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Increasingly, human interactions are communicated using electronic, internet-based medias. It allows for easy access to a lot of content in an organized format within a short amount of time. This creates for an ideal setting for facilitating online dating networks, where its users search for other users with the same intimate-based goals by using the community. Online dating communities are tailored specifically to users who are looking for a romantic partner, in contrast to social networking websites (Quesnel, 2010). The main difference between social media platforms and dating communities is that the first connects people who already know each other, and the second connects people that would like to know each other (Piskorski, 2014).

The growing popularity of online dating websites is altering one of the most fundamental human activities; finding a date or even a marriage partner. Research from the US Census Bureau has shown that 46% of the single population in the US uses online dating to initiate and engage in the process of finding a partner (Paumgarten, 2011). A recent trend is that online dating platforms offer new capabilities to users, such as extensive search, big data-based mate recommendations and varying levels of anonymity, whose parallels do not exist in the physical world (Gelles, 2011). However, little is known about the causal effects, which the authors of this paper seek to examine. Moreover, the authors of this article ran a randomized field experiment on a major North American online dating website, where 50,000 randomly selected users were gifted the ability to anonymously view profiles of other users. The control group was not able to anonymously view other profiles.

The effect of anonymity on users’ behavior

Anonymity may impact a user’s behavior through two distinct causal mechanisms (Bakos, 1997). First of all, lowering searching costs may lead to improved matching because users can express true preferences. An anonymous user has uninhibited access to information as compared to non-anonymous user, who may not visit a profile or regret visiting a profile, because the other user can see this. Because of anonymity, users do not need to worry about repeatedly visiting one’s profile, which is normally seen as stalking or inappropriate behavior. Furthermore, anonymity may impact the matching process because of the lack of signaling-related mechanisms, which are necessary to establish successful communication with a potential mate. It leads to an information asymmetry in which anonymous and non-anonymous users differ in the ability to gather information about the users they are interested in. Therefore, the research objective is to examine the net effect of disinhibition and signaling in online dating (Bapna, 2015).

Social norms may also inhibit the expression of what are considered taboo preferences, such as same-sex and interracial mate seeking (Panchankis and Goldfried, 2006). An anonymity feature may potentially lower this stigma, thereby lowering searching costs and resulting in improved search and improved matching.

Findings

The results of the article suggest that weak signaling is a key mechanism in increasing number of matches. Anonymous users ended up having fewer matches compared with their non-anonymous counterparts, as they were not able to leave a weak signal to the profile they viewed. This effect was particularly strong for women, as they tend not to make the first move and instead rely on the counterparty to initiate the communication. The reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches.

The results of this article also show that straight individuals of both genders significantly increase their likelihood of viewing profiles of users of the same gender when they are anonymous. Yet, total number of matches decreases for the anonymous users. Furthermore, this research shows that incoming views and messages decrease because of anonymity, while the number of outgoing messages remains unchanged. The findings of this article from the basis for further research on how the internet, social media and social communities are changing some of the fundamental activities we carry out as humans. Last, the results can also be used to further examine the impact of various levels of privacy protection on individuals’ behavior (Goldfarb and Tucker, 2011).

Sources

Bapna, R., Ramaprasad, J., Shmueli, G., and Umyarov, A. (2016) One-Way Mirrors in Online Dating: A Randomized Field Experiment. Management Science, 62(11), 3100-3122

Bapna R, Umyarov A (2015) Do your online friends make you pay?
A randomized field experiment on peer influence in online
social networks. Management Science, 61(8):1902–1920.

Bakos, J. (1997) Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12):1676–1692

Gelles, D. (2011) Inside Match.com. Financial Times, accessed 10 March 2018.

Goldfarb, A. and Tucker, C. (2011) Privacy regulation and online advertising. Management Science, 57(1):57–71.

Pachankis, J. and Goldfried, M. (2006) Social anxiety in young gay
men. J. Anxiety Disorders 20(8):996–1015

Paumgarten N (2011) Looking for someone: Sex, love, and loneliness on the Internet. New Yorker, http://www.newyorker.com/reporting/2011/07/04/110704fa_fact_paumgarten, accessed 10 March 2018.

Piskorski MJ (2014) A Social Strategy: How We Profit from Social Media, Princeton University Press, Princeton, NJ.

Quesnel, A. (2010) Online Dating Study: User Experiences of an Online Dating Community. Inquiries Journal, 2(11): 1-3.

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.

Airline industry

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).

Future chances

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)

Sources:

Baird, N. (2017) “Dynamic vs. Personalized Pricing”, https://www.rsrresearch.com/research/dynamic-vs-personalized-pricing, accessed at 13^th of February 2018.

Bertini, M. and Koenigsberg, O. (2014) “When Customers Help Set Prices”, MITSloan Management Review, accessed at 14^th of February 2018.

Hinterhuber, A. and Liozu, S. (2014) “Is innovation in pricing your next source of competitive advantage?” Elsevier Inc, accessed at 14^th of February 2018.

Lufthansa (2018) “Official website”, http://www.lufthansa.com, accessed at 14^th 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 14^th 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 13^th 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 14^th of February 2018.