Fashion Marketing of the Brand Sovereign in the UK Essay

Fashion Marketing of the Brand Sovereign in the UK - Essay Example The dip in revenue can be directly associated with the fall in consumer spending as the prolonged recessionary phase of the UK economy triggered a shortage of jobs, non-availability of consumer credit and decline in disposable income of the consumers (ibisworld.co.uk, 2013). A trend that was noticed in the UK apparel market in the year 2012 is that a large majority of the consumers are opting for either economy apparel or premium apparel. The focus of the consumers was mostly on buying apparels that can be worn on multiple occasions. However, during this period of slow domestic growth, the UK apparel market witnessed an increase in product demand from foreign buyers, who were looking forward to capitalizing on a weak currency. Also, as many retailers set up their online presence, the sales through online stores and platforms helped in boosting of sales on a temporary basis. As of the year 2013, the total valuation of the UK apparel market stood at around 3.2 billion pounds. Now, curr ently, with positive developments happening in most of the global economies, the levels of disposable income of the consumers are expected to rise again. This will again trigger a period of strong growth for the UK apparel market, thereby putting it back on the growing revenue trajectory. It is being currently forecasted by market experts that by the year 2018, the UK apparel market will have a cumulative annual growth rate (CAGR) of around 0.7%.

Data Analysis (Applied Research Method) Essay Example | Topics and Well Written Essays - 1250 words

Data Analysis (Applied Research Method) - Essay Example Household public transport miles per week -0.202 -0.074 -0.085 -0.404* -0.176 0.558** Total leisure miles per household per year 0.584** 0.451* 0.424* 0.398* 0.397* -0.05 -0.161 Total household gas and electric bills per annum 0.498** 0.379 0.491** 0.313 0.544** 0.05 0.003 0.153 **. Correlation is significant at the 0.01 level (2-tailed), *.Correlation is significant at the 0.05 level (2-tailed). C2: Number of Negative Correlations Twelve out of 36 independent correlations are observed to be negative correlations. In which number of public transport users in household negatively correlates with total city CO2 emissions per household per annum, number of household members, average household income per annum, number of cars per household and household car miles per year i.e. r = -0.16, -0.12, -0.188, -0.443, -0.235 respectively. Similarly we observe that there re some more negative correlations like Household public transport miles per week verses total city CO2 emissions per household per annum, number of household members, average household income per annum, number of cars per household and household car miles per year i.e. r = -0.202, -0.074, -0.085, -0.404 and -0.176 respectively. Finally we observe that total leisure miles per household per year also negatively correlates with the variables number of public transport users in household and household public transport miles per week i.e. calculated as r = -0.0 5 and -0.161 respectively. C3: r = 0.889 is the most strongly correlated correlation value which has measured by household car miles per year verses total suburban domestic CO2 kg emissions per household per annum. C4: Correlation is significant at the 0.01 level (2-tailed). A correlation coefficient of r = 0.889 indicates a very good...r = -0.202, -0.074, -0.085, -0.404 and -0.176 respectively. Finally we observe that total leisure miles per household per year also negatively correlates with the variables number of public transport users in household and household public transport miles per week i.e. calculated as r = -0.05 and -0.161 respectively. C4: Correlation is significant at the 0.01 level (2-tailed). A correlation coefficient of r = 0.889 indicates a very good linear relationship between household car miles per year and total suburban domestic CO2 kg emissions per household per annum. Since r2 = 0.7903, we can say that about 79% of the variation in the household car miles per year is accounted for by a linear relationship with total suburban domestic CO2 kg emissions per household per annum. C5: r = -0.16 is the least strongly correlated correlation coefficient value which has measured by number of public transport users in household verses total suburban domestic CO2 kg emissions per household per annum. C6: Correlation is significant at the 0.05 level (2-tailed). A correlation coefficient of r = -0.16 indicates a strongly weak linear relationship between number of public transport users in household and total suburban domestic CO2 kg emissions per household per annum. Bivariate