Marketing analysis and forcasting Coursework Example | Topics and Well Written Essays - 1500 words

Marketing analysis and forcasting - Coursework ExampleThe data available is of Hughes Travel PLC monthly travel data collected over the brace of January 1986 December 2010. It consists of both variables, namely build of overseas visitors travelling to the UK and the number of UK residents travelling overseas. The time interval of data collection for both the variables is a month. As both the variable data is independent of each other, hence, we have two univariate time series. The data does not depict a particular trend. Analysis of UK Residents clock time Series Figure 1 of appendix A shows the month wise distribution of UK residents travelling abroad. The graph shows that highest number of UK residents travel abroad during the months of terrific, September, and July. The graph shows that August has had highest number of UK residents travel abroad and it has happened consistently for the past 25 eld. Similarly, figure 2 of appendix A shows the cumulative data on UK residents travelling abroad on a yearly basis. The data shows a steady rise in the number of UK residents travelling abroad with the highest being year 2010. Figure 1 in appendix A also depicts that UK residents travel least during the months of December, January, February. ... Figure 4 of the appendix shows that the rate of Overseas UK travels has risen considerably from the previous years and it was the highest in 2010 from the past 25 years whereas the years 2007-2009 saw the lowest travelling statistics. This probably has to do with the recession and the credit crunch during these years. Figure 5 displays the overseas travelling statistics distributed over the 25 years. The graph clearly shows that not once in the period of 25 years, the months of July and August have never seen a decline in the number of overseas travellers as compared to other months. Moreover, the graph also shows that as the years 2007-2009 were an all time low for overseas travellers, the number of travellers decline d to their lowest during the July-August of these years as well. Forecasting Methods Several forecasting methods have been substantial over the years and each of them have their advantages and accuracy. We have chosen the two most basic and common forecasting models exponential function smoothing model, and ARIMA Model. Exponential Smoothing This method is most common forecasting method for different types of time series data. It was developed by Brown and Holt. A basic approach towards time series framework is to look at each observation as the combination of a constant and an error term. The value of constant would vary with time but is constant in a short interval of time. One way of modelling this is to assign greater weight to the most recent values of the constant as compared to the older observations also termed as moving averages, which is the basis of simple exponential smoothing. Following is the formula of simple exponential