Project Report for Mostan Superstore

1. Project Report
Client: Mostan Superstore Ltd.
Student ID: 21032086

2. Table of Content
 Executive Summary.
 Solution/Model Testing.
 Solution/Model Adjustments.
 Testing output/ Data Story using Descriptive
Statistical analysis.
 Hypothesis Testing /Data Story using T.test.
 Data Visualisation Dashboard.
 Recommendation.
 Reflection.
 References.

3. Project Overview
The identified problem in this project was
the slow response to emails sent by PTSP
from the client to confirm the refund of
failed transactions to customers and
research was conducted to produce
possible solutions to this problem in
Mostan Superstores. The project took six
months to complete and involved the
following key milestones in Blog 1 to 4.
Blog 1
Drafting a project plan and the summary
of how the project will be carried out.
Blog 2
At this phase, a current state analysis of
the business was conducted where the
exploratory data analysis was used to
identify the fact that POS was the major
source of payment used at the store, that
there were high failure rates and the
difficulty experienced by customers in
getting their refunds. An extensive
literature review to understand the
problem was also carried out where we
also looked for similar cases to gain insight
into how this was resolved by those
businesses.
Blog 3
In this section produced, possible
solutions for the problem which are
creation of a separate email
dedicated to emails relating to
chargeback for easy monitoring,
Identifying PTSPs with low
transaction failure rates for a
possible switch and the use of
points on loyalty cards to make
purchases as opposed to swiping
their bank cards.
Executive
Summary

4. Blog 4
In this segment, a survey was conducted where questionnaires
were rolled out to current customers and potential customers
of the store to seek their opinion on the possible solutions
drafted in blog 3. Data gathered were further analyzed to gain
insight into the customers’ experience on transaction failure
and refund journey as well as analyzing their acceptance of
the possible solution.
In conclusion, we were able to establish that despite the
transaction failure challenge, customers still prefer retaining
banks as the PTSP but will however be satisfied and willing to
come again if their transactions are refunded within 24hrs or
at most two working days.
Executive
Summary cont’d

5. Solution/Model
Testing Plan.
 To make a final recommendation to our client, the descriptive
statistical analysis and the T.test is used to test the results of the data
gathered to gain valuable insight. This is because descriptive analysis
helps to summarize and present results of findings in the simplest way
and aids the understanding of the distribution of data on the variables
to be considered for testing and analysis (Mishra et al., 2019).

6. Solution/Model
Testing Plan Cont’d.
Test Plan using Descriptive Statistical Analysis
 Step 1: The variables to be used are extracted from the
results of the survey and they are seven in total.
 Step 2: These variables are analyzed using the measure of
central tendency (mean and median). This measure helps
derive one value from the data which is used to represent the
whole distribution and can also form basis for other
distribution which can be used for further analysis. The
measure of frequency (Frequency and percentage), this is
used to identify how many times a variable occurs in the
distribution to identify concentration. Lastly, the measure of
variation (Range, Standard deviation, quartile and the
interquartile range) which is used to understand the spread
of the distribution.
 Step 3: The test of normality of the data will be done using
skewness and kurtosis.

7. Furthermore, the paired sample t. test is used to test the
hypothesis . The sample is divided into segments, customers who
are satisfied with the refund process and customers who are not
satisfied. Here the impact of response time on charge back claim
and customer satisfaction will be measured using the following
steps:
Step 1: Set up hypothesis which was set in the Blog1 which is:
H1: Quick response time to complaints on chargeback claims will
have a positive impact on customer satisfaction.
H2: Customers who do not experience transaction failures or get
refunds of their claims prompt or, are more likely to return another
time to shop.
Step 2: Specify the level of significance (set at 95% CI)
Step 3: Decide which test to use: (Paired sample T.test)
Step 4: Compute the test on the sample to get outputs on (t-
statistic, degrees of freedom and exact p-value. Weissgerber et al.,
2018).
Step 5: Draw conclusion from the result.
Solution/Model
Testing Plan. cont’d

8. Solution/Model Adjustments
The following adjustments were made to the solution model
while analysing the data. In blog three, it was stated that the
regression analysis will be used to analyse the data however
during the analysis and data interpretation it was discovered
that this method is not the most suitable and so the method
was switched to using the descriptive statistical analysis while
the paired sample t.test is used to test the hypothesis. The
hypothesis was also restructured to measure the response
time impact on customer satisfaction rather than customer
churn.

9. Testing output/ Data Story using
Descriptive Statistical analysis.
 The gender variables on the table shows the demographic distribution of
the data and from the values we can interpret that the variables are
categorical consisting of male and female the median value of 2 which
indicates slight even distribution of both categories.
S/N Variables Min. 1Qtr Median Mean 3Qtr Max S.D Skewness Kurtosis
1Gender 1 1 2 1.656 2 2 0.478 -0.6563 1.4308
2Age 1 2 2 2.172 3 4 0.653 0.0498 2.7818
3
Response Time
Satisfaction
1 3 4 3.452 4 5 0.903 -0.5671 2.8968
4
Transaction Failure
Satisfaction
1 1 3 3.118 4 5 0.858 -0.0204 3.2437
5Refund Process Influence 1 1 2 2.022 3 5 0.834 0.6386 3.5674
6Pre-Loaded Loyalty Card 1 1 2 1.828 2 5 0.816 1.0479 4.5836
7POS PTSP 1 1 2 3.118 4 5 1.69 0.6699 1.6757

10. Testing output/ Data Story using
Descriptive Statistical analysis.
 The analysis of the variable response time satisfaction with the mean value of
3.452, which is slightly lower than the median, suggests there are more
people in the sample who are dissatisfied or extremely dissatisfied with the
response time than are satisfied or extremely satisfied while the analysis of
the variable transaction failure with a median value of 3 shows that the rate
of satisfaction and dissatisfaction is evenly distributed however, with the
mean figure of 3.118, which is slightly higher than the median. This suggests
that there are slightly more people in the sample who were satisfied or
extremely satisfied with the refund process than were dissatisfied or
extremely dissatisfied
S/N Variables Min. 1Qtr Median Mean 3Qtr Max S.D Skewness Kurtosis
1Gender 1 1 2 1.656 2 2 0.478 -0.6563 1.4308
2Age 1 2 2 2.172 3 4 0.653 0.0498 2.7818
3
Response Time
Satisfaction
1 3 4 3.452 4 5 0.903 -0.5671 2.8968
4
Transaction Failure
Satisfaction
1 1 3 3.118 4 5 0.858 -0.0204 3.2437
5Refund Process Influence 1 1 2 2.022 3 5 0.834 0.6386 3.5674
6Pre-Loaded Loyalty Card 1 1 2 1.828 2 5 0.816 1.0479 4.5836
7POS PTSP 1 1 2 3.118 4 5 1.69 0.6699 1.6757

11. Testing output /Data Story using
Descriptive Statistical analysis
For the variable refund process influence the interpretation shows that
majority of the respondent are indifferent as the population in the sample
is centred around who neither agree or disagree, we really cannot draw a
conclusion from this because the population sample might be small or
there are other factors involved in influencing their decision to make a
return visit to the store. This may be explored in further study.
The analysis of the variable Preloaded loyalty Card with a median value of
2 and mean value of 3.118 higher than the median value suggests that
more people in the sample agree to accepting the use of loyalty card
against using their bank card while majority still prefer banks being the
PTSP.

12. H1: : Customers who do not experience transaction failures or get refunds of
their claims prompt , are more likely to return another time to shop
Q6 = If you have filed a chargeback claim, how quickly did you receive your
refund
Q5 = On a scale of 1 to 5, how satisfied are you with the response time to your
chargeback claim?
t = 5.2491 df = 184 P-value = 4.186e-07
95% confidence interval falls within the acceptable range of 0.4764917 and
1.0503900
Hypothesis Testing /Data Story
using T.test

13. The t-test results suggest that there is a statistically significant
difference in the satisfaction with the response time to the
chargeback claim (Q5) between people who received their refund
quickly (Q6) and people who did not receive their refund quickly. The
p-value is within the acceptable level. The 95% confidence interval
also shows that the difference in satisfaction is statistically significant.
This suggests that the speed of the refund process does have an
impact on the satisfaction of customers with the response time to
their chargeback claim. Customers who receive their refunds quickly
are more likely to be satisfied with the response time than customers
who do not receive their refunds quickly
Hypothesis Testing /Data Story
using T.test Cont’d

14. Data Visualisation Dashboard

15. Leveraging on the outcome of the analysis, the following suggestions will be
recommended:
Since customers still prefers bank being the PTSP(Payment Service Provider), the
options of switching PTSP will not be considered however, we suggest that our
client open a separate email for the purpose of receiving mails for chargebacks and
assign the monitoring of this mail to a dedicated staff in other to monitor
chargeback confirmation mails and respond timely. Since the delay in customers
getting their refund is majorly due to the slow confirmation response from our
client, doing this will make the refund process faster and this will translate to
increased customer satisfaction.
15
Recommendations

16. Secondly, more than half of the population sample affirmed that they would
prefer to make payment with loyalty points on their card, we suggest that our
client explore this as we believe this will reduce the traffic on making payment via
the pos thereby addressing the root cause of the problem which is transaction
failure. This will put ease for both our client and the customers as they have less
transaction failures complaint to deal with and customers on the other end do not
need to bother with filing for chargeback.
Above all, the suggestion is that our client implement the first to start with as this
requires less technicality while focusing on the second recommendation for
proper implementation as this may provide a lasting solution to the issue of
transaction failure, eliminate the need for customers to be tossed back and forth
for the refund of their funds as any failure encountered can be easily fixed by our
client without a third party.
16
Recommendations Cont’d

17. Client
Feedback
My client could not be present at my presentation because of his
busy schedule Here is the excerpt of my client feedback when I sent
a short video my findings to him,
“I want to thank you for embarking on this project to help find
solution to a major challenge in the supermarket , I am glad with
the outcome of the survey where customers are willing to use the
loyalty card as a means of payment as we have been trying to push
the loyalty card when we launched it and I believe this will create
more awareness to using our loyalty card and I believe offering
them a different solution from what competitors are doing is an
advantage for the business and I will work with my IT specialist to
see how this can be implemented. Also, the idea of creating a new
mail will be acted on as soon as possible to see how we can
leverage on that to ease the challenge at hand while working on
setting up the loyalty card payment mode. Once again, I appreciate
your work and I am looking forward to engaging you in the future
when the need arises.
Regards”.

18. Reflection
I am using the Gibbs reflective cycle to describe my reflection on the
entire journey of my research work. The Gibbs framework consists of five
stages as shown in the diagram below aadapted from: (Ibrahim, A., 2017.
Let’s try reflection using Gibbs reflective cycle).

19. Reflection Cont’d
Description: In this project, my task was to consult my client Mostan
superstore to pick out any problem the business is facing and work with
the client to find possible solutions to tackle the problem. After a careful
analysis of the business, the task was to help my client to improve the
process of handling failed transactions on the Point of sale. After the
project preparation phase in Blog one, I started by conducting a literature
review to understand the problem and to gain ideas on the possible
solutions and methods of implementation in Blog two and Blog three. I
then conducted a survey to seek opinions of the customers who may or
have experienced failed transactions and got their feedback to tailor the
solutions based on insight gotten from the data gathered.

20. Reflection Cont’d
 Feelings: At the beginning of the project, I was enthusiastic about the whole
process explained during lectures but by the time it started reality set in and
it feels like a serious hurdle to be crossed coupled with the result of my first
blog, I was not happy because I taught, I should have done better. This
frustration made me take a step back, took the feedback of my supervisor,
spoke to CASE, sent a draft of the second blog to my supervisor and went
back to the lecture notes to really understand what was expected of me in
the blog two to put out a good work both for the client and to my
supervisor.
 Another challenge I faced was getting data from my client and getting him
to sign the consent form. I was able to overcome the challenge by doing a
constant reminder and I also had to work with the staff supervisor in the
store to help facilitate fast delivery of these documents. Another not so
good moment was during the third blog where I had worked on the third
blog before the last session with the module leader where she made a
further explanation of what is expected at this point, I knew I had to start all
over , it was a bit challenging but with the help of my supervisor’s drop-in
sessions, I was able to pull through and met the submission deadline.

21. Reflection Cont’d
 My happy moments on the project were seeing the comment of my
supervisor on the second blog, this also challenged me that I can do
better and deliver much more work and being able to conduct the
primary data and analyses as this analysis broadened my knowledge
of the statistical package and tool which even fuel my desire to pursue
further learning of the statistical tool, I taught was difficult to use. I
felt a sense of satisfaction when I was able to produce a possible
solution to help Mostan Superstore to improve their handling of failed
transactions.
 Evaluation: I can account for the factors that led to the success of this
project as well as setbacks which are presented below:
 Support from the project stakeholders which are my client, my
supervisor, the module leader and CASE.

22. Reflection Cont’d
 The availability of data from my client as well as data from the survey
conducted, the ability to work on the project in batches and the time
frame for submission of each batch of the project as well as the
extension granted by the module leader at some point that we
needed it
 Some failure I experienced in the process of the work especially in the
first blog was not starting early enough and had to work under
pressure to deliver within time this made me loss grip of somethings I
should have done well. I learnt a lot from this, and I ensured an early
start to all other aspects.
 Conclusion: In conclusion on this project, I learnt the following:
 The process of consulting for a client, identifying business problems,
conducting research and drafting out solutions to business problems. I
have been able to build my communication skills, collaboration and
working with stakeholders to meet deadlines. The fourth blog helped
me improve my presentation skills, my data analysis skills and being
able to explain analysed data.

23. Reflection
 Next time…..
 What I will do differently is to start early to meet deadlines without
pressure. I will do this by sticking totally to the project plan. Rather
than using survey alone in gathering my primary data, I will also
include an interview method to complement the data gathered and
have wealth of information to predict possible solutions. I would also
spend more time communicating with the stakeholders for better
understanding and direction. This journey has not only broadened my
knowledge but has also ignited my passion for continuous learning
and improvement, it has opened my analytical reasoning and creative
thinking. It has also helped honed my problem-solving skills.
 Action: My action plan is to l continue to learn about the problem of
failed transactions. I will also continue to develop my skills in business
problems research, data analysis and communication. I am confident
that I can use these skills to help other businesses who may be
experiencing similar challenges and even other business problems.

24. References:
 Ibrahim, A., (2017). Let’s try reflection using Gibbs reflective cycle.
 Mishra, P., Pandey, C.M., Singh, U., Gupta, A., Sahu, C. and Keshri, A., (2019). Descriptive statistics and
normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), p.67.
 Weissgerber, T.L., Garcia-Valencia, O., Garovic, V.D., Milic, N.M. and Winham, S.J., (2018). Why
we need to report more than'Data were Analyzed by t-tests or ANOVA'. Elife, 7, p.e36163.

25. Thank You