Week1_portfolio_yzh
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School
Johns Hopkins University *
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Course
BU.510.601
Subject
Information Systems
Date
Dec 6, 2023
Type
docx
Pages
2
Uploaded by CoachField12328
How to identify potential membership customers by payment
preference in the supermarket?
Group 1 SA F3: Yang, Xiong, Qin, Wang
Supermarkets like Costco and Sam require customers to join their
membership in order to shop inside, but not every customer prefers joining
the membership due to various reasons. As a group of finance students who
are interested in opening a supermarket near Carey Business School, we are
interested in identifying the customers’ preference of joining the supermarket
membership in order to retain customers. For the first week, we would like to
explore if the payment method is related to the customers’ intention to join
the membership, for example, people using credit card might be more willing
to join the membership since they might have greater desire for consumption.
We will be utilizing
Supermarket Sales Data
from Kaggle to answer the
following question:
1.
What percentage of customers are members of the supermarket?
((Calculate marginal probability)
2.
What percentage of customers are members of the supermarket and use
Ewallet(or credit card) for payment? (Calculate joint probability)
3.
Does the probability that customers using Ewallet (or credit card) for
payment depending on the type of customers? (Calculate conditional
probability)
4.
What is the probability that a customer using Ewallet (or credit card) is a
member of the supermarket? (Using Bayes’ Theorem)
Information About Data and Data Preparation
The original data is provided by ARUN JANGIR agreeing on Open Database
License 1.0 which provides a comprehensive overview of supermarket sales,
offering insights into consumer purchasing behaviors, product trends, and
retail dynamics. We could explore sales volumes, seasonal patterns, and
promotional impacts for informed decision-making in the competitive retail
sector by utilizing this data form. This data form has 12 variables and 1001
rows. To answer the question above, we only need customers’ membership
and their payment method information. This is a well-formed, cleaned data
form so we do not need to clean and sort it out, we just removed features that
we don’t need and then we are good to go. You can find the filtered form on
our
Github Repository
.
Analysis and Results
To answer our 4 research questions, we utilized Excel Function “COUNTIF
(range, criteria)” and “COUNTIFS (range1, criteria1, range2, criteria2)” to
count the number of members and those who are using Ewallet or Credit card
for payment. The results are shown in Table 1.
1.
P(Member)=501/1000
2.
P(Ewallet∩Member)=161/1000
P (Credit∩Member) = 172/1000
3.
P(Member)=501/1000 P(Ewallet)=345/1000 P(Credit)=311/1000
P(Ewallet|Member) = 161/501
P(Ewallet|Normal) = 184/499
P(Credit|Member) = 172/501
P(Credit|Normal) = 139/499
4.
P(Member|Ewallet) = P (Member, Ewallet)/P(Ewallet) = 161/345
P(Member|Credit) = P (Member, Credit)/P(Credit) = 172/311
P(Member|Credit) > P(Member|Ewallet)
Table 1:Relative Frequency and Conditional Probability Results
The probability that a customer would purchase a membership is
501/1000, which indicates that about half of the customers are likely to be a
member of the supermarket. Among all the 1000 customers, 345 of them are
using Ewallet to pay and only 311 of them are using Credit Card. However, the
probability that a customer would use Ewallet given he/she is a member is
161/501 and the probability that a customer would use credit card given
he/she is a member is 172/501, which indicates that those customers who are
members of the supermarket are more likely to use credit card. In fact, if we
are trying to identify customers who are more likely to join our membership by
observing their payment method, conditional probability P(Member|Ewallet)
and P(Member|Credit) is what we are looking for. Applying Bayes’ Theorem to
the calculation (as it is calculated above), we have P(Member|Ewallet) =
161/345 and P(Member|Credit) = 172/311. Comparing the two probabilities,
we might draw to the conclusion that people using credit card to pay for their
shopping are more likely to purchase a membership in the supermarket.
Therefore, possible strategies here could be to try to encourage customers to
pay by credit card or try to attract more credit card users to shop in our
supermarket, such as giving a discount to credit card payments or establish
extra points system for credit cards.
(Portfolio updates on GitHub every week
Link
)
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