Advantages of demand forecast for the tourism industry

By Priya Chetty on November 8, 2011

Demand forecast in tourism is of great economic value both for the public and private sector. Any information concerning the future evolution of tourism flows is of great importance to hoteliers, tour operators and other industries concerned with tourism.In the last few decades, numerous researchers have studied international tourism demand and a wide range of the available demand forecasting techniques have been tested. Major focus has been given to econometric studies that involve the use of least squares regression. It is to estimate the quantitative relationship between tourism demand and its determinants. However, econometric models usually fail to outperform simple time series extrapolative models. This article introduces a new approach to tourism demand forecasting by incorporating technical analysis techniques. The proposed model is evaluated versus a range of classic univariate time series methods in terms of forecasting and directional accuracy [1].

Risk assessment in the tourism industry

Despite efforts undertaken since the mid-20th century (Kates, 1971; White, 1942; White, 1973; Quarantelli, 1988), the risk assessment seen from the perspective of disaster has only been treated fairly recently. Its systematic conception and analysis was practically assumed by experts in the natural sciences with studies regarding earthquakes, volcanic eruptions, mudslides, flooding and industrial accidents. In other words, emphasis was centered on the knowledge of hazards due to the existing investigative and academic biases and the efforts of those who first reflected on these issues (Cutter, 1994). It is important to point out here that the emphasis still remains, particularly in the highly developed countries, where due to their technological development people try to find greater detail the generating phenomena of the threats. This was an evident trend during the first years of the `International Decade of Natural Disaster Reduction’ declared by the United Nations (UN) General Assembly.

The tourism industries and those interested in their success in contributing to the social and economic welfare of a citizenry, need to reduce the chances that a decision will fail to achieve desired objectives. One important way to reduce this risk is by discerning certain future events or environments more clearly. One of the most important events is the demand forecast for a tourism product, be it good, a service or a bundle of services such as vacation or what a destination offers.

Need for a risk assessment tool

All industries are interested in such risk reduction. However, this need may be more acute in the tourism industries than for other industries with other products, for the following reasons [2]:

  • The tourism product is perishable. Once an airliner has taken off, or a theme park has closed for the day or morning dawns over a hotel, unsold seats, admissions or sleeping rooms vanish, along with the revenue opportunity associated with them. This puts a premium on shaping demand in the short run and anticipating it in the long run, to avoid both unsold `inventory’ on the one hand and unfulfilled demand on the other.
  • People are inseparable from the production-consumption process. To a large extent, the production of the tourism product takes place at the same time as its consumption. And much of this production-consumption process involves people interacting as suppliers and consumers, such as hotel staff, waiters and waitresses, flight attendants and entertainers. This puts a premium on having enough of the right supply personnel available when and where visitors need them.
  • Customer satisfaction depends on complementary services: The visitor’s experience depends on satisfaction with a host of goods and services offered by a hotel. Demand forecast of a hotel  depends on:
    • the volume of airline flights and other transport access to its area,
    • the quality of airport services,
    • the friendliness of taxi drivers and
    • the quality and cost of entertainment and the availability of recreational opportunities.
  • Demand forecast  can help ensure these complementary services are available when and where future visitors need them, which will rebound to the benefit of the hotel or other individual tourism facility.
  • Leisure tourism demand is extremely sensitive to natural and human-made disasters. Much holiday and vacation travel is stimulated by the desire to seek refuge from the stress of the everyday environment. Moreover, today there are countless alternatives for spending leisure time pleasantly for residents of most developed nations. As a result, crises such as war, terrorist attacks, disease outbreaks, crime and extreme weather conditions can easily dissuade leisure travellers from visiting a destination suffering from one of these, or from travelling at all. The ability to forecast such events and their projected impact in tourism demand can help minimize the adverse effects of catastrophes on the tourism-related sales, income, employment and tax revenue of a place.
  • Tourism supply requires large, long lead-time investments in plant, equipment and infrastructure.  A new hotel may take three to five years from concept to opening. A new airport or ski resort may take a decade or so for all planning, approvals and construction. A new airplane may take five years to may take five years to produce from an airline’s initial order to final delivery. Future demand must be anticipated correctly if suppliers are to avoid the financial costs of excess capacity or the opportunity costs of unfilled demand [3].
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Early warning system

Accuracy of monitoring plays an important role in early warning, not only in a technical sense. It also helps to prevent false alarms and therefore helps to build up trust in the warnings. Monitoring has to adapt continuously to the changing hazard landscape, especially in light of climate and environmental change. Examples such as the evacuation of 40,000 people in the Popocatepetl region, Mexico, in December 2000 just hours before a major eruption are noteworthy examples of successful early warning from which a lot can be learnt[4].

At the Symposium of the EWC III – Third International Conference on Early Warning[5], it was strongly felt that warning systems must be ‘people-centered’. They have to support and empower people in protecting themselves. In order to ‘go the last mile’, an integrated approach to early warning has to be based on the needs, priorities, capacities, and cultures of those at risk. People at risk must be partners in the system, not controlled by it [6].


  1. A technical analysis approach to tourism demand forecast C. Petropoulosa, K. Nikolopoulosb, A. Patelisa and V. Assimakopoulosc.
  2. Advantages of Tourism demand forecasting/Forecasting Tourism demand: Methods and Strategies By Douglas Frechtling.
  3. Advantages of Tourism demand forecasting/Forecasting Tourism demand: Methods and Strategies By Douglas Frechtling; page 5.
  4. Excerpt: Summary of the Scientific and Technical Symposium, Multi-hazard Approaches Session; 17 (EWC III Third International Conference on Early Warning, 27-29 March 2—6 March, Bonn Germany).
  6. Excerpt: Summary of the Scientific and Technical Symposium,  (EWC III Third International Conference on Early Warning, 27-29 March 2—6 March, Bonn Germany).

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

  • Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
  • Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
  • Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).