Participatory Sensing in Commerce: Using Mobile Camera Phones to Track Market Price DispersionNirupama Bulusu1, Chun Tung Chou2, Salil Kanhere2, Yifei Dong2, Shitiz Sehgal2, David Sullivan2 andLupco Blazeski21Computer Science Department, Portland State University, Portland, OR 97207, USAEmail:{nbulusu}@cs.pdx.edu2School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, AustraliaEmail:{ctchou, salilk, ydong, shitiz.sehgal, dsul945, lbla241}@cse.unsw.edu.auAbstractIn economics, price dispersion refers to the price difference of a homogeneous good across different vendors. According to [1] “The empirical evidence suggests that price dispersion in both online and offline markets is sizeable, pervasive, and persistent.” Not surprisingly, there exist several popular web commerce sites such as Froogle that enable users to track consumer pricing information in online markets. In this paper, we present and explore our vision that participatory sensing can be employed in this new application domain to track price dispersion in homogeneous consumer goods even in offline markets. We discuss two proof-of-concept participatory mobile camera-phone sensing systems that we have built: (1) automating fuel price collection, and (2) semi-automated scanning of receipts.I. INTRODUCTIONPrice dispersion of homogeneous goods is a fact of life [1]. We emphasize homogeneity because if two goods are not homogeneous, such as televisions of different brands, then there is a quality difference which makes them hard to compare quantitatively. We have encountered myriad real life examples of price dispersion. For example, the following homogeneous goods were sold at different stores at fairly different prices at the same time in June 2008. We observed a $10 price difference for multivitamins (a $30 product) between Costco and RiteAid stores, and nearly a $200 price difference for HDTVs (a $2000 product) between Circuit City and Best Buy. Online, the quoted air fare for the same flight was $600 higher at Expedia than Lufthansa $2600 at the same instant of time. Price dispersion is attributed to several causes. A seminal article by Varian [2] suggests that price dispersion might be a deliberate marketing ploy by retailers to entice consumers into exploring their choices. Nevertheless, a major cause is the consumer search cost incurred in collecting pricing information from competing retailers, including the opportunity cost in time in acquiring this information [Baye06]. Price dispersion remains widely prevalent on the Internet (15-17%) [3], although studies have speculated that the low Internet search cost, wherealternate retailers are often just a mouse click away, will eliminate price dispersion [4]. Not surprisingly, numerous web commerce sites such as Shopzilla1 and Amazon2try to remedy this situation in online markets by providing a clearinghouse of price information for a homogeneous good for different e-retailers. There are compelling reasons for creating such a clearinghouse of up-to-date product pricing information, even for offline markets of brick and mortar stores. It could create arbitrage opportunities, wherein an enterprising person can leverage the price difference for profit. The availability of real-time price dispersion information can empower consumers to more effectively negotiate prices [5]. In online markets, studies cited by [1] show that savvy consumers who use on-line price comparison sites save up to 16% in consumer electronics purchases.Numerous consumer communities are already tracking price dispersion manually. A group of Hong Kong 1 http://shopzilla.com/2 http://www.amazon.com/housewives divide themselves into teams to manually copy prices of selected staple grocery items in major supermarkets and local grocery stores, and upload the prices to a website, prompting a major Chinese newspaper to advertise weekly grocery prices across different stores on its website3. In several countries, petrol price information is collected manually, by volunteers or employees of websites such as gaspricewatch4 (USA) and motormouth5(Australia). Manual price information collection is cumbersome, error-prone and not up-to-date.Our vision is to apply participatory sensing to share consumer pricing information and reduce the search costs of tracking price dispersion in offline markets. We are motivated by the success of the Wikipedia, Youtube and BitTorrent applications that are driven by altruistic user participation. In this paper, we explore two participatory camera phone sensing systems: (1) automating fuel price collection, and (2) semi-automated scanning of receipts.II. RELATED WORKParticipatory Sensing enables collection and dissemination of environmental sensory data by ordinary citizens,through devices such as mobile phones, without requiring any pre-installed infrastructure [6]. Researchers have recognized its potential and applied it in many domains, including but not limited to, health (DietSense) [7], intelligent transportation (TrafficSense) [8] and air-quality monitoring [9]; however, to the best of our knowledge, participatory sensing has not been applied in commerce. As in our proof-of-concept systems, DietSense and TrafficSense use camera phones. Researchers are also developing geo-mapped clearinghouses such as SensorMap6to simplify sensor data sharing. Our goal is to extend this idea to pricing information collected by image sensors.The use of mobile phones to enable micro-transactions in commerce has burgeoned over the past few years, particularly in the developing world. It is estimated that Indian farmers get only about 20-25% of the final purchase price of their agricultural produce (about 40-50% for farmers in USA), while most of the rest goes to middlemen. The recently introduced Reuters Market Light services provides farmers with up-to-date information on crop prices and related agricultural news via SMS messages to their mobile phones [10]. Key distinctions between this work and our vision are that we focus on empowering the consumer community, and focus not only on modes of disseminating pricing information to users, but also modes of collecting information from consumers. Parikh has used camera phones to scan loan applications for supporting rural microfinance in the CAM
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