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"What marketing strategies should luxury hotels in the UK use to counteract the effects of a recession on their business?"
This question is being asked, and the research is being done, to take advantage of the recent recession in the UK, and use it to help provide advice and guidance to luxury hotel brands on how to mitigate the impacts of future recessions. An economic recession implies a shrinking of levels of consumer and business demand (Begg et al, 2003, p. 56). As a result, there will be a reduced demand for services such as hotel rooms, particularly in the luxury market where individuals can easily downgrade to a cheaper hotel option. This implies that luxury hotel operators need to understand how best to market their hotels in order to maintain their market share and the success of their business in the face of a shrinking market and reduced demand. In addition to this, the travel and tourism operators that are affiliated to them need to be able to market these hotels to their customers in order to maintain their affiliation; hence they also have an interest in the marketing strategies of luxury hotels in the UK.
An appropriate qualitative research design that will fully answer the research question will need to be based on a valid and accepted academic framework. This research design will use the '7Ps' extended marketing mix as a conceptual framework around which the qualitative research can be based. The 7Ps are based on the 4Ps of product marketing introduced by McCarthy (1960, p. 43) but supplemented by an additional three factors that Booms and Bitners argued were relevant to service marketing (Kotler and Keller, 2006, p. 89). This creates a model of seven distinct factors: Product, Price, Promotion, Place, People, Processes and Physical Layout that make up a model of the services marketing mix.
This framework will be used to construct a series of qualitative questionnaires which look at how luxury hotels have adjusted their marketing mixes during the recession. These questionnaires will be presented to a wide range of luxury hotels throughout the UK. Once the relevant marketing mixes for the hotels have been determined, they will be compared to industry measures of the success of the hotels, including their industry ratings; the reviews of guests and industry bodies; and the extent to which the hotels have maintained their revenues, numbers of guests and revenue per room over the course of the recession. The questionnaires will also be followed up by a series of semi structured interviews with managers of the hotels which have performed the best during the recent recession. These semi structured interviews will allow the managers to provide more detail on the use of marketing to prepare for the recession; how well the hotel performed during the recession; and the extent to which the 7Ps model guided the marketing decisions and strategy of the hotel.
The main strength of this research design is that it combined both academic and practical research and discussion into a single conceptual framework, and tests that framework in a contemporary context. As a result, this will mean that the results of any research carried out using the design will have both academic and practical relevance. This will help to maximise their reliability and validity (Saunders et al, 2007, p. 130). The design does not have any significant conceptual weaknesses; however it is overly reliant on the most recent UK recession as a source of data. As a result, it is possible that the data will not be generalisable, and hence will not be relevant to hotel managers and tour operators looking to deal with future recession which may be of a different nature. This implies that the results of the research should be compared to other studies that have taken place in previous recessions, to determine if these recessions had different outcomes and implications. The practical aspects of the design are mainly focuses on the collection of data from hotels. As most hotels are usually very busy, it may be difficult to get data from them. This may necessitate a significant investment of time and effort. The data may also not be fully complete or accurate, as hotels may not have time to obtain and verify all the necessary data.
Evaluate the application of multivariate regression analysis for hospitality and tourism demand for forecasting purposes.
The use of multivariate regression analysis for hospitality and tourism demand for forecasting purposes has an advantage over the use of standard regression analysis, in that it can consider multiple potential causal factors. As Frechtling (2001, p. 154) notes, tourism demand is usually influenced by a large number of factors, including both push and pull factors. As such, it is impossible to accurately forecast future hospitality and tourism demand without taking at least some of these factors into account. A multivariate regression analysis will not only show which factors influence the level of demand, but will also demonstrate how significant the impact of each of these factors is, hence better indicating how demand may change as these factors vary in future.
Unfortunately, the consideration of multiple factors does not avoid the potential issue that these factors may not be independent of each other. In other words, there may be significant cross correlation between the different predictive factors, indicating that these factors actually have a causal relationship with each other (Frechtling, 2001, p. 165), If this is the case, then a multivariate regression analysis may indicate that one factor has a strong influence on tourism demand, when that factor is actually being influenced by another factor, which is actually the factor influencing the demand level. This can lead to factors being wrongly believed to be predictors of demand, when in fact they are not, and hence the model being inaccurate. In addition to this, the multivariate model may not provide information on factors which may be lagging indicators, and this could introduce further inaccuracies into the model (Frechtling, 2001, p. 171).
Another critical issue is that multivariate regression analysis relies on all of the factors being used in the analysis having the same relation with the level of demand. In general, this implies that all causal factors must be linearly related to the level of demand, as the multivariate regression model is usually linear in nature. If any of the factors do not have a linear relationship with the level of demand, then the multivariate regression analysis will not produce an accurate representation of the actual behaviour of the demand, and hence the results of the model may also be flawed. This is particularly important in tourism and hospitality, where a great many factors influencing the demand function are likely to be seasonal. As a result, a purely linear multivariate regression analysis would not be able to account for these factors, and thus would not produce valid and useful forecasts. However, it should be noted that multivariate regression models can overcome this, either by including seasonality terms and dummy variables to model the seasonality, or by running different regressions for different seasons. As such, this is not a critical failure in the multivariate regression approach, and multivariate regression arguably handles this issue better than many other types of forecast.
Finally, it is important to note that, for any forecast model, there are issues associated with the extrapolation of past demand to future demand. In particular, the demand function can change suddenly and unexpectedly so that future demand is not influenced by the same factors, and does not follow the same pattern as past demand. Whilst this cannot be modelled quantitatively, it should be included in any model using qualitative inspection of the data, to look for patterns that may indicate a break from previous demand levels.
In conclusion, the use of multivariate regression analysis for hospitality and tourism demand for forecasting purposes is valuable as it allows multiple variables to be considered in a single mathematical model. It also allows forecasters to build models that will take account of seasonality and other non linear variation. However, the multivariate regression analysis method does not cope well with the use of multiple variables with different relationships to the level of demand, particularly if there are non linear variables involved. In addition, as with any forecasting model, care needs to be taken to ensure that extrapolation is possible, and the nature of the demand function will not change sharply without warning.