Data Analytics: A Business Case Study Executive SummaryRecently, eCommerce captured the attention of whole world. Online shopping is oneof the main part of eCommerce. As the eCommerce business increased exponentially it bringsnew challenges to the service provider. Business competition and customer satisfaction arethe important challenges for service provider. Service provider used the different tools, techniques and strategies to attract thecustomers. Business is all about the attraction, quality and service provided by the serviceprovider. We have data of 1180 Cloths (Jacket, Jeans and Suit). We considered the followingattributes / variables as Product Name, Product Price (in $), Sale Price (in $), Profit (in $),Number of customers who bought the product, Shipping Type (Free or Paid), Customer Type(New or Existing), Region (QLD, WA, VIC, TAS, SA), Product Material (Wool and Cotton)and Product Colour (Black, Blue, Pink, Red and White).We observed that company gaining about 7.95% profit overall. We can observed thatthere is no comparative difference in the different attributes. In the region, WA region givingthe most profit percentage as 8.23% and QLD region generate 7.75% lowest among the all-region. We observed that averagely there is 11.81 customer for each products with standarddeviation 3.82.We observed that only shipping type and material have significant association at 5%level of significance and customer type and material have significant association at 10% levelof significance whereas all other pairs are not associated. Average new customers are morethan the existing customers. Mean number of customers for the products which are shippedfreely is significantly more than products which has paid shipping. We can say that wool1
material products are more preferred than cotton as the number of customers for woolmaterial products are significantly more than cotton material product. We conclude that thereis significant difference between mean numbers of customers in different region and there isno significant differences between mean numbers of customers according to colour. We cansee that QLD has most number of customer compared to the other region. From the correlation analysis, we can say that product price and number of customerare positively related with each other. Number of customers is negatively correlated withprofit and product price. Regression analysis suggest that there is significant relation betweentotal profit and number of customers. We also observed R2 as 0.74 which suggest that fittingis good. Slope of number of customers suggest that every customer gives on an average$2.3592 profit to the company. We have also given recommendation from the analysis andplan for it.2
Table of ContentsSr. No.TopicPage No.1List of Abbreviations and assumptions made42Introduction – What is the problem?53Research Methodology64Analytical Findings75Recommendations to the company146An implementation plan based on the recommendations you have provided147Conclusion158List of References169Appendix183
List of Abbreviations and assumptions made Max: MaximumMin: MinimumNSW: New South WalesQLD: QueenslandSA: South AustraliaTAS: TasmaniaVIC: Victoria WA: Western Australia4
End of preview
Want to access all the pages? Upload your documents or become a member.