify freebies for consumers. Together the results underscore the potential benefits that can accrue by incorporating our models into current shopbot systems.

Advertising via wireless networks

The subscriber growth of wireless networks has created an increasing demand for wireless (mobile) advertising. However, at this point there are no standard industry practices to deal with some of the key mobile advertising issues. This research analyses the business model of a mobile advertising firm and highlights some of the key issues for modelling ad deliveries via wireless networks. We also perform an initial experimental study to gauge the significance on advertising effectiveness of some of the vital factors such as distance between retail store and ad delivery location.

Understanding Willingness-to-Pay Formation of Repeat Bidders in Sequential Online Auctions

A growing number of vendors are using a sequence of online auctions to sell large inventories of identical items. Although bidding strategies and bidder behavior in single auctions have been extensively studied, limited research exists on bidding in sequential auctions. We seek to explain how bidders in such an environment learn from the information, and form and update their willingness to pay (WTP). Using a large data set from an online auction retailer, we analyze the evolution of the bidders WTP as well as the effect of auction design on bidders WTP in sequential auctions. We see our study in the context of a longitudinal field experiment, in which we were able to track actions of repeat bidders over an extended period of time. Our results show that bidders WTP in sequential auctions can be explained from their demand characteristics, their participation experience in previous auctions, outcomes in previous auctions, and auction design parameters. We also observe, characterize, and measure what we call a modified demand reduction effect exhibited across different auctions, over time, by multiunit demand bidders. Our findings are important to enable better auction mechanism design, and more sophisticated bidding tools that explore the rich information environment of sequential auctions.

Mobile Advertising in Capacitated Wireless Networks

The growing number of mobile subscribers has attracted firms to invent newer strategies to reach prospective customers in innovative but nonintrusive ways. While customer mobility creates an opportunity to reach them at desired times and locations, in practice, real-time ad deliveries are difficult due to the size of the ad-delivery decision problem. This research aims at analyzing and developing a decision policy for delivering ads on mobile devices such as cell phones. We look at the ad delivery problem from the perspective of an advertising firm, which delivers ads on behalf of its clients (merchants) to mobile customer using a wireless carriers infrastructure. We formulate the mobile ad delivery problem as a Markov decision process (MDP) model. The ad delivery policy depends on the customers desire and willingness to receive ads, their real-time locations, historical mobility patterns, available network capacity, and fee structure agreed upon for ad deliveries. We also develop a fast heuristic to solve larger size problems. We test our heuristic against an upper bound we developed and analyze performance using simulation

Admediation: New Horizons in Effective Email Advertising

Using incentive-based approaches to match interested buyers and sellers.

Economics of first-contact email advertising

Since the advent of the Internet, email has emerged as an important new form of personal communication. The focus of this research is on commercial advertising through the email channel. We analyze the underlying economics of a business model termed admediation that facilitates effective first-contact email advertising. Admediary is a trusted third party that facilitates a mutually desirable communication between buyers and sellers via email, and operates under the opt-in mode widely supported by the consumer advocacy groups. Our analytical model examines the incentive structures for all participating entities, and derives pricing strategies, profit implications and characteristics of the email lists. We develop and model a form of price discrimination we term sequential elimination price discrimination that can be practiced via email. Our results indicate that the transactions facilitated by the admediary can create significant value whereby every participating entity realizes increased benefits. These findings underscore the potential of admediation to restore email as an effective communication media for online advertising.

Optimal Lot Sizing Policies For Sequential Online Auctions

This study proposes methods for determining the optimal lot sizes for sequential auctions that are conducted to sell sizable quantities of an item. These auctions are fairly common in business to consumer (B2C) auctions. In these auctions, the tradeoff for the auctioneer is between the alacrity with which funds are received, and the amount of funds collected by the faster clearing of inventory using larger lot sizes. Observed bids in these auctions impact the auctioneers decision on lot sizes in future auctions. We first present a goal programming approach for estimating the bid distribution for the bidder population from the observed bids, readily available in these auctions. We then develop models to compute optimal lot sizes for both stationary and non-stationary bid distributions. For stationary bid distribution, we present closed form solutions and structural results. Our findings show that the optimal lot size increases with inventory holding costs and number of bidders. Our model for non-stationary bid distribution captures the inter-auction dynamics such as the number of bidders, their bids, past winning bids, and lot size. We use simulated data to test the robustness of our model.

Narrowcasting of wireless advertising in malls

Abstract Wireless devices are personal, and the advertiser can schedule ads to reach the prospect at the proper time and place, which makes wireless advertising an ideal direct marketing tool. Industry associations, fearing a backlash from uncontrolled spam, have been careful to emphasize opt-ins. Yet the market has been more promise than reality. In spite of the possibilities of personalization, one of the major complaints has been that the ads are not relevant. Proper targeting and scheduling of wireless ads can go a long way to alleviate this problem. In a recent paper [De Reyck, B., Degraeve, Z., 2003. Broadcast scheduling for mobile advertising. Operations Research 51(4), 509–517] discuss a real implementation of wireless advertising in a shopping mall in London, and provide an integer programming model to schedule ads to various segments of prospects over a week. This model was found to be very effective in increasing response rates without saturating the devices with too many ads. We show in this paper that De Reyck and Degraeve’s model can be made more effective by utilizing information that is readily available to the advertiser. Our test runs show an improvement in the sum of ad priority value of about 150%, depending on the type of traffic in the mall. We provide two enhanced integer programming models that incrementally use additional contact history information to better scheduling of ads. Results from extensive runs are presented and managerial insights discussed.