There are several ways for companies to distinguish themselves in the way they price their products and services. They can choose for group pricing, which segments customers in groups that tend to behave similarly towards prices. For example, customers can be grouped based on age (such as student discount), gender or living area. Another option is to use versioning: to offer a product line and let customers decide on the trade-off between quality and price. The last form of differential pricing is perceived as difficult to achieve, namely personalized pricing. This means each individual customer receives a personal price for a specific product or service (Schofield, 2018). You may think that, in an offline world, no customer would accept personalized pricing. Can you imagine buying bread and cheese at a grocery store, and the person in front of you pays less for the exact same groceries? However, in an online world, this method has become a lot more feasible. Actually, there is a large chance you have already experienced personalized pricing online. One of the most obvious examples is eBay: one of the first companies to implement personalized pricing with their worldwide market place platform. However, it is important not to interpret personalized pricing as dynamic pricing. The main difference between these two forms of pricing is the variables that determine the final price. In dynamic pricing, the variables that are taken into account are, for example, time of the day, available supply or competitors’ prices (Baird, 2017). Personalized pricing has a customer focus and is interested in a specific customers’ behavior. Companies use data analytics to identify characteristics of the purchase environment or the customer’s profile and behavior that impact their willingness to pay. Bertini and Kounigsberg (2014) argue that the success of personalized pricing depends on at least the following three factors. First, abundant, high-quality data is needed. Also, the companies need to overcome various organizational challenges that come hand in hand with dedication to advanced analytics. Last, companies should be prepared to deal with customers who claim that the pricing approach is not fair.
One of the largest industries that divides consumer groups and price accordingly, is the airline industry. Different fares are charged for the exact same product, based on a market segment’s perceived ability to pay. For example, business travelers tend to pay more for their ticket as compared to leisure travelers, even when they fly the exact same route (Sumers, 2017). The key success is working to learn what the customer needs. Lufthansa, the largest European airline in teams of fleet size and passengers carried in 2017, is testing various approaches to better understand their customers. For example, they have deployed Bluetooth beacons and sensors, to be able to send out real time messages to their customers. When a targeted customer goes through security and has Bluetooth enabled on their phone, the personalization process is started. Or as Lufthansa calls it, the “Big Data Engine”. This program checks a traveler’s mobile boarding pass and looks at how much time the traveler has left before departure. If it is more than a set amount of time, the system examines the traveler’s profile in order to determine whether the customer would be interested in the “Miles and More” program, a discount for access to the airport lounge. This information is combined with the data from the sensors in the lounge, that register whether and how much space is left in the lounge, in real time. This lounge promotion program is part of SMILE., a companywide program that is dedicated to personalizing travel (Lufthansa, 2018). Companies can also use traveler data to offer two or more products or services as a package, increasing profits as it allows companies to appropriate a larger share of customer surplus, known as bundling (Hinterhuber and Liozu, 2014).
Although airlines have quite an advanced personalized pricing and recommendation system, there is more potential to be revealed in the future. Lufthansa is working on larger projects that try to develop a Netflix-style algorithm that seeks to guess where its most frequent flyers would like to go to next (Sumers, 2017). The airline then offers a personalized price and ticket to this customer, and further develops its algorithm using customer data. For airlines to stay competitive, they need to keep a close eye on the current and future changes in the market. First of all, airline companies should fully embrace innovation. Data should be used not only to cut costs and to be able to deliver the cheapest flight tickets, but also to facilitate new customer experiences and deliver more personalized services. This leads to an increase in importance of brand loyalty, as consumers are more closely connected to the airline that is best at personalizing their prices and services. Last, the mobile wallet should be seen as the central hub for the digital consumers. Mobile transactions are a lot richer in terms of data collection and analysis, and it provides access to end-consumers, which can drive more sales (Popova, 2016)
Baird, N. (2017) “Dynamic vs. Personalized Pricing”, https://www.rsrresearch.com/research/dynamic-vs-personalized-pricing, accessed at 13th of February 2018.
Bertini, M. and Koenigsberg, O. (2014) “When Customers Help Set Prices”, MITSloan Management Review, accessed at 14th of February 2018.
Hinterhuber, A. and Liozu, S. (2014) “Is innovation in pricing your next source of competitive advantage?” Elsevier Inc, accessed at 14th of February 2018.
Lufthansa (2018) “Official website”, http://www.lufthansa.com, accessed at 14th of February 2018.
Popova, N. (2016) “Has Personalization of Passenger Experience Entered a Critical Stage?”, https://skift.com/2016/12/29/has-personalization-of-passenger-experience-entered-a-critical-stage/, accessed at 14th of Febuary 2018.
Schofield, T. (2018) “Price discriminations: definition, types, and examples”, https://study.com/academy/lesson/price-discrimination-definition-types-examples.html, accessed at 13th of Febuary 2018.
Sumers, B. (2017) “Airlines Become More Sophisticated With Personalized Offers for Passengers”, https://skift.com/2017/02/03/airlines-become-more-sophisticated-with-personalized-offers-for-passengers/, accessed at 14th of February 2018.