IS3100 Techniques For Big Data S01 Group 2 (Bubble) “Make Data Talk” Black Friday Business Analysis
IS3100 Techniques For Big Data
S01 Group 2 (Bubble)
“Make Data Talk”
Black Friday Business Analysis
Group Members:
Choi Yi Ting (54837597)
Chuang Nga Tung (54845014)
Leong Hei Tong (54702088)
Tsang Tsz Ki (54691266)
Agenda
Background – Black Friday
Data Description
Business Insights
Recommendations
Limitations
Background Information
Black Friday
The day after Thanksgiving Day (4th Thursday in November)
Retailers offer massive discounts
to report higher profits
profit is recorded in black ink
Our DataSet
Topic : Analysis of Black Friday
Size of data : 23.7MB
Amount of data: around 550,000 observations
Contains 538K rows and 12 columns
Variables we focus on:
Gender
Age
Occupation
Marital Status
Product _Category_1
Purchase
Our Aims
To analyse the dataset
Find out who spent more on Black Friday by
Age
Gender: Men or Women
Occupation
Popular product types among genders, and age groups
Relationship between types of product and gender, and age group
To give recommendations to solve retailers’ problems
Improve marketing strategies
Suggest products based on age/gender
Data Description
Find out potential market :
Which age groups spent more on black friday?
Which age groups spent more by gender?
Which occupation spent the most?
Which type of product has the best sale?
Age and purchase
The purchase amount spend by age on black friday
Age of 26-35 group spent the most
Stronger purchasing ability
Potential customer
We will discuss and find out the business insights later
Eg. Which product they spent most?
Age of 0-17 group spend less
Less total purchase amount
less attractive for business to choose them as target market.
Total purchase amount spent by different age of people
Age and purchase by gender
The purchase amount spend by age and gender
Average purchase amount by each age group is almost the same
In total, the majority of male spent more than female
Male of age 26-35 group spent the most
Strongest potential and main target market
Discuss business insights later
Eg. Which product they spent most?
Design more product suit them
The woman of age 0-17 group spent the less
Purchase amount of age group by gender
Occupation and purchase
Total money spent by each occupation on black friday
Occupation 4 spent the most
Occupation 0 spent the second most money
Both are strong potential customer
But hard for a business to identify their main target market by occupation
Occupation 8 spent the less
Product and sales
Among product category 1:
Product 5, 1 and 8 are more popular
High purchase amount $ and
Large Number of product purchased
Potential market
Discuss business insights later
Among Product Category_1
Black Friday’s
Business Insights
Two aspect to analyze the dataset
Popularity of different product type among Genders
Relationship between Gender and Purchase of Product Type
Popularity of different product type among Age groups
Relationship between Age group and Purchase of Product Type
Limitations:
Missing values about occupation and product category that may due to viruses, malware damage or man-made problem
Solution:
Base on the results generated through Spyder, we made prediction for occupation and product category for following analysis
Relationship between Gender and Amount of Item Purchased by Product Type
Popularity of products among genders on black friday
Most female and male customers among all spent on buying products in category 5 (Books) and least on category 9 (Gift card)
Likelihood of a product to be purchased by gender on black friday
Male customers are more likely to purchase products in category 17 (Electronics) than female for 2.7 times
Due to development and design of electronic products are dominated by male thinking
( Schröder, 2010)
Male customers are least likely to buy products in category 14 (Beauty)
However, there is a trend that increasing number of men using beauty products (Cheng, 2008)
Relationship between Gender and Amount of Item Purchased by Product Type
2. Relationship between Age group and Item Purchased by Product Type
2. Relationship between Age group and Amount of Item Purchased by Product Type
Popularity of products among age groups on black friday
Product type 5 (Books) is most welcomed among all products in general
Product type 17 (Electronics) is second least welcomed among all products
Electronics is durable goods and relatively costly
Middle-aged adults (age 26-55) are the major target
6.5 times to group of youth and young adults (age 0-35)
Higher purchasing power
Potential to grow
Recommendations
Improve Marketing Strategies
Bundle Sets/Upselling
group products that are more likely to be bought with less popular products
offer an extra product at a discounted price
i.e. outdoor backpack and hangers set deal for male customers
E-commerce Personalization
display goods that are more likely to be bought
retargeting on social media
2. Inventory Control
Avoid running out of stock
Identify the popularity of various product types
Better estimation of inventory needs
Limitations
Limitations
Representativeness
information from one retail store only
Missing Values in Dataset
Categories and Occupation ID are unknown
may due to data damage
contacted data uploader
Thank you
Any Question?