# A tibble: 2 × 3
Gender mean_purchase median_purchase
<chr> <dbl> <dbl>
1 Female 60.2 60
2 Male 59.5 60Shoppers
Shoppers
Huge thanks to Sourrav Banerjee who compiled and published the data I used for this analysis.
For a consumer, finding out other peoples shopping trends might not be that useful. For small businesses however, knowing how frequent a product is being sold and by which demographic could be important. Realistically I can’t definitively say what products will maximize their sales, but I could determine how likely certain products get bought. This would give a solid baseline as to which product type should be continued.
Lets begin by getting a rough view of a part of the data we’re going to be graphing. Separated by gender (male and female) we’ll see how the data is distributed with the amount of purchases they’ve done. This is to see if there’s any large bias in the data set I might need to consider later.
With this I could say the spending habits of men and women displayed in the data are pretty evenly distributed. Now lets get a basic graphs to help visualize some of the differences between how many more instances of people purchasing 20-100 items exist. The count value in the second graph is significantly larger because, for clarity, I grouped up the amount purchased in frequencies of five (20-24, 25-29, so on so forth). That is to say all people who only bought twenty to twenty four items are grouped together.
Bar graphs are good quick visualizations but having the data more accurately displayed is important. Suppose you wanted to see what payment method your clients had and what data you had to work with.
# A tibble: 6 × 2
`Payment Method` count
<chr> <int>
1 Bank Transfer 612
2 Cash 670
3 Credit Card 671
4 Debit Card 636
5 PayPal 677
6 Venmo 634, , = Female
Accessories Clothing Footwear Outerwear
Bank Transfer 62 92 30 19
Cash 66 93 38 15
Credit Card 73 99 32 19
Debit Card 50 92 28 11
PayPal 74 90 41 16
Venmo 67 90 30 21
, , = Male
Accessories Clothing Footwear Outerwear
Bank Transfer 118 193 62 36
Cash 148 208 66 36
Credit Card 145 207 61 35
Debit Card 145 201 71 38
PayPal 158 192 62 44
Venmo 134 180 78 34This is still pretty general though, let’s look at something much more specific. These next expanded tables with regards to men and women detail the frequency purchase for specific items and their quantity.
, , = Female
Annually Bi-Weekly Every 3 Months Fortnightly Monthly Quarterly
Backpack 5 5 8 5 1 6
Belt 7 7 8 5 13 8
Blouse 13 14 14 3 7 7
Boots 6 9 3 10 2 12
Coat 9 5 5 9 7 5
Dress 10 10 9 2 10 4
Gloves 9 6 2 6 3 5
Handbag 4 11 10 7 12 7
Hat 9 7 7 6 8 8
Hoodie 4 6 10 8 9 8
Jacket 9 6 10 13 9 4
Jeans 2 3 6 5 9 2
Jewelry 10 7 13 8 3 6
Pants 7 8 3 5 10 9
Sandals 9 11 10 7 9 5
Scarf 11 5 5 3 7 7
Shirt 7 12 8 9 10 7
Shoes 6 5 7 4 7 7
Shorts 5 8 5 7 6 8
Skirt 4 6 9 7 10 5
Sneakers 5 3 7 8 5 8
Socks 13 8 8 7 7 7
Sunglasses 6 9 4 9 9 12
Sweater 5 13 7 4 4 8
T-shirt 10 4 8 6 8 4
Weekly
Backpack 7
Belt 7
Blouse 8
Boots 8
Coat 7
Dress 7
Gloves 6
Handbag 7
Hat 7
Hoodie 6
Jacket 3
Jeans 2
Jewelry 5
Pants 6
Sandals 8
Scarf 7
Shirt 6
Shoes 12
Shorts 9
Skirt 8
Sneakers 6
Socks 8
Sunglasses 7
Sweater 9
T-shirt 6
, , = Male
Annually Bi-Weekly Every 3 Months Fortnightly Monthly Quarterly
Backpack 17 16 15 17 10 15
Belt 17 17 19 14 10 14
Blouse 19 17 17 17 8 18
Boots 9 14 16 15 6 19
Coat 14 15 13 18 13 22
Dress 21 10 19 18 17 13
Gloves 13 17 14 15 17 14
Handbag 17 9 21 14 14 12
Hat 12 10 13 13 21 21
Hoodie 11 21 13 11 16 14
Jacket 11 14 19 13 15 21
Jeans 18 6 12 11 19 17
Jewelry 16 25 12 13 17 17
Pants 14 11 22 15 22 19
Sandals 17 10 17 17 14 12
Scarf 18 15 16 20 17 13
Shirt 24 11 12 10 18 15
Shoes 20 15 17 15 15 11
Shorts 12 19 19 17 14 12
Skirt 16 13 15 17 15 14
Sneakers 15 13 15 20 17 9
Socks 16 16 16 10 19 18
Sunglasses 13 14 13 15 9 24
Sweater 17 16 15 21 11 12
T-shirt 10 15 18 13 14 18
Weekly
Backpack 16
Belt 15
Blouse 9
Boots 15
Coat 19
Dress 16
Gloves 13
Handbag 8
Hat 12
Hoodie 14
Jacket 16
Jeans 12
Jewelry 19
Pants 20
Sandals 14
Scarf 13
Shirt 20
Shoes 9
Shorts 16
Skirt 19
Sneakers 14
Socks 6
Sunglasses 17
Sweater 22
T-shirt 13Perfect! With this we could infer what items would be in higher demand and how often the store should stock.