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Question: COMM1190 Industry-based Assessment 1: Individual Report
About the task:
The leadership team is seeking to explore the factors that are associated with
voluntary superannuation contribution of users on the RoundUps app. Moneysoft has
contracted you as a data analyst to investigate these factors. The company has
provided you with the data, on the user’s demographic information (e.g., age, gender,
employment) and application usage information (e.g., number of transactions, number
of sessions per month). A detailed description of each attribute of the dataset is
presented in the Data Dictionary, which will be shared separately.
Moneysoft requires you to:
1. conduct descriptive analytics to identify the factors that are associated with
voluntary superannuation contribution. Note that Descriptive Analytics refers to
statistics and visualization techniques. As an example, a box plot and a bar
chart are considered as two different techniques.
2. provide recommendations to its leadership team about how to improve
voluntary superannuation contributions, and more generally, user engagement
on application based on the descriptive analytics results.
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1. TABLE OF CONTENTS
LIST OF TABLES......................................................................................................................2
LIST OF FIGURES ....................................................................................................................2
INTRODUCTION ......................................................................................................................3
DATA ANALYSIS.....................................................................................................................3
RECOMMENDATIONS ............................................................................................................7
APPENDICES ............................................................................................................................9
APPENDIX I: MAPPING SCHEME.....................................................................................10
APPENDIX II: OTHER PLOTS............................................................................................11
2. LIST OF TABLES
Table 1: Mapping Scheme used for Analysis.............................................................................10
LIST OF FIGURES
Figure 1: Correlation Heatmap for the given data ........................................................................4
Figure 2: Histogram of Total Voluntary Contribution bifurcated using the Frequency of Decline 7
Figure 3: Histogram of Total Voluntary Contribution bifurcated using "Show Tutorial"..............7
Figure 4: Histogram of Total Voluntary Contribution bifurcated using Home Ownership Status11
Figure 5: Histogram of Total Voluntary Contribution and Financial Literacy ............................11
Figure 6: Histogram of Total Voluntary Contribution and Financial Advisor.............................12
3. INTRODUCTION
Moneysoft Private Limited is a company that specializes in FinTech solutions. The company is
based out of Sydney. The money has a product which uses superannuation funds for engaging and
retaining the members. A key issue here is that the providers are unable to engage users due to
communication gaps regarding the benefits of the voluntary contributions towards these
superannuation accounts. The company’s product named “MoneySoft RoundUps” aims at solving
this challenge.
The company has hired a data analyst to give insights on the factors that are associated with the
superannuation contribution and also to throw light on improvising the contributions.
DATA ANALYSIS
To start off with the analysis, a heatmap showing the correlation matrix is generated that would
enable us to determine the interrelated factors in the data. This would help us eliminate the other
non useful pairs of data. Firstly, the other data has been mapped as numerals for better analysis.
The mapping scheme is shown in Appendix I. Fig. 1 shows the heatmap generated.
There were many variables that had a strong correlation. However, we shall concentrate on the
one’s that are of importance to us. From the correlation heatmap, following can be concluded:
a. Home ownership status and personal financial advisor show a positive relationship. This
means people who are renting usually do not avail this service. Similarly, there is a positive
relationship with frequency of decline. This means for people renting their houses tend to
have higher decline rates.
5. b. Considering personal financial advisor parameter, it was seen that the frequency of decline
is higher for people who do not have financial advisors. Similarly, people who do not have
availed the financial advisor service, do not tend to use Show Tutorial option.
c. The relationship status and number of dependents are positively correlated. This means
people who are not single tend to have dependents.
d. The income and home ownership are strongly negatively correlated. That is, as the income
increases, people tend to buy their own homes. Also, income and personal financial advisor
are negatively correlated. This means people who have higher incomes tend to hire
financial advisors. The income and the frequency of decline show a negative correlation.
This means, that as the income increases, the frequency of declining decreases. The income
and Show Tutorial option are also negatively correlated. This means that higher income
groups tend to use Tutorial option.
e. Trends similar to ‘d’ are observed for Financial Literacy, Total Contribution and Age
factors as well.
f. The monthly income and financial literacy are positively correlated to each other. This
means as the income increases, the financial literacy increases. Also, the income and age
are positively correlated. This means, an increase in age tends to increase in income. The
income and total contribution are also positively correlated. This means that as the income
increases, the total contribution towards the funds increases.
g. Age and total contribution are positively correlated. This means, as the age increases,
people tend to contribute higher.
h. The financial literacy and age are positively correlated. Therefore, an increase in age tends
to increase the financial literacy of person. The financial literacy is also correlated to the
6. total contribution of the person. Therefore, as the financial literacy increases, the total
contribution of the person increases.
Based on the above conclusions from the heatmap, the key factors that are of importance in this
dataset for analysis of contribution are the frequency of decline, Showing Tutorial and Total
Contribution. It has already been established, that these factors are strongly correlated (either
positively or negatively) with Home Ownership Status, Personal Financial Advisor, Monthly
Income, Age and Financial Literacy.
We will now see the relationship of the three factors, namely, Total Contribution with Frequency
of Decline and Show Tutorial using histograms. The other plots are shown in Appendix II.
Fig. 2 shows the histogram for Total Contribution bifurcated by the Frequency of Decline. It is
seen that the users who contribute more have a low frequency of decline, while users who
contribute low have higher decline rates. The reason could be the income differences leading to
such decisions.
Fig. 3 shows the histogram for Total Voluntary Contribution and Show Tutorial. Higher income
groups tend to have Show Tutorial enabled. The reason could be that it was seen that Age and
Income are highly correlated. Thus, most of the high income groups would be elder and thus,
somewhat less tech savvy.
7. Figure 2: Histogram of Total Voluntary Contribution bifurcated using the Frequency of Decline
Figure 3: Histogram of Total Voluntary Contribution bifurcated using "Show Tutorial"
RECOMMENDATIONS
Based on the results obtained from previous sections, following are the recommendations:
8. a. People who have personal financial advisors are more open to contributions. Start a
campaign to educate people by giving them free financial advice and customized plans
according to their pocket.
b. Low income groups have a high decline frequency. This is primarily due to limited budget.
Ask for income during pre – registration process and set the Trigger and other related
variables high enough to provide leverage to low income groups.
c. Usually, people who have their own homes are more open to contributions. This is because
there are no rent obligations etc. Thus, provide suitable options are people who are renting.
d. Create a blog to improve financial literacy of the audience.
10. APPENDIX I: MAPPING SCHEME
The mapping scheme is illustrated in Table 1.
Table 1: Mapping Scheme used for Analysis
Variable Name Value Mapped Value
C_Education
H
D
B
M
P
1
2
3
4
5
C_Gender
M
F
O
1
2
3
C_HomeOwnershipStatus
Y
N
1
2
C_RelationshipStatus
S
M
O
1
2
3
C_EmploymentType
C
P
1
2
C_FinancialLiteracy
L
M
H
1
2
3
C_PersonalFinancialAdvice
Y
N
1
2
App_FrequencyOfDecline
L
M
H
1
2
3
App_ShowTutorial
Y
N
1
2
11. APPENDIX II: OTHER PLOTS
Fig.’s 4, 5 and 6 show the other plots. These are kept as appendix, as their conclusions were
deduced majorly from correlation matrix itself.
Figure 4: Histogram of Total Voluntary Contribution bifurcated using Home Ownership Status
Figure 5: Histogram of Total Voluntary Contribution and Financial Literacy
