Analysing Bedrock UrbanVega Furniture Product Segment with Data Analytics
Added on 2021-05-31
12 Pages1901 Words168 Views
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Data analyticsName of the StudentName of the University
Executive SummaryThe study is to analyse the furniture product segment of Bedrock UrbanVega with the power of dataanalytics. This has been primarily done to meet the customer sentiment issue and retail spendingacross Australia for the annualised growth of the industry as a whole. Also, with use of Rapid Minertools like decision tree and KNN specification had made the analysis of the results simple andstrategic such that recommendations could be developed for strengthening the business dcsions tobe executed in the long run.
Table of Contents1.0Introduction – What is the problem?..............................................................................................42.0Research Methodology....................................................................................................................43.0 Analytical Findings...........................................................................................................................54.0Recommendations to the company...............................................................................................105.0 An implementation plan based on the recommendations you have provided.............................106.0 Conclusions....................................................................................................................................11List of References................................................................................................................................12Appendix.............................................................................................................................................12
1.0 Introduction – What is the problem? The “Bedrock UrbanVega” has been into the furniture business in Australia for a considerableamount of time. However, due to continuous competition in the industry in Australia, the companyis not able to meet up the consistent change in customer expectations. Although, the process hasbeen streamlined and as per the capability and skills household items data is collected over a periodof time. In household items, furniture retailing has been a challenging market. As stated, customersentiment is an issue and retail spending across the domestic has not only adversely affected thetrading conditions but also the annualised growth of the industry as a whole (Ibisworld.com.au2017).The revenue for furniture retailing industry is projected to fall by 2%, and this primarily affects thediscretionary income. Industry profitability posts varied results as the profits margins are hinderedby softer retail economy and with products and sales it forecasts that the trading conditions can beimproved if the growing volatility is met (PRWeb 2018). Also, retail spending will be affected byprojected volatility in consumer sentiment through continued competition that are affected by risinginterest rates from the external players.2.0 Research Methodology As a data scientist, there are eight categories that are defined in the product segment across 1000 records for “Bedrock UrbanVega”. The attributes for the furniture product segment can be given with the nature of the attribute.product name – Furniture (categorical)product price – Ranging from $125 - $800 approximately (numerical)shipping type -Free or customer paid (categorical)monthly sales ($) – Ranging from $1500 - $26000 approximately (numerical)geographic region – all the regions of Australia (categorical)No. Of customers who bought the product (numerical)Customer type - New or existing (categorical)Primarily these variables are taken because this can be studies across the classification techniqueundertaken for the study.
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