7045SSL Business analytics and Intelligence Assignment Sample 2023
INTRODUCTION
This report is aimed to analyse the clothing retail market in the UK. In addition, the report would present the descriptive and predictive analytics of the clothing market in the UK and it would present recommendations for the clothing industry, from the perspective of a business analyst.
BACKGROUND INFORMATION
Over the years, UK’s clothing industry has grown and it now has a myriad of retail consumers. Due to technological advancements, most retailers operate using online portals, despite owning conventional brick-mortar stores. The clothing market in the UK is characterised by steady growth (Sabanoglu 2020). The market is expected to set a market value of 66.6 billion Euros, as of 2020 companies such as Next, Primark, New Look and Marks and Spencer’s, dominate the industry. Over the years, the growing trend of online purchase is set to dominate, as evidenced by 71.2% of sales being conducted online (Sabanoglu 2020).
SECTION 1: LITERATURE REVIEW
Numerous studies have pointed out towards the analytics and business intelligence industry growing at a rapid pace (Turban et al. 2008). For instance, Holsapple, Lee-Post and Pakath (2014), pinpointed that analytics and business intelligence comprises a $12.2B market and it is a top technology priority of “Chief Financial Offers”.
Chen, Chiang and Storey (2012), opined that business intelligence and analytics (BI&A) has emerged as an important field in studies, and it has the ability of reflecting upon the impact of data-related problems and its magnitude in solving problems that arise in contemporary business environments. Amongst the myriad of benefits of BI&A, Trkman et al. (2010), conducted a study of its impact on supply-chain management, and they deduced that it can help explain and identify the areas that affect supply-chain performance. Trkman et al. (2010), added that BI&A are approaches, which are used in sync with organizational tools and procedures to analyse information and predict outcomes.
Most charts presented above show the dominance of Bershka UK Limited amongst other players in the industry (Figure 1, Figure 4, Figure 3). Despite magnificent revenues and a holding of assets, Bershka’s profit margins were shy in comparison to other key players such as Marks and Spencer’s. Excessive expenditure attributable to companies such as H&M and Marks and Spencer’s caused their net profit margin to crash (Figure 4). Figure 5 presented above shows that expenditure in the UK retail industry has been rising over the years and this is indicative of increasing activity in the industry. For companies such as Tesco and Marks, the current asset exceeded the current liabilities and it was indicative of liquidity constrains.
SECTION 3: DESCRIPTIVE ANALYTICS
Explanation of Selection
- Revenues has been selected as it is an important aspect of businesses as without appropriate amounts of revenues, companies cannot keep earning profits.
- Intangible assets are important for businesses as it can help them with a competitive advantage and it also aids in decision making skills.
- Profit margins such as gross and net profit margins are helpful as it can show the profits made by businesses and its health.
- Assets based items such as fixed and current assets are chosen as they are integral component to sustain business operations.
Summary statistics such as the mean, median, mode, standard deviation, variance, skewness, kurtosis and the range can help understand the data simply (Fisher and Marshall, 2009). These statistics can provide information about the sample data. For example, Figure 5 shows items such as the mean, median and skewness. It helps in quantitative data analysis by portraying a summary of the collected quantitative data and this is because summary statistics can help present the calculated data in a meaningful way (Bickel and Lehmann, 2012).
Figure 6 shows that liquidity ratios are correlated to turnover and profit margins, whereas, profit margins are correlated to return on capital employed. Similarly, profit before taxation is positively correlated (significant) to profit margin. Return on Capital Employed is negatively correlated to turnover and turnover is negatively correlated to profit before tax.
SECTION 4: PREDICTIVE ANALYTICS
In consideration with the variables chosen, the dependent and the independent variables are as follows.
- Dependent Variables: Revenues
- Independent Variables: Gross Profit and Profit Margin
The predictive model, is as follows.
ln Y t = b1 + b2 ln X2t + b3 ln X3t et
Where,
- X2: Gross Profit
- X3t: Gross Profit Margin
The R-Squared of the model calculated is equivalent to 100% (Figure 9). This means that the model is capable of explaining 100% of the variation in the dependent variables, as caused by the independent variables. Co-efficient of variables such as profit before taxes is positive, whereas, gross profit margin has a negative co-efficient. At the 5% level of significance, the independent variable (Gross Profit and Gross Profit Margin) is rejected as their p-value is less than 0.05, and this means that there is a significant relationship between revenues and gross profit and gross profit margin.
CONCLUSION
The aim of the report had been to analyse companies within the UK clothing retail industry. As seen above, there is an abundance of literature pertinent to business analytics and companies such as Tesco are dominating in terms of volume, whereas, H&M has been leading in terms of gross profit margin. However, a fall in net profit margin shows that expenses were higher for companies. The predictive model presented above shows that revenues is explained by gross profit margin and gross profits. In the near future, inculcation of artificial intelligence in the retail industry can better help improve profitability.
REFERENCES
Bickel, P. J., and Lehmann, E. L. (2012). Descriptive statistics for nonparametric models. III. Dispersion. In Selected works of EL Lehmann (pp. 499-518). Springer, Boston, MA.
Chen, H., Chiang, R. H., and Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
Fisher, M. J., and Marshall, A. P. (2009). Understanding descriptive statistics. Australian Critical Care, 22(2), 93-97.
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141.
Sabanoglu, T. (2020) Topic: Apparel Market In The UK [online] available from <https://www.statista.com/topics/3348/apparel-market-in-the-uk/#:~:text=The%20apparel%20and%20footwear%20market,value%20at%2066.6%20billion%20euros.&text=The%20apparel%20manufacturing%20market%20is,over%20the%20last%20five%20years.> [20 June 2020]
Turban, E., Sharda, R., Aronson, J. E., and King, D. (2008). Business intelligence: A managerial approach (pp. 58-59). Corydon^ eIndiana Indiana: Pearson Prentice Hall.
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