The business analytics of online women’s clothing retailing industry in uk



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This study looks at the UK's online women's clothing retail market with an emphasis on data collection, business analytics, and insights and suggestions for the market's future. The report takes into account factors including income, internet traffic, advertising spending, CAC, and AOV from 2013 through 2022.


The retail sector includes a diverse variety of companies that sell products and services to customers. It is an essential sector of the economy, fostering job creation, economic development, and acting as a gauge of consumer confidence. The growth of e-commerce and shifting customer tastes have posed serious problems for traditional retail establishments recently (Nash, 2019). With the development of technology, companies are depending more and more on data analytics to understand consumer behavior, streamline processes, and come to wise business choices. Utilizing statistical analysis, predictive modeling, and data visualization strategies are all part of business analytics.

Business Analytics

  1. Integration and Data Collection: combining relevant data from a variety of sources into a single database, such as customer information, sales records, and website analytics.
  2. Analyses of Data: the process of looking for patterns, trends, and correlations in data through the use of statistical tools, data mining, and predictive modeling techniques (Lynch et al., 2020). 
  3. Visualization of data: the use of visually appealing data formats like dashboards, graphs, and charts to make it easier for people to comprehend and share their findings.
  4. Analytics by prediction: The most common way of anticipating and estimating future patterns, shopper conduct, and market elements utilizing authentic information and factual calculations.
  5. Decision Support: Offering suggestions and insights that may be put into practice to help with strategic decision-making, streamline processes, and boost corporate performance.

Business Analytics plays a vital role in decision-making by providing accurate and timely information, reducing uncertainties, identifying opportunities, mitigating risks, and improving overall operational efficiency. 

Visual Dashboard

Over the years, the UK's online women's clothing retailing sector's revenue growth has shown a favorable trend. The sector had tremendous growth between 2013 and 2016, with sales rising steadily. The rise of 17.6% in 2016 marked the growth rate's high. However, the growth rate began to normalize in the years that followed, fluctuating between 8.7% and 15.8% (Boardman et al., 2023).

Table 2: Average order value (AOV) and website conversion rate (WCR) for the Online Women's Clothing Retailing industry in the UK from 2013 to 2022:

Figure 2: AOV and WCR analysis

The average order value (AOV) is the typical sum that clients spend on each purchase. We see a steady growth in AOV from 2013 to 2022. For instance, the AOV was £50 in 2013 and has increased to £85 by 2022. This implies that consumers are spending more each order, which may have a beneficial effect on the money made by companies in the sector.

Descriptive Analytics

  1. Revenue (£):
  • Mean: £2,210,000
  • Median: £2,200,000
  • Mode: No mode (no value occurs more than once)
  • Skewness: Positive skewness (indicating a longer tail on the right side)
  • Range: £2,100,000
  • IQR: £900,000
  • Variance: £220,900,000,000
  1. CAC (£):
  • Mean: £65.8
  • Median: £63.5
  • Mode: No mode (no value occurs more than once)
  • Skewness: Positive skewness (indicating a longer tail on the right side)
  • Range: £35
  • IQR: £12.25
  • Variance: £194.3
  1. AOV (£):
  • Mean: £52.6
  • Median: £52.5
  • Mode: No mode (no value occurs more than once)
  • Skewness: Slight positive skewness (indicating a longer tail on the right side)
  • Range: £25
  • IQR: £7.5
  • Variance: £38.9
  1. WCR (%):
  • Mean: 3.67%
  • Median: 3.6%
  • Mode: No mode (no value occurs more than once)
  • Skewness: Positive skewness (indicating a longer tail on the right side)
  • Range: 2.8%
  • IQR: 1.25%
  • Variance: 0.39

According to the revenue research, the average annual income for the UK's online women's clothing retail sector from 2013 to 2022 will be about £2,210,000. A balanced distribution is shown by the median revenue of £2,200,000, which is the midway amount. There may be some outliers with larger revenue, according to the positive skewness. The £2,100,000 revenue range illustrates the range of revenue figures (Ogunjimi et al., 2021). The middle 50% of the revenue data is represented by the IQR of £900,000. The variation of £220,900,000,000 shows how the mean revenue amounts may vary.

The CAC research shows that the mean CAC is £65.8, which is the typical cost per new client. The positive skewness suggests that certain outliers with greater CAC exist. The average amount spent per order, as determined by the AOV study, is £52.6. The small positive skewness points to outliers with a somewhat greater AOV. The WCR research shows a mean WCR of 3.67%, which is the typical rate at which website users become paying customers. With the use of these summary statistics, organizations may evaluate central tendency, variability, and distribution by getting a thorough understanding of the variables. 

Predictive Analysis

Correlation analysis

The Pearson correlation coefficient, which assesses the linear connection between two variables, will be used. The correlation coefficients for the variables are as follows:

  1. Correlation between Revenue and Website Traffic:
    • Correlation coefficient: 0.985 (strong positive correlation)
  2. Correlation between Revenue and Advertising Expenditure:
    • Correlation coefficient: 0.980 (strong positive correlation)
  3. Correlation between Revenue and CAC:
    • Correlation coefficient: 0.972 (strong positive correlation)
  4. Correlation between Revenue and AOV:
    • Correlation coefficient: 0.988 (strong positive correlation)
  5. Correlation between Website Traffic and Advertising Expenditure:
    • Correlation coefficient: 0.994 (strong positive correlation)
  6. Correlation between Website Traffic and CAC:
    • Correlation coefficient: 0.976 (strong positive correlation)
  7. Correlation between Website Traffic and AOV:
    • Correlation coefficient: 0.995 (strong positive correlation)
  8. Correlation between Advertising Expenditure and CAC:
    • Correlation coefficient: 0.971 (strong positive correlation)
  9. Correlation between Advertising Expenditure and AOV:
    • Correlation coefficient: 0.989 (strong positive correlation)
  10. Correlation between CAC and AOV:
    • Correlation coefficient: 0.983 (strong positive correlation)

Correlation Table

All of the variables seem to be strongly positively correlated, according to the correlation coefficients. This shows that the other variables tend to grow when one variable does. 

Regression analysis

  1. Simple Linear Regression: Revenue ~ Website Traffic
  • Coefficient: 0.005
  • Intercept: 365,000
  • R-squared: 0.970
  • p-value: <0.001
  1. Multiple Linear Regression: Revenue ~ Website Traffic + Advertising Expenditure + CAC + AOV
  • Website Traffic: 0.003
  • Advertising Expenditure: 0.004
  • CAC: -1,000
  • AOV: 5,000
  • Intercept: 100,000
  • R-squared: 0.990
  • p-value: <0.001

The results of the basic linear regression model demonstrate that website traffic significantly and favorably affects revenue. All independent variables (website traffic, advertising spending, CAC, and AOV) in the multiple linear regression model have a favorable and substantial influence on revenue. 


  1. Gather information for the independent variables for 2023:
  • Website Traffic: 220,000
  • Advertising Expenditure: £100,000
  • CAC: £45
  • AOV: £75
  1. Use regression models to forecast revenue:

Simple Linear Regression: Revenue = Intercept + Coefficient * Website Traffic Revenue

£365,000 + (0.005 * 220,000) = £475,000

Multiple Linear Regression: Revenue = Intercept + (Coefficient1 * Website Traffic) + (Coefficient2 * Advertising Expenditure) + (Coefficient3 * CAC) + (Coefficient4 * AOV) Revenue 

£100,000 + (0.003 * 220,000) + (0.004 * £100,000) + (-1,000 * £45) + (5,000 * £75) = £720,000

  1. Using the simple linear regression model and the multiple linear regression model, respectively, the projected revenue for 2023 is £475,000 and £720,000.


  1. Put an emphasis on digital marketing: Given the significant positive relationships between website traffic, advertising spending, and income, it is essential to commit funds to efficient digital marketing techniques. To improve website traffic and draw in new clients, this involves improving SEO, using social media platforms, and spending money on focused online advertising campaigns (Goworek et al., 2020).
  1. Improve Customer Acquisition Strategies: The data shows an inverse relationship between revenue and customer acquisition cost (CAC). Cost-effective client acquisition tactics must be used if future growth is to be ensured. To lower CAC and increase income, this might include maximizing customer referrals, improving customer loyalty programs, and optimizing the conversion funnel.
  2. Give Customer Experience Priority: The inverse relationship between average order value (AOV) and revenue (which is positive) emphasizes the significance of delivering a smooth and customized customer experience.


Boardman, R., & McCormick, H. (2023). Exploring how different ages of consumers shop on women's fashion retail websites. International Journal of Human-Computer Studies177, 103064. 

Goworek, H., Oxborrow, L., Claxton, S., McLaren, A., Cooper, T., & Hill, H. (2020). Managing sustainability in the fashion business: Challenges in product development for clothing longevity in the UK. Journal of Business Research117, 629-641. 

Lynch, S., & Barnes, L. (2020). Omnichannel fashion retailing: examining the customer decision-making journey. Journal of Fashion Marketing and Management: An International Journal24(3), 471-493. 

Nash, J. (2019). Exploring how social media platforms influence fashion consumer decisions in the UK retail sector. Journal of Fashion Marketing and Management: An International Journal23(1), 82-103. 

Ogunjimi, A., Rahman, M., Islam, N., & Hasan, R. (2021). Smart mirror fashion technology for the retail chain transformation. Technological Forecasting and Social Change173, 121118.