Thao Phi's Submission in Data Story-telling with QuickSight Contest during DevAx Online Workshop Series 2: Data Analytics.

1. US E-COMMERCE
AUTHOR: PHI LAM PHUONG THAO
EMAIL: PHILAMPHUONGTHAO@GMAIL.COM
Data Story-telling with QuickSight
October 15, 2021
(in the period from 13-sept-2013 to 14-Jan-2014)
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2. CONTENT
Get started
Idea behind
the scene
Customers’
Demographic
Customers’
Behavior
Order
Management
Delivery
Further
Implication
Forecasting
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3. Here’s my outline to get started
• Topic: US E-commerce
• Period: 5 months (from 13-Sept-2013 to 14-Jan-
2014)
• Information: a data set provide information
about order management. It comprises 19
columns which can divide into 4 groups:
1. Customer demographics
2. Customer behavior
3. Order management
4. Delivery
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Key facts from analyzing the dataset

4. The idea behind the
scene
Why did I slice the dataset into 4 groups? Because 1 group can
reveal specific insights. More specific:
1. Customer demographic:
• Customers come from which state, which city, their gender?
2. Customer behavior:
• At what time do customers trigger an order?
• At which period of the year do they order the most?
• Which platform do they prefer to order?
• Conversion rate?
• What status of customers do they log in the most frequently?
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5. The idea behind the
scene
3. Order management
• Any correlation between Individual Price and Quantity (cheaper
product be sold much more)?
• Which product is sold the most? In which category? Belonging
to which state? When?
• Which products are bought by which gender?
4. Delivery
• What kind of products are delivered by what method?
• Which delivery method is preferable over regions?
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6. CUSTOMERS’
DEMOGRAPHIC
¡ Customers in E-commerce Industry in the US come from 3 States:
Washington (39,725 people), California (22,852 people), and New
York (2,799 people), with different gender ratios relating to shopping
online.
¡ As we all know, female has a passion for shopping. But, not alike what
we expected, the pie chart below shows that male takes dominance
in the number of customer in E-commerce in the US, as they account
for 57% over 100%.
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7. CUSTOMERS’
BEHAVIOR
¡ The total cost for the order gets the highest in November and
December, as it’s time for Christmas - a high-demand season.
Therefore, need to stock goods for this period.
¡ However, there is no big difference in order time. Customers tend to
order from 00-24h per day, looking at the distribution chart, it can be
seen that the 3 most ordered time period are: early morning (8h-9h),
early afternoon (14h), and evening (21-22h) 7

8. CUSTOMERS’
BEHAVIOR ¡ People tend to use Web to be shopping instead of mobile, they want to see
clearly the product with a full screen, which accounts for 81%
¡ 96% customers log-in are Members, only 4% are Guest, New and First
SignUp
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9. CUSTOMERS’
BEHAVIOR
¡ There are 65,376 customers
who have started to order, but
finally, only 56,709 orders are
successful (equal to 86,74%
conversion rate)
¡ Need to consider, why the
remaining 13,26% have
canceled or stop the order.The
reason may be because of
payment method, user
interface website is difficult to
follow, etc.
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10. ORDER
MANAGEMENT
¡ Fashion takes the major in the total cost
of the order in both Washington and
California, takes 85,59M (52,14%) and
37,20M (40,16%), respectively.
¡ In NewYork, consumers prefer Clothing
to Fashion.
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11. ORDER
MANAGEMENT
¡ Thanks to the stacked bar chart above, we know that males are
interested in a certain number of products, such as shirts, spectacles,
Books, wat NewYork. Whilst, female desires Jean, Shoes, and Bag.
Both males and females bought Fairness Cream.
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12. ORDER
MANAGEMENT
¡ In the developing countries, cheaper products will sell more, and vice
versa, more expensive products will sell less. But this doesn't seem to
be the case for US E-commerce market
¡ The Scatter Plot chart shows that there is no correlation between
Quantity and Price. It means consumers are not sensitive to price.
Therefore, when planning a price strategy, enterprises should notice that
price is NOT the priority in US E-commerce.
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13. DELIVERY
¡ There is no difference in Delivery Type between Categories. It’s quite
balanced in the one-day delivery method vs. Normal Delivery.
¡ In contrast to this, the second chart shows that it's a significant
difference in delivery methods amongst States.While customers in
California prefer one-day delivery, the ones in Washington are
interested on Normal Delivery 13

14. FURTHER
IMPLICATION
¡ Launching the loyalty program to attract customers, as
96% of customers log in to order is Members. Introduce a
member card, for those who own this, will become Member.
With that card, they can accumulate points, get a discount
on the next purchase.
¡ Allocate the cost of the order among the States by
the ratio: 60%, 35%, and 5% for Washington, California, and
NewYork respectively
¡ Customer is not sensitive to price, define the new price
strategy to increase profit.
¡ Last but not least, stock up inventory for the last 3
months of the year, especially November.
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15. FORECASTING
¡ Trends cannot be used to predict in this case,
because:
- Less data, only for 5 months
- These are seasonal products (the number of products
increases in the last quarter of the year), so if the
prediction for the next month, the low season will be
inaccurate.
For example: It is impossible to predict for February, March,
April 2014, but has no data of February, March, April 2013.
Ø This leads to incorrect prediction.
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16. THANKYOU!
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