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The Physical Cookie

In an evermore interconnected world, where objects, places and people constantly exchange data and information, boundaries between offline and online activities are constantly being redefined, raising not only opportunities but also urging many industries to reevaluate and adapt their processes. (Brynjolfsson & al. 2013) The retail sector is no exception to this phenomenon and increasingly seek to develop business intelligence by adopting new digital retail technologies (Rigby, 2011). Among them, the Physical Cookie is an innovation first experimented in a shopping centre in autumn 2014 in Helsinki. It uses real-time Radio-frequency identification(RFID)-based shopper tracking (Silberer and Friedemann, 2013) and enables elaborated marketing practices such as providing the consumer with targeted offers on multiple connected screens and displays across the shopping complex, very much like the internet cookie (or browser cookie) does online. The analogy between both objects is of high relevancy when studying the Physical Cookie, since its online precursor has been raising intense debate publicly and among scholars over the last decade, especially in regards of privacy issues. The Physical Cookie isn’t the first trial at transposing the online tracking method in the real world. However, these attempts seem to generate a greater feeling of intrusion and thus struggle to get large public acceptance, forcing their promoters to articulate substantial benefits for the users in order to overcome their reluctance. (Clifford and Hardy 2015) This paper aims at describing the specificities of the Physical Cookie and outlining the affordances it provides from both the retailer and the consumer perspectives. Alongside, it will set ground for further research and discussion about whether this technology effectively preserves privacy as argued by its inventors, or could be seen as a stepping stone for omnichannel retailing strategies (Frazer and Beate, 2014).

Materialised as a plastic keychain to be carried by the consumer at all time while shopping, the device’s fairly simple form – longitudinal, thin and with profiled edges – obviously aims at minimising friction when inserted or removed from the user’s pocket or handbag. It is worth noticing that the shape and colour could vary depending on the target customer group (Physicalcookie 2014). In this first version publicly tested, its pink colour can be perceived as addressing a feminine audience. Furthermore, the press material mainly pictures women experiencing the device, underlining a stereotypical gender oriented perception of the act of shopping. The design of the Physical Cookie suggests no particular functionalities to the user neither triggers any action. Moreover, it appears as an inert and passive device, which primary focus is to least interfere with the shopper’s activity, while running in the background as an essential brick of a larger infrastructure. The Physical Cookie is a passive Ultra High Frequency RFID tag – composed by a chip and an antenna – which can be tracked by active RFID readers located throughout the store and covering its surface. RFID technology exists since decades (Mitton and Simplot-Ryl, 2011) and is applied in many daily applications where wireless capabilities are required. RFID has gained a certain public acceptance as a conventional technology over the course of the years, allowing foreseeable opportunities for further sophistication. However, this specific use can by far not be considered as publicly accepted (Silberer and Friedmann, 2013). Along his shopping experience, the shopper leaves digital crumbs, payment history and behavioural data which, operatively coupled with the store’s computer system and payment terminals, are processed and rendered for the store’s manager into a comprehensive digital dashboard. In return, the consumer accesses to an augmented shopping experience in which the commercial environment anticipates his expectations and channels a tailored set of advertisements, offers and pricing adjustments. The shopping centre thus becomes a dynamic network where humans and non-humans actors influence each other’s behaviours. While the consumer experiences an effortless amplification of his shopping activity across the stores, the retailers of the mall are given the opportunity to dynamically drive their marketing plan with a set of experimental broadcasting tools and extensive metrics to evaluate the efficiency of their strategy. Notably, the rendering of consumers wayfinding patterns (Titus and Everett, 1995) as detailed visual maps reveals a new layer of information and insights which hold the potential to reevaluate space and eventually reshape the stores arrangement.

The manufacturer claims the system to be anonymous, non-intrusive and assimilable to marketing-as-service, since it doesn’t contain personal information such as name, e-mail or phone number. Therefore, the user is always addressed in an impersonal manner such as “Hey there shoe lover” on the screens engaging him with an offer. This important characteristic distinguishes the Physical Cookie from other in-store tracking methods such as iBeacons which rely on users personal devices (Danova, 2014). This non-subscription model also facilitates user participation by lowering the opt-in requirements. However, promoting a tracking technology by arguing its non-intrusive nature seems inherently contradictory. It can understandably be argued that collecting this data, even under a non-nominative procedure still puts user’s privacy in the hands of a third party, who is driven by economical interests in the first place. Assuming the Physical Cookie database coexists with other loyalty programs or customers lists maintained by the stores, the situation defined as data fusion by Silberer and Friedemann is likely to occur: “If anonymous wayfinding data are merged with data containing personal information and recorded for entirely different purposes, the anonymous wayfinding data becomes personal data” (2013, 35). This potential issue should be further investigated, especially bearing in mind the value and the importance of such data in the omnichannel retailing perspective, followed by powerful actors putting great efforts in merging consumer’s offline behaviour to their online identity.


I wrote this essay in the context of the Master Programme in New Media Studies & Digital Culture
I am currently following at Utrecht University.


Bibliography

Brynjolfsson, Erik, Yu Jeffrey Hu, and Mohammad S Rahman. 2013. “Competing in the Age of Omnichannel Retailing.” MIT Sloan Management Review 54 (4). Massachusetts Institute of Technology: 23–29.

Clifford, Stephanie, and Quentin Hardy. 2015. “Attention, Shoppers: Store Is Tracking Your Cell – NYTimes.com.” Accessed March 2. http://www.nytimes.com/2013/07/15/business/attention-shopper-stores-are-tracking-your-cell.html.

Danova, Tony. 2014. “Beacons: What They Are, How They Work, And Why Apple’s iBeacon Technology Is Ahead Of The Pack.” Accessed March 1. http://www.businessinsider.com/beacons-and-ibeacons-create-a-new-market-2013-12?IR=T.

Frazer, Mariette, and Beate Elizabeth Stiehler. 2014. “Omnichannel Retailing: The Merging of the Online and off-Line Environment.” Global Conference on Business & Finance Proceedings 9 (1).

Hind, John R., James M. Mathewson, and Marcia L. Peters. 2006. “Identification and Tracking of Persons Using RFID-Tagged Items in Store Environments.” United States of America. doi:10.1074/JBC.274.42.30033.(51).

Mitton, Nathalie, and David Simplot-Ryl. 2011. “From the Internet of Things to the Internet of the Physical World.” Comptes Rendus Physique 12 (7). Elsevier Masson SAS: 669–74. doi:10.1016/j.crhy.2011.06.006.

“Physical Cookie.” 2014. http://www.physicalcookie.com/.

Rigby, Darrell. 2011. “The Future of Shopping.” Harvard Business Review 89 (12) 1–9.

Silberer, Günter, and Stefan Friedemann. 2013. “European Retail Research” 26 (I): 19–44. doi:10.1007/978-3-658-00717-1.

Titus, P. A., and P. B. Everett. 1995. “The Consumer Retail Search Process: A Conceptual Model and Research Agenda.” Journal of the Academy of Marketing Science 23 (2): 106–19. doi:10.1177/0092070395232003.

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