Tell me who you are, I’ll tell you how much you pay.

Algorithmic pricing methods are on the rise nowadays since they allow companies, especially retailers, to maximize their sales and, at the same time, their margins.

This twofold goal can be achieved by customizing the price charged for each product at the individual customer level. By doing so, companies can incorporate each customer’s willingness to pay for a product in their pricing mechanisms. For example, suppose customer A is willing to pay a maximum of 100€ for a jumper, while customer B is willing to pay a maximum of 85€ for the same jumper. In that case, the retailer might propose a different price to the two customers, incorporating their different levels of price acceptability, as long as both prices are profitable for the retailer.

Clearly, retailers necessitate excellent input data to feed the algorithmic pricing software with appropriate data, allowing the software to reach an accurate estimation. For instance, the probabilistic models on the basis of algorithmic prices might request access to relevant information, such as historical transactional data, store-level data, and even biometric information, allowing the detection of consumers’ emotions while shopping.

Albeit the clear advantages in terms of accuracy in setting the prices, such algorithmic pricing methods pose relevant issues in terms of consumers’ privacy concerns, which constitute the basic aspect investigated in the present research.

Specifically, this work aims to understand the extent to which consumers perceive that the benefit of receiving customized prices for the product outweighs the “costs” they afford in terms of the amount of personal information they have to disclose in exchange for personalized prices.

With these regards, the present article adopts the consumer perspective to investigate customers’ psychological reactions and behavioral intentions toward disclosing their personal information to retailers.

Specifically, this work addresses the key role played by two relevant aspects pertaining to customers’ personal information disclosure, namely the sensitivity of the data they have to disclose and the type of benefit they receive in exchange.

With respect to the former, the extant literature has widely investigated the effect exerted by consumers’ disclosure of their behavioral data, such as their shopping habits or previous purchasing history, and found that consumers might accept to disclose them as long as the benefits they receive in exchange are perceived equitable (Privacy Calculus Model).

Despite the increasing number of technologies allowing the detection of some customers’ biometric information (e.g., fingerprint authentication for payment, in-store cameras equipped with AI face reading to detect consumers’ movements, etc.), there is still a paucity of studies empirically exploring if and to what extent biometric data trigger more severe privacy concerns and how such concerns might be mitigated with appropriate incentives offered by retailers.

In this vein, the incentives could potentially range from non-monetary benefits (e.g. customized assortments and suggestions) to monetary reward (e.g. dedicated discounts yielding a personalized price).

Notably, the present research assumes that data disclosure represents an effort from the consumer perspective, which makes them feel entitled to pretend more from the retailer.

The joint effect of the type of data to be disclosed by the customer and benefit offered in exchange by the retailer has been investigated by means of two experimental studies which involved more than seven-hundred participants recruited online from a panel of British and American respondents.

As expected, results from the two studies showed that individuals react more negatively to the request of biometric data, thus requiring more conspicuous monetary incentives from the retailer to counterbalance privacy concerns.

Indeed, results from the present research support that consumers feel more entitled to pretend higher incentives from the retailer when they are more concerned about the disclosure of their personal information.

Indeed, on the one hand, Study 1 shows that consumers will display more favorable future intentions (in terms of intention to visit and to stay loyal) toward those retailers offering equitable incentives in exchange for the data disclosed. On the other hand, Study 2 clearly demonstrates that the amount of the discount expected by consumers is significantly higher if they consent to the disclosure of their biometric information.

Published in: Journal of Business Research

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Authors at the Department of Management

Gabriele Pizzi – Associate Professor

Academic disciplines: Marketing

Teaching areas: Marketing, Retailing

Research fields:  Retailing and Channel Management; Technological Innovation in Retailing

Gabriele Pizzi is an Associate Professor of Marketing and Scientific Director of the Observatory on Retailing in partnership with Retail Institute Italy. In 2017, he received a Research Grant from the Italian Marketing Society for his research on the application of VR in the Retail industry, and he is currently the Principal Investigator for a PRIN project funded by the Italian Ministry for University and Research.

 Virginia Vannucci – Assistant Professor

Academic disciplines: Marketing

Teaching areas: Social Media Marketing, Branding

Research fields:  Retailing and Channel Management; Technological Innovation in Retailing

Virginia Vannucci is an Assistant Professor of Marketing. In 2017, she received a Research Grant from the Italian Marketing Society for his research on the application of VR in the Retail industry. Her research interests revolve around the marketing discipline and deal with retail marketing, digital technologies (VR, blockchain, chatbot, etc.), innovation, branding, and privacy issues.