FEATURED ARTICLE | December 5, 2017
Tourism as an industry is of particular importance for the economy of a country. Millions of people travel around the globe throughout the year. They travel on business, vacations, sightseeing, or other reasons. Travellers spend a good amount of money on purchase of tickets, accommodations, food, transportation, and entertainment. These expenditures are a significant source of foreign exchange including direct and indirect employment.
Web 2 facilitates novel ways of meeting dynamic consumer behaviour, leading to an “information product” of the overall tourism value chain. Tourism value chain generates a lot of information. One type of information flow is from the service providers to consumers or tourists. It provides information about tickets, hotel rooms, entertainment, etc. The second type of information flow consists of aggregate information about tourists to service providers.
Tourism service is data intensive with complex relationships. Organizations and firms across the globe communicate with tourists through various channels to market their products and build customer relationships creating data collections. Data provides a good insight on the traveller’s behaviour. Data here refers to searching, selection, and consumption of merchandises and services for the satisfaction of their needs. The tourism firms’ DNA is built on such datasets to forecast customer needs and respond to their demands. The need for accurate tourism forecasting is thus particularly important due to the industry’s significant contribution to the economy. A key challenge in many tourism destinations is the accurate forecasting of inbound tourism to support destination management decisions. Forecasting has the potential to deliver new and more highly informed inferences about consumers that will give the tourism industry a big boost.
Due to technological development, firms in this business have started moving towards marketing automation tools, coupled with machine learning capability for data collection, data analysis leading to decisions. Data is rich with structured and unstructured data types. Identification of important variables and relationships located in these consumers-information collection leads to better insights. Better decision making and service improvement needs are supported by the insights and knowledge discovered from the service data obtained from the visitors through their interactions. The complex travel data calls for use of state of the art machine learning methods, tools, and techniques beyond simple statistical analysis.
Since the travel destinations are unlimited, travellers seek advice about destinations through a travel agent. The recommendations from agents are restricted by various factors. Sometimes the final decision is dependent on the travel agent. This bottle neck is addressed with machine learning systems like recommender systems, coupled with Artificial Intelligence (AI). Recommender systems are software components to support on available options better suited for a certain individual customer. In a tourism domain, recommendation techniques with respect to tourism will provide suggestions on cities to go to, places to visit, attractions to see, events to participate in, options for hotels, etc. A recommender system is designed to assist in matching the customer’s preferences with the available options and services specific to tourism. It simulates the role of a travel agent in offering options to the customer. The system shows relevant tourist options and enables interactions between the travel agent and the customer through a private Web chat. Messages are exchanged online using computers connected to the Internet. The software uses machine learning techniques to discover insights. After that, the system searches a database of options built from structured and unstructured text and retrieves tourist options. Of late chatbots and voicebots have started influencing tourism industry providing personalized attention to the tourist right through the travel. Such travelbot assistants are also called as virtual assistants as they go beyond guiding tourists to destinations, recommending food en-route, and assisting during travel delays.
Demand for AI and machine learning specialists in the country are expected to see a 60 per cent rise by 2018 due to increasing adoption of automation. However, the sector is so unpredictable that even a small disturbance in the environment can decrease the tourism potential significantly.
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