Among ‘tailored offerings’, ‘interests-based content’ and other promises of customized user experience companies employ to retain their clients and boost profitability, ‘personalized pricing’ is perhaps the holy grail. Personalized pricing entails offering each customer a unique price for a product, according to their individual profile. In more technical terms, personalized pricing is a First-Degree Price Discrimination Mechanism, aiming to identify the exact amount each customer is ready to pay for a product (their ‘maximum willingness to pay’) in order to maximize potential profits for the firm and make the product affordable for as many people as possible (Varian, 1996). The maximum willingness to pay can be inferred from the aforementioned individualized customer profile, which is based on a series of factors such as employment situation, geographical residence and level of interest in the given product. The higher the number of factors taken into account when building the customer profile, the higher the likelihood that the inferred maximum willingness to pay is equal to the customer’s actual one (Stobierski, 2020).
With companies seemingly having more access to granular customer data than ever before, ‘big data’ analytics techniques evolving rapidly and clear economic incentives to implement personalized pricing, it can appear surprising that this practice is still relatively uncommon in the market (Vomberg, 2021). However, early adopters have faced several difficult hurdles in implementation, detracting others from trying the model. The key difficulties in implementing personalized pricing are:
1. Customer backlash over perceived unfairness
So far, companies openly engaging in ‘pure personalized pricing’ (i.e., showing different retail prices for each customer based on their profiles) have been subjected to severe backlash, with customers questioning the fairness of the process and feeling aggrieved when finding out they paid more than some of their peers for the same product. Amazon was one of the pioneers of this technique in the early 2000’s, using personalized pricing for its best-selling DVDs range (Reinartz, 2002). The customers’ anger was so severe that the company quickly reversed its policy and to this day, has not discussed introducing it again.
2. Data usability and accuracy
While larger amounts of data can now indeed be more easily stored and analyzed, growing public concerns over privacy are limiting companies in the use of the customers’ individualized data. In addition, more tech-savvy customers can try to understand the algorithms behind personalized pricing and ‘game’ the system to obtain the best deals, regardless of their true customer profiles (Howe, 2017).
3. Regulatory concerns
Regulators and, in particular, Competition Councils, are stringently assessing any attempts at personalized pricing, wary of the potential anti-trust implications they may have. Migros, an international grocery retailer, was severely challenged by Swiss regulators when deploying a new ‘personalized discounts’ scheme in its stores throughout the country. Moreover, governmental entities must ensure no discrimination based on sensitive characteristics such as sex, religion or nationality is made (Nakahara, 2020).
Given the existing obstacles, it is likely that the business environment is still years away from seeing personalized pricing become ubiquitous. Creating transparency over the algorithms behind personalized pricing, in order to win the customers’ trust, while making the system complex enough not to be easily ‘gamed’ and ensuring all regulatory requirements are met present a formidable challenge for businesses over the next decade. However, the first ones to manage this successfully stand to gain considerable boosts to their margins and even unlock a new source of competitive advantage.
References
Howe, N. (2017) A Special Price Just for You. Forbes. Available at: https://www.forbes.com/sites/neilhowe/2017/11/17/a-special-price-just-for-you/?sh=4aba44bb90b3 (Accessed: September 28, 2021)
Nakahara, M. (2020) Regulatory Solutions for Personalized Pricing. The Regulatory Review. Available at: https://www.theregreview.org/2020/08/06/nakahara-regulatory-solutions-personalized-pricing-2/ (Accessed: September 28, 2021)
Reinartz, W., 2002. “Customizing Prices in Online Markets.” Emerging Issues in Management, 6(1), doi:10.4468/2002.1.05reinartz.
Stobierski, T. (2020) Willingness to pay: what it is & how to calculate. Harvard Business School Online. Available at: https://online.hbs.edu/blog/post/willingness-to-pay (Accessed: September 28, 2021)
Varian, H., 1996. “Differential Pricing and Efficiency.” First Monday, 1 (2-5), https://doi.org/10.5210/fm.v1i2.473.
Vomberg, A. (2021) Pricing in the Digital Age: A Roadmap to Becoming a Dynamic Pricing Retailer. Groningen Digital Business Centre. Available at: https://www.rug.nl/gdbc/white-paper-digital-pricing.pdf (Accessed: September 28, 2021)
