Monthly Turnover Rate Estimates in Australian Retail Industries

This report examines monthly retail turnover rate estimates in Australia, utilizing data from the ABS to analyze market performance across various retail industries including food retailing, supermarkets, liquor, household goods, and electrical goods. The study investigates the relationship between monthly turnover estimates and different retail sectors, employing statistical analysis and graphical representation to identify trends and patterns. Key findings reveal the distribution of turnover estimates, highlighting peak sales periods like December 2017 and variations across industries, with supermarkets and grocery stores generally exhibiting higher turnover rates. The research also explores the impact of economic factors and customer relations on retail sales, emphasizing the role of technology and online forums in promoting retail trade. The overall objective is to provide reliable economic information for decision-making in the retail sector, contributing to a deeper understanding of market dynamics and informing business strategies.

STUDY OF MONTHLY TURNOVER RATE ESTIMATES IN RETAIL INDUSTRIES,
AUSTRALIA
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Abstract
The ABS (Australia Bureau of Statistics) in the past has carried research in the Australian
industry. The purpose of the findings being to investigate the Market performance in Australia.
The figures produced by ABS provide an insight on retail and wholesale trade performance.
Study of Market performance using the retail turnover trade is very important because it offers a
reliable information on the progress of the trade industry in a given country. Retailing is an
important aspect of the business world as it acts as a link between the producers and the final
consumers of the product. Some retail turnover estimates are compiled on monthly basis by the
ABS in Australia. And currently, the total estimate for the turnover is approximately 64%. This
study will explore the market performance in retail trade in Australia and give some
recommendations based on the analyzed data. Moreover, the dissertation will also discuss some
conclusions derived from this document and also offer a deeper analysis of the data using the
appropriate statistical software. Further research is also very crucial in this industry as it will give
alternative and practical ways of carrying the same study in a different set-up and also produce
reliable results.
The scope of this study is restricted to Australian retail trade businesses which comprise
of industry groups and subgroups. The sampling frame is also derived from the study population.
The sampling frame consists of statistical units that are used in this study. Data modeling in this
project plays a big role as it will relate different variables /factors under this study. The estimated
turnover rate is used in updating the past retail turnover reports. On updating the turnover
estimates, economists, therefore, will use this information on strategizing the market demands.

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CHAPTER 1: INTRODUCTION
Certain publications are available on the internet that offers deeper market analysis using
the retail turnover trade. Research shows that most economists and statisticians compile monthly
reports on turnover estimates in different parts of the world. These turnover estimates are very
important in interpreting the market performance in a given country (Lal & Siahpush, 2009,
p.405). Monthly retail turnover estimates are presented in terms of the current price. Turnover
may be defined as the retail and wholesale sale or commission collected from the agency
activities such as selling lottery tickets (Jensen, 2011, np). The ABS uses economic models in
modeling their economic data. This dissertation will also formulate some economic model that
will model the data provided. The model will show some structural relationship between certain
variables to be discussed in this dissertation.
This survey uses economic factors and also some industry groups and subgroups in
exploring the retail trade statistics. This improves the coverage and also the quality of the
turnover estimates as each of the classes/groups provides a reliable information that is used in the
market analysis of these factors. This survey is conducted on a monthly basis as to ensure no
breaches in the data intervals. The data that was used in this dissertation is a secondary data
published on the ABS website and therefore has been used in this project to offer a deeper
understanding of retaining turnover determinants. A generalized methodology is used also in this
paper in gaining an insight into the turnover market trend. Many enterprises provide their
turnover reports on monthly basis accompanied by data collected during the survey. Then this
information is used in decision making by qualified economists and statisticians.

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Problem statement
The retail industry is a competitive industry in our economy today. Retailers and dealers
in this industry are concerned with making high turnover rates from their businesses. Though
many retail industries have failed in meeting this goal (Rahman et al. 2010, p.156). The trends of
the turnover rates in a calendar year play a big role in providing an insight to retailers on peak
seasons and the off-peak season that help them adjust their operations appropriately depending
on the expected retail sales (Kolias, Dimelis & Filios, 2011, p.143). Therefore, the study of these
seasonal patterns in retail industries is an important aspect in evaluating the performance of any
retail industry in terms of the retail sales it makes in a given month or a specific calendar year.
Objectives of the study
The overall objective of this study is to explore whether there exist a relationship in the
monthly turnover estimates of the different retail industries discussed in this dissertation. The
study is also used to provide a reliable and trusted economic information that is crucial for
decision making in trade industry specifically the retail turnover trade. Therefore the study has
focused on data reliability in making informed decisions by the government agencies and also
research agencies. This economic data is modeled to study the trends in the retail industry. The
study also has explored the impacts of certain economic factors on the retail trade. Generally, the
monthly report compiled from this dissertation shows how certain retail industries vary in terms
of their turnover estimates. These retail industries discussed in this paper include; Food retailing,
supermarket and grocery stores, Liquor retailing, households' goods retailing, electrical and
electronic goods retailing and others. Therefore the goal of this project is to explore monthly
turnover estimates of these different retail industries.

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Hypothesis testing
Hypothesis testing shows how the outlined market variables will influence the market
productivity. Therefore, it shows some relationship between the predictor variables and the
response variable. Our hypothesis testing in this study is to investigate whether there is a
relationship in the monthly turnover estimates of different retail industries provided in the data
set. Therefore;
Null hypothesis (HO): A relationship exists in the monthly turnover estimates
Alternative Hypothesis (H1): There is no relationship in the monthly turnover estimates.
The overall study, therefore, explores whether there is a general relationship in the
monthly turnover estimates of the different retail industries in the data set provided. The
hypothesis targets the overall goal of the study.
CHAPTER 2: LITERATURE REVIEW
Retailers are interested in higher stock turnover rates in their businesses. For instance,
supermarket retailing industry will do well where the retail turnover rates are very high
(Alexander & Doherty, 2009, np). Some factors may also influence the rate of retail sales. These
factors may be economical, social, political or environmental (Tian-Foreman, 2009, p.356).
Further investigations in this field have been done to put into account these confounding
variables that influence the behavior of the retail turnover rates (Australia, 2011, p.24). Customer
relationship with the retailer in a specific industry is also an important factor that may influence
the turnover rates of a certain retail industry (Jefferson & Preston, 2010, p.335). Therefore good
customer relations is a crucial factor in determining the retail turnover rates of any retail
industry.

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In the modern world, technology has improvised the strategies of making retail sales
through online forums that promote retail trade (Lynch et al. 2011, p.277). Through these online
forums, many businesses have grown into bigger firms thus extending their sizes and also
becoming higher-profit makers (Baltagi, 2008, np). Online retail selling has also reduced the cost
of production in both retail and wholesale industries as now most of the labor work is done by
fewer individuals who operate with business machines (Gaur & Kesavan, 2015, np). Generally,
we can conclude that technology has played a positive role in promoting many retail industries
around the globe (Davis, Smith & Marsden, 2007, np).
Some market theories have also been developed to study market forces that may
influence retail turnover rates in retail industries (Lee, Zhou & Hsu, 2015, p.35). These market
forces are said to play a big transitional role to business performance (Booth & Hamer, 2007,
p.289). Researchers, therefore have invested in making further researcher in providing a proper
conclusion to these factors that influence the retail turnover rates (Capkun, Hameri & Weiss,
2009, p.789). Study of the retail turnover rates will provide an insight into different industries in
formulating their business policies.
CHAPTER 3: EXPERIMENTAL TECHNIQUES AND METHODS
As indicated earlier, these turnover estimate reports are produced on monthly basis
(Choudhary & Tripathi, 2012. P.43). The survey has been conducted primarily by use of data
collection techniques such as use questionnaires, telephone interviews, direct interviews or
mailing some questionnaires to businesses (Jefferson & Preston, 2010, p.335). These businesses
that are involved in the survey are selected randomly based on their size and also the industry.
The annualized turnover is used to evaluate the size of the business. Some stratification
technique is also used in grouping different industries into their specific classes (Eroglu & Hofer,

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2011, p.356). A graphical methodology is used to show the trend in the monthly turnover
estimates of different industries. Stratified sampling technique is employed when grouping the
industries into classes they belong to. Therefore, different industries are viewed as strata.
Stratification has an advantage over other sampling techniques; for instance, different
groups are put into strata with homogeneous characteristics (McLachlan, 2013, np). Several
categories (strata) of industries were used in this research paper. Each industry had a
corresponding monthly turnover value that was analyzed using statistical software and Microsoft
Excel. The data analysis process involved outputting of statistical charts and graphs. These
graphs and charts were used in making some conclusions based on their shape and the trend
displayed.
CHAPTER 4: RESULTS AND DISCUSSIONS
This section discusses the findings of the research project. The data analysis focuses on
volume of retail sales (retail turnover) in different times of the year for retail industries in
Australia which include: Food retailing, supermarket and grocery stores, liquor retailing,
household goods retailing, furniture floor coverings, houseware and textile goods retailing and
electrical and electronic goods retailing. Graphs and charts have been used to show the trend in
the retail sales at different points in time of the year.
1.1 Distribution of monthly total turnover estimates
The figure 1.1 below shows how the monthly total turnover estimates were distributed.
The month of December-2017 had the highest total turnover estimates.

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Nov-2017 Dec-2017 Jan-2018 Feb-2018 Mar-2018 Apr-2018
0
5000
10000
15000
20000
25000
30000
35000
40000
Total ditrubution of monthly turnover estimates
Number of months
Turrnover estimates
Fig
ure 1.1: Total distribution of monthly turnover estimates
1.2 Distribution of turnover estimate in November -2017
Research findings of the study showed that in the month of November -2017,
supermarket and grocery stores industry recorded the highest turnover estimates while liquor
industry recorded the least turnover estimates in that specific month of the year. The results of
the findings of the study were as shown in figure 1.2 below

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Supermarket and grocery stores
Liquor retailing
Household goods retailing
Furniture, floor coverings, houseware and textile goods
retailing
Electrical and electronic goods retailing
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
November- 2017 Turnover estimates
Nov-2017 10780.7 Nov-2017 10780.7
Figure 1.2 Distribution of turnover estimate in Nov-2017
1.3 Monthly turnover estimates in each retail industry
The findings of the research showed how monthly turnover estimates were distributed in
each of the retail industry under the study. Food retailing, supermarket and grocery stores were
the industries that recorded higher turnover estimates in the past six months of the study while
furniture, floor covering, houseware and textile goods retailing and liquor retailing recorded the
least turnover estimates in the past six months. The findings also show that retail sales were
higher in the month of December -2017 in all the various types of retail industry.

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Food retailing
Supermarket and grocery stores
Liquor retailing
Household goods retailing
Furniture, floor coverings,
houseware and textile goods
retailing
Electrical and electronic goods
retailing
0 2000 4000 6000 8000 10000 12000 14000
Monthly turnover estimates in each retail industry
Apr-18 Mar-18 Feb-18 Jan-18 Dec-17 Nov-17
Figure1.3 Monthly turnover estimates in each retail industry
1.4 Trend of the monthly turnover estimates
The findings of the research investigated trends in turnover estimates of each industry
category. The volume of retail sales seemed to shoot up in the month of December -2017 in all
retail industries discussed in this study (Choi & Varian, 2012, p.2). And generally, retail turnover
was very low in the month of January-2018. The difference in the two occasions could be due to
some economic or environmental factors that have not been exhausted by this research project.
The trend of the turnover estimate was as shown in figure 1.4 below

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0 1 2 3 4 5 6 7
0
2000
4000
6000
8000
10000
12000
14000
Trend of turnover estimates
Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 Apr-18
Figure 1.4 Trend of the turnover estimates
From above charts and graphs, we can say that turnover estimates for each of the industry
categories had certain pertain during each of the months of the year. For instance figure 1.5
above shows variation of the turnover estimates in every month. Using our hypothesis testing,
that was testing whether a relationship exists in the monthly turnover estimates. From the
findings, it seems that the monthly turnover estimates were independent though there could be
some variation that could show dependency due to other that were not explored by this study.
Therefore, the data could only be modeled through graphing to investigate the effect of trend and
seasonal adjustments in the data set.

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CHAPTER 5: CONCLUSIONS
The datatype of the retail turnover rate estimate was measured in terms of current prices
($ million). The study explored the behavior of the turnover estimates of different industries for a
period of six consecutive months. This report shows that the retail sales increased in the month
of December-2017 in all the types of retail industries. These estimates reflect on how the retail
sales tend to behave in certain periods of the year depending on the type of the retail industry.
The ABS retail small scale (retail) trade include the retail industries discussed in above
section. However, there are other retail industries not explored in the study such as online retail
sales which also offer a significant role in the study of retail turnover trade analysis. The survey
only chose random samples of the retail industries to study the trend of the quarterly retail sales
in a year. This dissertation, therefore, brings a clear insight of the retail sales in Australia
economy and how they trend at different times of the year. Further research in this field is also
needed to expand the knowledge on the behavior of retail sales in different parts of the year and
technical methods for statistical analysis also need to be reviewed to capture inner details of this
data phenomenon.