Skyscanner: Your digital traveling buddy

Skyscanner is a search-website for traveling. You can easily compare flight tickets from almost all flight companies on price, traveling time and location. Their mobile app is downloaded over 40 million times and it has over 50 million returning customers who use Skyscanner every month. It is because of the quality of the recommendation system (RS) that they have perceived a customer satisfaction rate of 8.2 (trustpilot). In this blog you will learn what the three key success components are of Skyscanners’ RS.

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Figure 1: Defaults in the input list lowers user effort.

No need to fill out a long list of preferences
In general users are lazy and don’t like to put much effort in searching. That is why they came to skyscanner right? Skyscanner uses three types of defaults in the input list to lower customer effort (Figure 1).
Firstly, Skyscanner uses mass defaults; most customers buy return tickets for two adults, so they suggest you are looking for the same.
Secondly, Skyscanner uses your visiting history; if you have considered tickets to Madrid two days ago, it is plausible that you are still interested in them.
Lastly, Skyscanner uses more implicit data such as your IP address and date to safe you effort. Your IP address gives information about your location, so Skyscanner assumes that you are looking for a ticket from your current location. Is this all still too much effort? Use the “Price Alert” button to receive price updates in your mailbox.
Smart defaults in the input menu of Skyscanner safe effort and time.

Skyscanner does the searching work for you
Skyscanner has implemented three different smart features, which increase the perceived effort of the RS (Figure 2).
To begin, skyscanner has implemented two loading bar effects. One loading circle directly after the user clicks on “search flights” and one loading bar on top of searching results. The loading effects give the customer the idea that Skyscanner is searching the Internet for the best tickets.
In addition, Skyscanner shows which websites it is scanning for tickets. After you click on “search flights” it shows a long list of all websites it will scan for you. Moreover, it shows you which website it is scanning at the moment in the loading bar on top of the results.
Last, skyscanner gives information about the accuracy of the data shown. It shows how many minutes ago the results were updated. This convinces the user of the quality of the results the RS gives.
Because skyscanner shows the user how much effort the RS puts in the searching process in different ways, the perceived quality of the RS is high.

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Figure 2: Smart features increase perceived RS effort and framing eases the decision process.

It is easy to pick the best flight
Skyscanner makes it easy to pick the flight that suits your preferences best.
Skyscanner uses a top-down framing effect; they show the best results on top according to your preferences (Figure 2). If the best result is not on top, you can easily modify your preferences in the column on the left. Have you find your ideal flight? The button “select” forwards you directly to the website where you can buy your ticket immediately. Skyscanner simplifies the decision process by providing the right information in the right order.

In conclusion, the three most important success components of Skyscanners’ RS are; smart defaults in the pre-settings, features which show you how much effort the RS puts in the scanning and right information in the right order eases the decision process. These components of the RS contribute to the perceived quality and thus have a great positive impact on the customer satisfaction rate. Curious how Skyscanner will improve customer satisfaction in the future? Take a look at


Images: Skyscanner

Dellaert, B. G., & Häubl, G. (2012). Searching in choice mode: consumer decision processes in product search with recommendations. Journal of Marketing Research, 49(2), 277-288.

Goldstein, D.G., Johnson, E.J., Herrmann, A., & Heitmann, M. (2008). Nudge your customers toward better choices. Harvard Business Review, 86(12), 99-105.

Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing science, 19(1), 4

Li, S.S., & Karahanna, E. (2015). Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions. Journal of the Association for Information Systems, 16(2), 72-107.

Tsekouras, D. & Li, T. (2015). The Dual Role of Perceived Effort in Personalized Recommendations. European Conference of Information Systems 2015, Paper 187


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