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The rapid development of the Internet and IT spurred new business models based on social commerce that is mediated by social media. Various community platforms and social networks create a space for people who are willing to share their resources, whereas the advancement of smartphones and e-commerce enabled its wide application. Airbnb, Uber, WeWork, and Kuaidi Dache are among the most innovative start-ups in the industry. Nevertheless, there are some serious challenges regarding safety, regulations, and ethics. Platforms and services like Uber enjoy access to consumer data that exponentially grow every day. Nowadays, all the information about transactions can be collected, stored, and then analyzed to gain insights about customer behavior, preferences, and willingness to pay. Big data usage, dynamic/personalized pricing, and digitalization of analog data was discussed by Levchin (2013) in his DLD13 keynote. Although the famous software engineer lists some risks concerning data processing, he sees data-driven understanding and dynamic pricing as an opportunity. Despite possible risks, consumer data and dynamic pricing may be beneficial both for customers and sellers as this model is more flexible, sustainable, and economically beneficial.
The sharing economy evolved as a popular and progressive business model that abandons intermediary third-parties. It is a decentralized economic model based on peer-to-peer interactions (P2P) to acquire, provide or share resources that are usually coordinated through community-based online services (Puschmann and Alt, 2016, p. 2). The sharing is often economically motivated and involves such C2C transaction methods as payments and bookings. For instance, BlaBlaCar provides customers with the ability to ride in another consumers car temporarily, which does not require the ownership transfer (Eckhardt et al., 2019). Such businesses are famous for collecting and using customer data to improve their offerings and performance.
The information that was analog before is now digitalized and centrally managed. For instance, Uber processes requests of consumers in a centralized queue, places them on auction, finds matches, and gives feedback in a real-time (Levchin, 2013). Although such services are known for transparency and feedback, they also allow companies to assess demand and efficiently price their services and products. Levchin (2013), in his speech, called this ability a network effect of data that makes it difficult to compete with analog-data-driven companies. The representatives of the sharing economy collect this real-time information and assess it to set dynamic prices and improve efficiency by targeting peoples willingness to pay. Big data analytics and pricing approaches would be discussed further to define the pros and cons.
With technological advancement and continuous generation of data, businesses started to collect personal, engagement, behavioral, and attitudinal data. The big data approach allows them to draw insights from and monetize customer information. According to Le and Liaw (2017, p. 3), big data is a collection of massive and complex datasets and data volume that includes huge quantities of data, data management capabilities, social media analytics, and real-time data. In other words, big data analytics was developed as a tool to predict, measure, and understand customer behavior. Data on previous purchases, consumer satisfaction, emotional tendencies, and product usage helps firms to design appropriate recommendation systems, customer service, and information search.
The application of different data mining techniques leads to personalized offerings that meet peoples current demands, hobbies, and preferences. Covid-19 pandemic significantly shifted consumer behavior towards using sharing platforms and e-commerce, which effects may persist for an extended period (Thomann, 2020). Consumers are mindful of their spending and care more about the products prices. E-commerce vendors utilize big data analytics and similar approaches to offer the experience of convenient online shopping, set dynamic prices, and provide consumer value through the appropriate channels. All these positive factors enable companies to cath customers intention, shape their behavior, and encourage them to take action.
Dynamic pricing is a big data-based strategy that is very often applied by e-vendors to maximize sales and revenue. According to Gibbs et al., (2018), it is a strategic revenue management tool that enables timely adjustment of prices in accordance with demand fluctuations, changes in competitors prices, time of the day, buying triggers, and consumer behavior. Internet marketing made this individual-level price discrimination more popular among modern businesses. Newly available information attracts retailers to set the highest prices every consumer is prepared to pay (Le and Liaw, 2017). This approach maximizes the sellers profit by capturing a higher proportion of the customer surplus.
Dynamic pricing is not a product of recent technology; it has been present for a while. Airlines were the first who adopted this strategy using the power of the Internet. During its inception, specialized personnel manually had been changing the ticket prices based on the time of day, how many seats were left, and how many days remained before departure. This idea, accompanied by the development of AI and significant investment, resulted in automatic price adjustment algorithms based on known information and factors. For instance, in 1960, American Airlines founded SABRE corporation that established the first reservation system. In 1985 the company presented the easySabre system that offered inline reservations of tickets, car rentals, and hotels (Jovin, McMurtrey and Griffin, 2018). Hence, such industries as hospitality and airlines have pioneered this concept. Then the system was transferred to travel agencies and e-commerce industries. Amazon started to use dynamic pricing back in 2013, while prices of Airbnbs listings began to take local events and seasonal changes into account in 2015 (Gibbs et al., 2018, p. 3). The implementation of dynamic pricing still has high technical costs as airlines, car rentals, and hotels invest massively to develop systems which able to collect information, analyze it, and ultimately set prices autonomously. Moreover, these systems need specially trained human supervisors who can verify and approve price changes.
Uber would be further used as an example to find out how dynamic pricing works and how it applied in the sharing economy. Levchin (2013) compared Uber platform to conventional cab calling and stressed that marketplace based on real-time queue management is a superior option due to its transparency. Uber mobile application is designed to match a rider with a nearby driver following the riders request for a lift. The reservation is guaranteed by the pre-saved credit card information in the app. In case when the driver accepts and fulfills the offer, the application takes into consideration the time and distance of travel to calculate the fare.
Then it charges the customer electronically whereby parties to the transaction do not have to waste time dealing with cash. The American company also applies a surge pricing algorithm that is an example of dynamic pricing practice. It is all about using the multiplier in times of high demand that increases standard fare. The firm perceives this practice to be beneficial both for drivers (due to higher profits) and for riders who wait less and value time. The Uber dynamic pricing was found to increase the ride-sharing market efficiency, though some decisions provoked public concern over its fairness (Gibbs, C. et al., 2018). During holidays and public events, especially on New Years Eve, Uber often charges passengers at up to eight times higher price for a ride home that often causes complaints.
In terms of software, the transportation network company applies machine learning to process consumer data and forecast the amount and time of future Uber requests from customers. Various holidays, sporting events, and concerts influence the outcome of demand prediction. Machine learning is a more sophisticated approach in comparison to the rule-based system (Altexsoft, 2019). The latter needs continuous human intervention to set new if-then statements, while machine learning enables the system to mine data and solve problems itself. This method avoids direct programming and does not need real-time updates to decision-making instructions. However, in this case, the more information is collected, the better this software operates and learns from it.
Events like New Years Eve when the number of riders exceeds the number of drivers occurs once a year. Thus, researches address the lack of recent data by the long short-term model (LSTM). This recurrent neural network remembers information about all trips completed during the last five years and uses time-series data to predict demand during extreme events (Altexsoft, 2019). Uber uses its application and car sensors to gather data on consumer preference, drivers, speed, and location of the vehicle and other previously analog but now digitized information. Such data then stored and analyzed to predict demand, set fares, and improve customer service.
Everybody who at least seldomly check IT periodicals is aware of dynamic pricing advantages. Companies adjust prices in real-time using the benefits of data mounts collected with the help of dynamic pricing. Levchin (2013) also mentioned in his keynote that it is difficult to compete with business rivals who use data mining. This approach allows them to react instantly to market conditions changes and maximize the revenue opportunity. Price adjustments are almost fully automated and timely what increases firms competitiveness.
Although dynamic pricing seems to be beneficial for business, it relates to a range of challenges and concerns. Privacy and data security are among the most critical concerns in the e-commerce world. Big data is usually stored in a concentrated way; thus, it can be a target of profit-seeking hackers. Such information becomes more and more valuable today, and competitors dream of getting access to their rivals databases. Personal customer information can be a subject of leakage, which posses a security challenge. Another privacy issue is connected with such technologies as cookies that enable real-time behavioral advertisements. Target advertising allegedly breaches privacy laws, especially in Europe, where it is forbidden to collect and process user data without consent (Edelman, 2020). According to Gaille (2018), customers do not like to be tracked and then targeted by the pricing strategy. They perceive it as an artificially created situation when they do not have a real choice other than to pay more for things they need.
Moreover, personalized prices, tailored from consumer-identifying information personal price, may lead to increased customer alienation. Prices vary because the system evaluates every online customers information concerning hobbies, income, interests, behavioral patterns, and then charges them in a personal way. For instance, the customer who searches for a product using an expensive smartphone or tablet and has impressive buying history may receive a higher bill just for those reasons. This model of dynamic pricing is currently rarely used because of negative fairness perception. E-retailers assess consumer willingness to pay and then sort them in special groups, what is a consumer privacy problem.
It is not-transparent for customers as they do not know which information is used and how it is assessed; thus, they perceive personal prices as manipulation and privacy infringement. Priester, Robbert, and Roth (2020) state that consumers who care about their privacy have specific behavioral responses to personalized prices, including lower trust, purchase intention, and unwillingness to disclose data. Although personalized prices increase revenue, the segment pricing causes fewer ethical concerns than the individual one. Mangers addressing these issues should give more information to their customers on the price forming factors. Furthermore, the illusion of control over the final price may also help them to mitigate the fairness perception of personalized pricing (Priester, Robbert and Roth, 2020). Without such alterations, this type of dynamic price setting has too many risks associated with adverse behavioral consequences and consumer satisfaction.
I like the future that Levchin envisioned during his speech back in 2013 when the social economy boosted its development. He was right, arguing that the digitalization of analog data creates room for incredible new efficiencies that have the potential to optimize almost all industries in the world. The analog-data-driven enterprises can provide their customers with products that they like, search for, and need. Different platforms, applications, and sensors that collect consumer information and unique algorithms establish a network effect of data. The bid data gathering and its analysis gives essential insights about consumer behavior and enables adequate prediction of demand. To my mind, it is fairer to set prices based on real demand than let them remain to be market-driven. In terms of dynamic pricing, if factors that shape the final price are reasonable, the additional fare would be fully justified.
However, I share the concerns of people who were excessively charged by service providers. I also received the Uber bill one time that charged me four times higher than usual because it was Saturday evening. To my mind, such an increase in price could be at least deemed unfair. I feel an intense dislike for personalized pricing because this approach discriminates customers. Usage of personal information is not an issue for me here, whereas increasing prices only because somebody is wealthy is not rational. Understandably, someone should pay more to get tickets a few hours before dispatch because it is about high demand and value. Nevertheless, I do not see why others should spend more money just because they have particular hobbies and purchase history. People hate to realize that they paid more for the goods that others purchased for less price. In general, I like dynamic pricing because it comes from real demand and valid customer information, but at the same time, I hate the idea of personalized pricing.
To conclude, user data provides businesses with essential insights on consumer behavior and willingness to pay. Dynamic pricing helps them to optimize revenue, increase profits by automatic price adjustment following market fluctuations. Customers also enjoy goods and services based on their preferences, tastes, and other personal factors. However, the personalized pricing approach is unethical because it just exploits an individuals willingness to pay. Thus, its transparency should be improved and further regulated if the industry wants to apply it. The sharing economy and dynamic pricing must be revised in the post-COVID-19 period as it regains its opportunity to shape the world economy and peoples lives.
Reference
Altexsoft (2019) Dynamic pricing explained: Machine learning in revenue management and pricing optimization, 24 April.
Eckhardt, G. M. et al. (2019) Marketing in the sharing economy, Journal of Marketing, 83(5), pp. 5-27.
Edelman, G. (2020) Why dont we just ban targeted advertising?, Wired.
Gaille, B. (2018) 13 dynamic pricing advantages and disadvantages, BrandonGaille.
Gibbs, C. et al. (2018) Use of dynamic pricing strategies by Airbnb hosts, International Journal of Contemporary Hospitality Management, 30(1), pp. 2-20.
Jovin, C. I., McMurtrey, M. E. and Griffin, K. (2018) Dynamic ticket pricing: Model and application, Journal of Marketing Development and Competitiveness, 12(1), pp. 64-69.
Le, T. M. and Liaw, S. Y. (2017) Effects of pros and cons of applying big data analytics to consumers responses in an e-commerce context, Sustainability, 9(5), pp. 1-19.
Levchin, M. (2013) [Tumblr] Web.
Priester, A., Robbert, T. and Roth, S. (2020) A special price just for you: effects of personalized dynamic pricing on consumer fairness perceptions, Journal of Revenue and Pricing Management, 19, pp. 99-112.
Puschmann, T. and Alt, R. (2016) Sharing economy, Business & Information Systems Engineering, 58(1), pp. 93-99.
Thomann, Z. (2020) Shifting U.S. consumer behavior in the face of COVID-19, Digital Commerce 360, Web.
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