Personalized Pricing and Its Potential Pitfalls
Dynamic pricing has recently taken over the online shopping market. It is typically driven by elements like modern shop software, complex tracking models, and vast data analytics. Through this approach, companies can set individual-specific prices for any consumer. The ultimate goal is to maximize profits. However, while in theory, this approach (coined as perfect price discrimination) enables firms to capture the entire consumer surplus, real-world applications are determined by significant complexities and risks. Keep reading the chapters below to learn more about potential pitfalls.
Perfect Price Discrimination
Perfect price discrimination involves setting the highest price each consumer is willing to pay, thereby capturing the entire consumer surplus.
This method, while maximizing profits, assumes perfect information and rational behavior from consumers. However, most of the time, in the real world, these assumptions are not realistic (Varian, 1989). For instance, perfect information about each consumer’s willingness to pay is challenging to obtain, and consumers do not always behave rationally due to various cognitive biases and external influences (Shapiro and Varian, 1998). Furthermore, finding out the truth about every consumer’s willingness to pay for a product is quite challenging to get. Most consumers do not always behave as companies may assume.
Not perfect, but practical – Common Approaches to Pricing
Due to those limitations firms employ two other approaches which are more practical, and (especially before digitalization) less costly. They include:
1. Bulk Pricing
This involves offering quantity discounts to incentivize self-selection. By doing so, firms encourage consumers to purchase more by reducing the unit price for larger quantities, effectively segmenting the market based on purchase volume (Smith and Brynjolfsson, 2001).
2. Group Pricing
This is where a company gives discounts to a specific group of clients, based on their attributes like age, occupation, income, or health conditions. For example, student discounts or senior citizen pricing in the public transportation are common practices. This approach relies on observable characteristics that function as proxies for consumers’ willingness to pay (Chen and Hitt, 2002).
Advanced Technology and Personalized Pricing
Thanks to technological advancements and the introduction of AI and big data analytics, companies have been able to set prices more accurately (Acquisti and Varian, 2005). These technologies allow companies to analyze vast ranges of data like browsing histories, purchase patterns, and even social media activities. These elements allow more accurate predictions of customers’ willingness to pay for a product. Taking advantage of technological advancements can lead to improved profits. While this can theoretically lead to higher profits, it also relies on assumptions from traditional economic theory that do not always hold in practice (Clemons et al., 2002).
Risks and Challenges of Personalized Pricing
While dynamic pricing does have a lot of benefits, there are several risks and challenges that are associated with this approach. If these risks and challenges are not managed carefully, the approach could end up backfiring on a company instead of benefiting it. In that sense, let us look at some of these risks and challenges which might occur when setting personalized prices.
Consumer Backlash
Nowadays, thanks to social media, clients can easily voice their complaints. A few complaints from clients can turn into organized boycotts against what clients may perceive as unfair pricing practices. The last thing you want to do is annoy people who know how to harness the power of social media. What may seem like a simple social media boycott may turn into a significant issue for a brand. Two good examples were Amazon and Orbitz:
In 2000 Amazon generated dynamic prices based on search history and purchasing behavior which sparked outrage in online communities. 2012 Orbitz showed higher hotel prices to Mac users. This incident led to reputational damage due to negative media coverage. Both examples show how fast adverse consumer opinions can spread and affect a company (negatively). Expecially in the latter case, it is highly likely, that the boycott has led to significant drops in revenue.
Competitive Dynamics
Regardless of what market a company operates in, competition is something they cannot avoid. In the world of a lot of competition, it is very easy for clients to switch their preferred brands when they feel like there are falling victim to unfair prices. Price comparisons and network effects further influence market power (Gallego and Van Ryzin, 1994). For example, Zalando faced backlash when consumers realized that prices for the same items varied across different regions and platforms, leading to calls for boycotts and a re-evaluation of their pricing strategy. Companies lose a lot of clients when they realize that they can get the same product or service at a lower price from another brand. It can get even worse when clients share this information with their peers.
Product Differentiation
Brands can beat the backlash from dynamic pricing by making sure that their products are unique and can be differentiated from their competitors easily. A brand can retain loyalty from its clients by offering unique products. The uniqueness of the products is what will cause clients to look past the higher prices. However, if clients perceive any unfairness, their loyalty to a brand will be affected. A good example is how Amazon faced scrutiny from clients for its dynamic pricing practices despite its unique products. Even the uniqueness of its services could not save the brand from distrust from clients. This situation demonstrated that even market giants are not immune from the risks and challenges of dynamic pricing (Bakos and Brynjolfsson, 1999).
Fairness and Ethical Considerations
Clients value fairness a lot. So, if clients discover that they are being charged more than others, it can result in public outrage and boycotts. This can especially be a problem if clients discover that the pricing disparities are based on assumptions or wealth or specific consumer behavior. Ethical considerations are also very important in maintaining a positive brand image. For instance, Apple clients may have an issue with paying higher prices for phones while android users pay less, especially if the assumption is that Apple users are wealthier. This not only leads to individual boycotts but can escalate if income sources or self-image are involved. Dierksmeier et al. (2019) provide a comprehensive literature review on this field.
The Role of Understanding Consumer Behavior in Implementing Personalized Pricing
Understanding consumer behavior is very important in implementing effective strategies for personalized pricing. Recent advances in the field of consumer neuroscience offer deeper insights into how consumers make their purchasing decisions and how they respond to pricing. According to Neuroscientists, the orbitofrontal cortex encodes a client’s willingness to pay for a product. This information offers the neural basis for understanding how clients evaluate prices and make their purchasing decisions (O’Doherty et al., 2007). Further studies indicate that the human brain responds strongly to perceived fairness in economic transactions. This explains why clients respond negatively to unfair pricing practices (Aronson et al., 2003). More studies have found that the brain plays a key role in influencing purchasing decisions and the emotional impact that pricing strategies have on clients (Knutson et al., 2007).
Privacy and Data Security
Dynamic pricing based on personal data raises significant privacy concerns. Consumers are becoming increasingly aware of their privacy rights and the extent to which their data is being used. If consumers feel their privacy is being invaded or their data is being misused, it can lead to backlash and loss of trust. Data breaches can also have severe repercussions, including legal penalties and eroded consumer trust (Gallego and Van Ryzin, 1994). For instance, consumers who feel their personal information is not secure are less likely to engage with a company.
Legal Risks
Dynamic pricing can also attract regulatory scrutiny and legal risks if the process is perceived as unfair or discriminatory in any way. Regulatory standards regarding pricing practices differ from one country to the next. Non-compliance with such regulations can lead to legal battles and hefty fines (Bakos and Brynjolfsson, 1999). There are also consumer protection groups that may rise against a company. The last thing a company wants is to deal with lawsuits and class actions as a result of unfair pricing practices.
Technological Dependence
Relying on algorithms for pricing decisions can lead to errors due to incorrect data, biases, or technical glitches, resulting in unfair pricing and subsequent backlash. For example, airlines often face backlash for dynamic pricing, especially when consumers realize prices fluctuate based on demand and booking times. Incorrect data inputs, biases in algorithms, or technical glitches can result in unfair or incorrect pricing, leading to consumer dissatisfaction (Clemons et al., 2002).
3 Strategic Implications when Considering Personalized Pricing
Companies can navigate the challenges and risks of dynamic pricing by considering the following aspects:
- Transparency and open communication – Companies can practice fair and clear communication with their clients about their pricing strategies. Educating clients on why prices may vary will help with building and maintaining their trust.
- Monitoring and adaptation – companies can also frequently monitor the sentiments from their clients. Additionally, companies must be ready and willing to adjust their pricing strategies to maintain the trust of their clients. Staying attuned to feedback from clients and social media trends will help.
- Finding a balance between benefits and risks – companies also need to weigh the benefits of dynamic pricing and the potential risks that are attached to them (boycott, reputational damage, image loss, etc.) . This will help them make better pricing decisions.
Conclusion: Fairness Perception is a Significant Factor
Dynamic pricing is a good strategy for enhancing profits for a company. However, it can result in significant risks, especially if clients perceive it as unfair. Therefore, companies have to navigate this approach carefully and bare in mind the potential backlash that may arise from it. Understanding the challenges and risks of dynamic pricing can help companies implement better strategies that are acceptable to clients. The ultimate success of dynamic pricing is highly dependent on striking a balance between pricing and maintaining trust and fairness to consumers.
References
- Acquisti, A., & Varian, H. R. (2005). Conditioning Prices on Purchase History. Marketing Science, 24(3), 367-381.
- Bakos, Y., & Brynjolfsson, E. (1999). Bundling and Competition on the Internet. Marketing Science, 19(1), 63-82.
- Chen, Y., & Hitt, L. M. (2002). Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry. Information Systems Research, 13(3), 255-274.
- Clemons, E. K., Hann, I. H., & Hitt, L. M. (2002). Price Dispersion and Differentiation in Online Travel: An Empirical Investigation. Management Science, 48(4), 534-549.
- Dierksmeier, C., Hofstetter, M., Seele, P., R., Schultz. (2019). Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing. Journal of Business Ethics.
- Gallego, G., & Van Ryzin, G. (1994). Optimal Dynamic Pricing of Inventories with Stochastic Demand Over Finite Horizons. Management Science, 40(8), 999-1020.
- Knutson, B., Rick, S., Wimmer, G. E., Prelec, D., & Loewenstein, G. (2007). Neural Predictors of Purchases. Neuron, 53(1), 147-156.
- Plassmann, H., O’Doherty, J., & Rangel, A. (2007). Orbitofrontal Cortex Encodes Willingness to Pay in Everyday Economic Transactions. Journal of Neuroscience, 27(37), 9984-9988.
- Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The Neural Basis of Economic Decision-Making in the Ultimatum Game. Science, 300(5626), 1755-1758.