Data Analytics Report: Analyzing Furniture Sales for Bedrock UrbanVega

This report presents a data analytics study focused on the furniture product segment of Bedrock UrbanVega in Australia. The analysis aims to address customer sentiment issues and retail spending trends within the industry, leveraging data analytics techniques to provide strategic recommendations for business improvement. The study utilizes RapidMiner tools, including decision trees and KNN classification, to analyze a dataset of furniture sales across various regions. Key variables such as product name, price, shipping type, monthly sales, geographic region, number of customers, and customer type were considered. The report details the methodology, analytical findings, and proposes actionable recommendations to enhance profitability, improve customer engagement, and optimize sales strategies. The implementation plan suggests targeted approaches for different customer segments and geographic regions. The analysis concludes with insights on market share improvement and the validation of the results using data mining tools.

Data analytics
Name of the Student
Name of the University

Executive Summary
The study is to analyse the furniture product segment of Bedrock UrbanVega with the power of data
analytics. This has been primarily done to meet the customer sentiment issue and retail spending
across Australia for the annualised growth of the industry as a whole. Also, with use of Rapid Miner
tools like decision tree and KNN specification had made the analysis of the results simple and
strategic such that recommendations could be developed for strengthening the business dcsions to
be executed in the long run.

Table of Contents
1.0 Introduction – What is the problem?..............................................................................................4
2.0 Research Methodology....................................................................................................................4
3.0 Analytical Findings...........................................................................................................................5
4.0 Recommendations to the company...............................................................................................10
5.0 An implementation plan based on the recommendations you have provided.............................10
6.0 Conclusions....................................................................................................................................11
List of References................................................................................................................................12
Appendix.............................................................................................................................................12

1.0 Introduction – What is the problem?
The “Bedrock UrbanVega” has been into the furniture business in Australia for a considerable
amount of time. However, due to continuous competition in the industry in Australia, the company
is not able to meet up the consistent change in customer expectations. Although, the process has
been streamlined and as per the capability and skills household items data is collected over a period
of time. In household items, furniture retailing has been a challenging market. As stated, customer
sentiment is an issue and retail spending across the domestic has not only adversely affected the
trading conditions but also the annualised growth of the industry as a whole (Ibisworld.com.au
2017).
The revenue for furniture retailing industry is projected to fall by 2%, and this primarily affects the
discretionary income. Industry profitability posts varied results as the profits margins are hindered
by softer retail economy and with products and sales it forecasts that the trading conditions can be
improved if the growing volatility is met (PRWeb 2018). Also, retail spending will be affected by
projected volatility in consumer sentiment through continued competition that are affected by rising
interest 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 technique
undertaken for the study.

Data Source: The data sources of the variables are defined through the market undertaken across
the eight attributes. The entries made in the records are over a period of 2-3 months. All the
attributes are cleared and those entries are only taken that do not have any missing variables for a
category. On the whole, the company has maintained its records to analyse the performance trend
of the industry as well.
Methods Used: Rapid Miner Studio, one of the efficient software for data mining classifications is
opted so that monthly sales for the geographic region could b predicted. The specific classifications
that constitute for the comparative analysis used is decision tree and KNN (K-Nearest Neighbours)
classification. These algorithms are used for simplest classification and regression problems. Also,
optimal structure obtained is to examine the small changes in the data.
2.1 Sample Data
3.0 Analytical Findings
3.1 K-NN classification
Process
The company “Bedrock UrbanVega” is interested knowing its profitability and whether it is able to
meet up the changing demand and competition. As a result, for this the detailed information has
helped in calculating the credit card scores and furniture products classification so that a decision
could be taken whether a discount based on geographic area and the sales generated by the sales

person is constituting for profitable results in long run or not. In response to this the K-NN
classification has been cross validated to retrieve information from the applied model to analyse the
performance of the company. The process can be further illustrated in the images below.
Output:
The KNN classification deals with eight categories across different variables for examining the
company’s profitability for the sales data (Chauhan and Gautam 2015). This model has been used
because it is not only simple but even converges to the correct decision surface as 1000 records of
data had been used. The following class is for 1- Nearest Neighbour model across two values that are

“True and False” in response to the model used. The prediction value of the model states that the
results for the model is 95% accurate which further elaborates on the performance to rise based on
the given attributes.
The graph below highlights the changes in performance of the company for the given model. H
upward line graph depicts that Bedrock UrbanVega with continuous performance leads to increase
in sales in the coming months. However, the performance vector given the contingency table of true
and false and the results that with certain improvement in the system, the company will be able to

yields the results of changes in customer demands and will help in establishing better trading
conditions across the states of Australia.
3.2 Decision Tree
Process:
The decision tree model known b classical model uses “splitting rules” for the organized tree
structure across the eight variables in the data (Rangra and Bansal 2014). The results of the test are
assigned by the predictions values is based on geometric illustration. The conversion from binomial

to numerical is to illustrate the outcomes and the chances of occurrence of the sales data and its
predictability.
Output:
The results of decision tree is analysed on the 7 types of furniture undertaken for analysis.
To start with the discount option is analysed based on the “True or False” classification as suggested
in K-NN model. The furniture products like chairs, console tables, and secretary desks, tables and
writing desks leads to “False” results that discount is not applied on these items. Moreover, if
customer types are new customers then discounts will not be given as they cannot be trusted after
first buy whereas the same will be considered for existing customers as it is assumed that they will
buy more than one products. The New customer types are further divided in different areas of
Australia (geographic region) that are NSW, Queensland, Victoria, South Australia, Tasmania and
Western Australia. However, when discounts for the sales are undertaken on performance scale
then only New South Wales is seen to be a profitable region to increase the trade with new
customers. Further, NSW has been classified on the monthly sales only when the sales are greater
than $22,372. The decision tree below illustrates the results.

4.0 Recommendations to the company
The possible recommendations that can be made to enhance the profitability and performance of
the company to measure its sales can be given as:
 Firstly, the results need to be in favour for new customers and to expand in different
geographic regions so initially a discount plan can be started.
 Secondly, the salesman getting the maximum product price, in this Jhon should be given
those geographical regions where sales of the furniture products are less as he is a sales
person with good communicative skills.
 Thirdly, except NSW, other areas with products of Sofas and game tables should be
promoted.
 Fourthly, a common discount is needed to establish a large customer with change in
customer demands.
5.0 An implementation plan based on the recommendations you
have provided
The implementation needs to be based on the recommendations made to enhance the performance
of Bedrock UrbanVega.

Salesperso
n
Customer
Type
Geographical regions Discount Type Targeted Time
Period
Jhon and
Maria
New Tasmania, Western Australia, South
Australia, Queensland Victoria
More than 1.5% 3-4 months
Existing All areas Less than 2.4% 30 days
The table above depicts the corresponding areas, discounts that can further enhance the
performance of the company. However, a word of mouth/ advertisement will further enhance the
scope of getting new clients and retaining them for a longer time. The competition will be closer to
the existing competitors in the market and it will update on enlarging the customer base (Myrodia,
Kristjansdottir and Hvam 2017).
However, with further incidence of sales person as per the previous months data ensures greater
revenue from the untouched areas of Australia. On the contrary, with more diversification of
products and styles can initiate and attract customers on their purchasing decisions.
6.0 Conclusions
To conclude, it can be stated that growth of Bedrock UrbanVega will aid the industry growth through
the initiation of recommendations at an early stage. The analysis done on the 1000 records of the
previous monthly sales and discounts offered to the customers resulted to be better for existing
customers but not for new customers for different areas in Australia. However, the results were 95%
accurate but a further affirmation of certain areas had led to static sales. The market share is bound
to improve if the potential operation of the company sales is carried out for a certain period of time.
In addition, with reference to data mining tools, the examination of the scenario was simple to
interpret and validates the results later.

List of References
Chauhan, N. and Gautam, N., 2015. Parametric comparison of data mining tools. international
journal of advanced technology in engineering and science, 3.
Ibisworld.com.au. (2017). Furniture Retailing – Australia Industry Research Reports | IBISWorld.
[online] Available at: https://www.ibisworld.com.au/industry-trends/market-research-reports/retail-
trade/other-store-based-retailing/furniture-retailing.html [Accessed 21 May 2018].
Myrodia, A., Kristjansdottir, K. and Hvam, L., 2017. Impact of product configuration systems on
product profitability and costing accuracy. Computers in Industry, 88, pp.12-18.
PRWeb. (2018). Furniture Retailing in Australia Industry Market Research Report Now Updated by
IBISWorld. [online] Available at: http://www.prweb.com/releases/2013/10/prweb11273968.htm
[Accessed 21 May 2018].
Rangra, K. and Bansal, K.L., 2014. Comparative study of data mining tools. International journal of
advanced research in computer science and software engineering, 4(6).