Analytics for Managers: Analysis Report

ANALYTICS FOR MANAGERS
ASSESSMENT 2 : ANALYSIS
REPORT

TABLE OF CONTENTS
TABLE OF CONTENTS................................................................................................................2
1.Introduction...................................................................................................................................1
1.1 Supermarket Dataset..............................................................................................................2
2. Three problems in the retail sales datasets..................................................................................4
2.1 Sales Analysis and Forecast...................................................................................................4
2.2 Insights of customer engagement in various industries.........................................................5
2.3 Retail Sales Classification.....................................................................................................6
3. Data driven techniques for resolving three problems of retail sales data....................................7
3.1 Regression Analysis for solving sales analysis and forecasting............................................7
3.2 Cluster Analysis for solving customer engagement in retail industry...................................7
3.3 Sales Trend Analysis and the Visual Analysis......................................................................8
4. Justification and Recommendations............................................................................................8
4.1 Implementation of the Regression Analysis..........................................................................8
4.2 Implementation of the Cluster Analysis..............................................................................10
4.3 Implementation of the Sales trend analysis and the Visual analysis...................................11
Conclusion.....................................................................................................................................13
REFERENCES..............................................................................................................................15

1.Introduction
In each area of management significant amount of the data are gathered and generated and
used in different forms for taking decisions and giving insights to the users. Intrinsic business
analysis could be defined as the tools, competencies, methods for the iterative analysis. Also,
reviews previous results of the industry for obtaining higher insights and improving the strategies
and procedures. It helps the management to decide about the database that are relevant and
important for decisions and how they could be manipulate for resolving the challenges and
improving competitiveness, performance and the sales. Efficient and effective business analytics
is dependent over the reliability of information and data, market and technical understanding of
the experts and analysts and the organisational commitments using analytics for deriving insights
for facilitating the strategic decisions.
According to Edwards & et.al., (2017) where market objective of analysis is researched and
identified, analysis methodology is framed and the information is compiled for validating
research. Management of data also includes processing, downloading and the update to new and
regular services inclusive of the centralised database. Thus computational approach varies from
the realistic mathematical tables to the sophisticated analysis as well as applications of predictive
simulation. Business analytics is therefore said to be prescriptive method where data could be
gathered, different patterns recognised and the models designed for describing the recent events,
forecasting future events and the also suggest steps for maximising outcomes.
Present report illustrates the potential data driven methods to selected data for addressing the
associated with the decision making process of the managers that helps the managers and
company executives in grasping the industry environment, predicting demand trends and
mitigating risks. Understanding the industry trends are very essential for the companies before
taking strategic decisions and planning for the future demand and productions. For effective
decision making regarding the sector in supermarket industry that are performing well and those
showing negative trend (Robertson & et.al., (2017). Report will provide datasets used in the
report, 3 data problems that are uncovered from dataset and methodology employed for
processing and extracting required insights from data with justification and evaluation of the
techniques used and the recommendations.
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1.1 Supermarket Dataset
The report uses information of actual retail sales values by the quarter by industry, actual
sales by geographical region and seasonally adjusted retail sales value by the industry. For
research and study sales of retail market industry is taken. Data is of three quarters for three
years of retail sales in different industries. Data consists of all the retail goods that are sold in the
country of various industries such as groceries, electronic goods, food and beverage, recreational
goods, clothing footwear, personal accessories, accommodation and many more. Data will help
the managers for analysing sales forecasts, customer engagement insights as well as trending
consumer goods for improving sales challenges by using business analytics. Below table shows
the information about actual sales in retail industry of 3 quarters.
Present report will be illustrating how data driven techniques could be applied in the business
analytics using software like Microsoft excel which is spreadsheet. It is used for making
statistical graphs and computation for implicating the results before final recommendations and
conclusion.
2. Three problems in the retail sales datasets
Advancement of the business analytics allows retailers for drawing large volumes of the
consumer data that helps them in developing customised goods and contributing to maintain
customer loyalty by data mining. Section provides different sale problems in retail industry sales.
2.1 Sales Analysis and Forecast
Sales forecasts and analysis is method used for making predictions for potential demands
where the reliable predictions enable the companies in making smarter decisions of the business
and forecasting performance over short term and long term. It enables the managers to identify
how companies could handle the workforce, cash flows and finances. It also allows retailers in
minimising impact of measures like cutting expenses and reorienting market activities by taking
advanced steps.
Challenging part in present database is of analysing customer sales in retail market in
specific industry type, seasonal sales and sales by geographical region as all data is randomly
distributed locating the particulars in spreadsheet from the data providers take considerable time,
translating information and storing all data in spreadsheet. With all buzz providing the precise
measurements as well as interpretations without making expensive errors or mistakes are
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