The current touristic industry is a customer-centric one and based on the available customer data or even knowledge. Daily millions of people travel all around the world, so that makes customer relationship management an important topic for every touristic organization and for hotels in this case in particular. For a hotel it is important to maintain a customer-supplier relationship by analyzing the available customer knowledge and their current guests’ profiles.
One instrument that helps the hotel industry to create a better understanding of customers’ profiles and turning them into personalized offers and/or seasonal campaigns is data mining. In this way the customer is the ‘’key success factor’’ of the hotel industry. Data mining is about finding patterns and connections within a huge database, an example for the hotel industry is a connection between the booking history and the future purchases. (Akçetin et al., 2016)
There are different so called learning types within data mining as a whole topic. The first type is association learning; this means that the particular hotel discovers a relationship between two or more characteristics of the tourists’ behaviour. An example is that a data mining technique determines a connection between guests who have an interest in shopping also want to stay in a hotel with a city centre location.
Another type is classification learning, this is a way of segmenting your entire database in smaller and more specific target groups. One characteristic to distinguish the diverse target groups of each other in the hotel industry is for example the booking history. Based on that one segment are the so called ‘’big spenders’’ who book a luxury hotel room and book many other extra services during their stay and another segment are the ‘’low spenders’’ who stay in a basic room and book no extra services. By using these types of data mining it becomes possible to adjust the way of approaching your customers and offer them special offers based on their personal customer knowledge. (Bose, 2009)
Perhaps one of the most famous examples of applying data mining within your hotels’ website is the sentence: “people who booked/viewed this product also bought this/viewed this” followed with a list consisting of a number of recommendations that the customers may also like. This items are always adjusted to the customers’ personal wishes. Hotels can also keep track of special requests from their guests within their databases. If for example a customer always asks for an extra pillow or a particular drink in their minibar you can add these requests to the booking screen by applying cross-selling.
Customers will be happy surprised with the personalized booking options and will appreciate that the hotel remembers their special requests. Customers nowadays expect this personalized approach from especially hotels. They don’t want to repeat their personal wishes over and over again but they expect that companies already know this information by using data mining.
Akçetin, E., Kiliç, A., Yurtay, N., Yurtay, O.Y., Öztürk, E., & Şahin, O.A. (2016). DATA MINING: USAGE AND APPLICATIONS IN TOURISM INDUSTRY. Retrieved from https://www.academia.edu/9070001/DATA_MINING_USAGE_AND_APPLICATIONS_IN_TOURISM_INDUSTRY
Beltman, R., & Peelen, E. (2013). Customer Relationship Management. (2nd Ed.). Edinburgh, United Kingdom: Pearson Education Limited.
Bose, I. (2009). Data Mining in Tourism. Retrieved from http://www.irma-international.org/viewtitle/13687/