Nike Sales Analysis and Product Recommendation for 2026
Abiola Toluwani
2025-11-18
This synthetic dataset belongs to one of the World’s leading sportswear and footwear brands, Nike. It contains:
Multiple product lines(Running, Basketball,Lifestyle, Training, Soccer,etc).
Gender Specific Sales(Men, Women, Kids).
Sales from both retail stores and online channels.
AIM
Determine Top Products to stock in 2026.
QUESTIONS
Which product line and product generate the highest revenue and units sold?
Which regions and sales channels contribute the most to overall sales?
Which products are most popular among different genders?
Which products should Nike stock for 2026 to maximize sales?
OBJECTIVES
Determine the top-performing product lines and products by revenue and units sold.
Determine sales performance across regions and sales channels.
Identify popular products among different genders.
Provide data-driven recommendations for products to stock for Nike’s 2026 inventory.
Step1: Load the Necessary Libraries
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.2.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(tidyr)
library(scales )##
## Attaching package: 'scales'
##
## The following object is masked from 'package:purrr':
##
## discard
##
## The following object is masked from 'package:readr':
##
## col_factorStep2:Read in the dataset
sales_details<-read.csv("/Users/mac/Downloads/Nike sales cleaned data.csv")
glimpse(sales_details)## Rows: 2,500
## Columns: 12
## $ Order_ID <int> 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,…
## $ Gender_Category <chr> "Kids", "Women", "Women", "Kids", "Kids", "Women", "M…
## $ Product_Line <chr> "Training", "Soccer", "Soccer", "Lifestyle", "Running…
## $ Product_Name <chr> "SuperRep Go", "Tiempo Legend", "Premier III", "Blaze…
## $ Size <chr> "M", "M", "M", "L", "XL", "M", "M", "M", "XI", "M", "…
## $ Units_Sold <int> 2, 3, 4, 2, 2, 1, 2, 1, 1, 4, 1, 1, 1, 2, 2, 2, 1, 4,…
## $ MRP <int> 6040, 4958, 6040, 9674, 6040, 7364, 6820, 6040, 6040,…
## $ Discount_Applied <dbl> 0.470, 0.615, 0.615, 0.615, 0.615, 0.615, 0.615, 0.32…
## $ Revenue <int> 6402, 5726, 9302, 7449, 4651, 2835, 5251, 4107, 2325,…
## $ Order_Date <chr> "2024-09-03", "2024-09-07", "2024-10-11", "2024-10-04…
## $ Sales_Channel <chr> "Online", "Retail", "Retail", "Online", "Retail", "Re…
## $ Region <chr> "Bengaluru", "Hyd", "Mumbai", "Pune", "Delhi", "Delhi…colSums(is.na(sales_details))## Order_ID Gender_Category Product_Line Product_Name
## 0 0 0 0
## Size Units_Sold MRP Discount_Applied
## 510 0 0 0
## Revenue Order_Date Sales_Channel Region
## 0 0 0 0Step3: Convert Categorical variables to factors
sales_details$Gender_Category <- as.factor(sales_details$Gender_Category)
glimpse(sales_details$Gender_Category)## Factor w/ 3 levels "Kids","Men","Women": 1 3 3 1 1 3 2 1 3 2 ...sales_details$Product_Line <- as.factor(sales_details$Product_Line)
glimpse(sales_details$Product_Line)## Factor w/ 5 levels "Basketball","Lifestyle",..: 5 4 4 2 3 5 5 5 4 4 ...sales_details$Product_Name <- as.factor(sales_details$Product_Name)
glimpse(sales_details$Product_Name)## Factor w/ 20 levels "Air Force 1",..: 16 17 14 4 15 6 16 16 14 10 ...sales_details$Size <- as.factor(sales_details$Size)
glimpse(sales_details$Size)## Factor w/ 10 levels "IX","L","M","VI",..: 3 3 3 2 10 3 3 3 8 3 ...sales_details$Sales_Channel <- as.factor(sales_details$Sales_Channel)
glimpse(sales_details$Sales_Channel)## Factor w/ 2 levels "Online","Retail": 1 2 2 1 2 2 1 2 1 1 ...sales_details$Region <- as.factor(sales_details$Region)
glimpse(sales_details$Region)## Factor w/ 9 levels "Bangalore","Bengaluru",..: 2 4 8 9 3 3 1 1 5 9 ...sales_details$Product_Line<-as.factor(sales_details$Product_Line)
glimpse(sales_details$Product_Line)## Factor w/ 5 levels "Basketball","Lifestyle",..: 5 4 4 2 3 5 5 5 4 4 ...Step4:Check the orders by Gender_Category
ggplot(sales_details, aes(Gender_Category)) +
geom_bar(fill="lightgreen") +
theme_minimal() +
labs(title="Orders by Gender Category")
table(sales_details$Gender_Category)##
## Kids Men Women
## 841 840 819From above, kids and Men accounts for the highest orders with just kids leading with just one order.
Step5:Check Orders by Region
ggplot(sales_details, aes(Region)) +
geom_bar(fill="lightgreen") +
theme_minimal() +
labs(title="Orders by Region")
table(sales_details$Region)##
## Bangalore Bengaluru Delhi Hyd Hyderabad Hyderbad Kolkata Mumbai
## 216 220 438 136 126 141 417 418
## Pune
## 388From above, most orders originated from Delhi than any other region. This indicates that customers from Delhi are the most active contributing the largest share of total orders.
Step6: Check Orders by Channel
ggplot(sales_details, aes(Sales_Channel)) +
geom_bar(fill="lightgreen") +
theme_minimal() +
labs(title="Orders by Sales Channel")
table(sales_details$Sales_Channel)##
## Online Retail
## 1255 1245From above, the online channel has the highest number of orders leading with ten orders.
Step7:Group Product Name by Product line
library(dplyr)
# Summarize units sold per Product Name within each Product Line
product_summary <- sales_details %>%
group_by(Product_Line, Product_Name) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE),
total_revenue = sum(Revenue, na.rm = TRUE)) %>%
arrange(Product_Line, desc(total_units)) # highest units first per line## `summarise()` has grouped output by 'Product_Line'. You can override using the
## `.groups` argument.product_summary## # A tibble: 20 × 4
## # Groups: Product_Line [5]
## Product_Line Product_Name total_units total_revenue
## <fct> <fct> <int> <int>
## 1 Basketball LeBron 20 278 811545
## 2 Basketball Air Jordan 265 767045
## 3 Basketball Zoom Freak 246 703635
## 4 Basketball Kyrie Flytrap 217 540874
## 5 Lifestyle Waffle One 279 742751
## 6 Lifestyle Blazer Mid 267 732817
## 7 Lifestyle Dunk Low 243 705320
## 8 Lifestyle Air Force 1 239 708072
## 9 Running React Infinity 275 679475
## 10 Running Free RN 240 661652
## 11 Running Air Zoom 206 563047
## 12 Running Pegasus Turbo 198 572598
## 13 Soccer Phantom GT 255 707336
## 14 Soccer Premier III 242 605251
## 15 Soccer Tiempo Legend 224 634785
## 16 Soccer Mercurial Superfly 217 604607
## 17 Training SuperRep Go 308 835423
## 18 Training Flex Trainer 282 782761
## 19 Training ZoomX Invincible 274 779115
## 20 Training Metcon 7 224 622380I grouped Product Name by Product Line so as to identify which Product Name falls under Product Line and also to know the total units of each product sold out with the revenue generated.
Step8: Check Total Units sold by Product Line.
productline_units <- sales_details %>%
group_by(Product_Line) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE)) %>%
arrange(desc(total_units))
productline_units## # A tibble: 5 × 2
## Product_Line total_units
## <fct> <int>
## 1 Training 1088
## 2 Lifestyle 1028
## 3 Basketball 1006
## 4 Soccer 938
## 5 Running 919ggplot(productline_units, aes(x = reorder(Product_Line, total_units), y = total_units)) +
geom_col(fill = "lightgreen") +
geom_text(aes(label = total_units), hjust = -0.05) +
coord_flip() +
theme_minimal() +
labs(title = "Total Units Sold by Product Line", x = "Product Line", y ="Total Units Sold") +
scale_y_continuous(expand = expansion(mult = c(0,0.25)))
From above, Training Product line is the highest units sold.
Step9: Check Total Revenue generated by Product Line
productline_revenue <- sales_details %>%
group_by(Product_Line) %>%
summarise(total_revenue = sum(Revenue, na.rm = TRUE)) %>%
arrange(desc(total_revenue))
productline_revenue## # A tibble: 5 × 2
## Product_Line total_revenue
## <fct> <int>
## 1 Training 3019679
## 2 Lifestyle 2888960
## 3 Basketball 2823099
## 4 Soccer 2551979
## 5 Running 2476772ggplot(productline_revenue, aes(x = reorder(Product_Line, total_revenue), y = total_revenue)) +
geom_col(fill = "lightgreen") +
geom_text(aes(label = paste0("#", format(total_revenue, big.mark=","))), hjust = -0.05) +
coord_flip() +
theme_minimal() +
labs(title = "Total Revenue Generated by Product Line", x = "Product Line", y ="Total Revenue") +
scale_y_continuous(expand = expansion(mult = c(0,0.25)))
From above, Training product line generates the highest revenue for Nike Sales store.
Step 10: Check Total Units Sold by Product Line per Gender
table <- sales_details%>%
group_by(Product_Line, Gender_Category) %>%
summarise(TotalUnitsSold = sum(Units_Sold, na.rm = TRUE)) %>%
ungroup()## `summarise()` has grouped output by 'Product_Line'. You can override using the
## `.groups` argument.table## # A tibble: 15 × 3
## Product_Line Gender_Category TotalUnitsSold
## <fct> <fct> <int>
## 1 Basketball Kids 338
## 2 Basketball Men 351
## 3 Basketball Women 317
## 4 Lifestyle Kids 352
## 5 Lifestyle Men 331
## 6 Lifestyle Women 345
## 7 Running Kids 319
## 8 Running Men 283
## 9 Running Women 317
## 10 Soccer Kids 310
## 11 Soccer Men 330
## 12 Soccer Women 298
## 13 Training Kids 355
## 14 Training Men 385
## 15 Training Women 348ggplot(table, aes(x = reorder(Product_Line, TotalUnitsSold), y = TotalUnitsSold, fill = Gender_Category)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = TotalUnitsSold), hjust = -0.05, size = 3) +
coord_flip() +
facet_wrap(~ Gender_Category, scales = "free_y") +
theme_minimal() +
labs(title = "Total Units Sold by Product Line per Gender", x ="Product Line" , y ="Total Units" ) +
scale_y_continuous(expand = expansion(mult = c(0,0.25)))
From above, Training is more popular amidst Kids, Men and even Women.
Step 11:Check Total Units Sold by Product Line Per Region
reg <- sales_details%>%
group_by(Product_Line, Region) %>%
summarise(TotalUnitsSold = sum(Units_Sold, na.rm = TRUE)) %>%
ungroup()## `summarise()` has grouped output by 'Product_Line'. You can override using the
## `.groups` argument.reg## # A tibble: 45 × 3
## Product_Line Region TotalUnitsSold
## <fct> <fct> <int>
## 1 Basketball Bangalore 74
## 2 Basketball Bengaluru 77
## 3 Basketball Delhi 181
## 4 Basketball Hyd 57
## 5 Basketball Hyderabad 45
## 6 Basketball Hyderbad 74
## 7 Basketball Kolkata 180
## 8 Basketball Mumbai 176
## 9 Basketball Pune 142
## 10 Lifestyle Bangalore 84
## # ℹ 35 more rowsggplot(reg, aes(x = reorder(Product_Line, TotalUnitsSold), y = TotalUnitsSold, fill =Region)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = TotalUnitsSold), hjust = -0.05, size = 3) +
coord_flip() +
facet_wrap(~Region, scales = "free_y") +
theme_minimal() +
labs(title = "Total Units Sold by Product Line per Region", x = "Product Line", y ="Total Units Sold") +
scale_y_continuous(expand = expansion(mult = c(0,0.45)))
Training Product Line has the highest units sold in Bangalore, Bengaluru, Delhi,Mumbai and Pune.
Lifestyle Product Line has the highest units sold in Hyd and Hyderabad.
Basketball Product Line has the highest unit sold in Hyderbad and Kolkata.
Step12: Check Total Units Sold by Product Line Per Sales Channel
sal <- sales_details%>%
group_by(Product_Line, Sales_Channel) %>%
summarise(TotalUnitsSold = sum(Units_Sold, na.rm = TRUE)) %>%
ungroup()## `summarise()` has grouped output by 'Product_Line'. You can override using the
## `.groups` argument.sal## # A tibble: 10 × 3
## Product_Line Sales_Channel TotalUnitsSold
## <fct> <fct> <int>
## 1 Basketball Online 488
## 2 Basketball Retail 518
## 3 Lifestyle Online 518
## 4 Lifestyle Retail 510
## 5 Running Online 473
## 6 Running Retail 446
## 7 Soccer Online 473
## 8 Soccer Retail 465
## 9 Training Online 572
## 10 Training Retail 516table## # A tibble: 15 × 3
## Product_Line Gender_Category TotalUnitsSold
## <fct> <fct> <int>
## 1 Basketball Kids 338
## 2 Basketball Men 351
## 3 Basketball Women 317
## 4 Lifestyle Kids 352
## 5 Lifestyle Men 331
## 6 Lifestyle Women 345
## 7 Running Kids 319
## 8 Running Men 283
## 9 Running Women 317
## 10 Soccer Kids 310
## 11 Soccer Men 330
## 12 Soccer Women 298
## 13 Training Kids 355
## 14 Training Men 385
## 15 Training Women 348ggplot(sal, aes(x = reorder(Product_Line, TotalUnitsSold), y = TotalUnitsSold, fill =Sales_Channel)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = TotalUnitsSold), hjust = -0.05, size = 4) +
coord_flip() +
facet_wrap(~Sales_Channel, scales = "free_y") +
theme_minimal() +
labs(title = "Total Units Sold by Product Line per Sales Channel", x = "Product Line", y = "Total Units Sold") +
scale_y_continuous(expand = expansion(mult = c(0,0.45)))
Training has the highest orders online.
Basketball has the highest orders in Retail shops.
Step13: Check the Top Ten Products by Revenue
# Summarize top 10 products by revenue
top_products <- sales_details %>%
group_by(Product_Name) %>%
summarise(total_revenue = sum(Revenue, na.rm = TRUE)) %>%
arrange(desc(total_revenue)) %>%
slice_head(n = 10)
top_products## # A tibble: 10 × 2
## Product_Name total_revenue
## <fct> <int>
## 1 SuperRep Go 835423
## 2 LeBron 20 811545
## 3 Flex Trainer 782761
## 4 ZoomX Invincible 779115
## 5 Air Jordan 767045
## 6 Waffle One 742751
## 7 Blazer Mid 732817
## 8 Air Force 1 708072
## 9 Phantom GT 707336
## 10 Dunk Low 705320# Horizontal bar chart without numeric x-axis
ggplot(top_products, aes(x = reorder(Product_Name, total_revenue), y = total_revenue)) +
geom_col(fill = "lightgreen") +
geom_text(aes(label = paste0("#", format(total_revenue, big.mark = ","))),
hjust = -0.05, size = 4) +
coord_flip() +
theme_minimal() +
labs(
title = "Top 10 Products by Revenue",
x = "Product Name", # omit x-axis label
y = "Total Revenue" # y-axis label
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.25))) 
From above, SuperRep Go generated the highest revenue among all products.This indicates that it is the best performing product, contributing the largest share to total sales.
Step 14: Check the TOP 10 products by Units Sold
# Top 10 products by units sold
top_products_units <- sales_details %>%
group_by(Product_Name) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE)) %>%
arrange(desc(total_units)) %>%
slice_head(n = 10)
top_products_units## # A tibble: 10 × 2
## Product_Name total_units
## <fct> <int>
## 1 SuperRep Go 308
## 2 Flex Trainer 282
## 3 Waffle One 279
## 4 LeBron 20 278
## 5 React Infinity 275
## 6 ZoomX Invincible 274
## 7 Blazer Mid 267
## 8 Air Jordan 265
## 9 Phantom GT 255
## 10 Zoom Freak 246# Plot
ggplot(top_products_units, aes(x = reorder(Product_Name, total_units), y = total_units)) +
geom_col(fill = "lightgreen") +
geom_text(aes(label = total_units), hjust = -0.05, size = 4) +
coord_flip() +
theme_minimal() +
labs(
title = "Top 10 Products by Units Sold",
x = "Product Name",
y = "Total Units Sold"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.25)))
From above, SuperRep Go Product was purchased in the largest Quantity.
Step 15: Check the TOP 3 Products by Units Sold per Gender Category
top_products_gender <- sales_details %>%
group_by(Gender_Category, Product_Name) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE)) %>%
arrange(Gender_Category, desc(total_units)) %>%
slice_head(n = 3)## `summarise()` has grouped output by 'Gender_Category'. You can override using
## the `.groups` argument.top_products_gender## # A tibble: 9 × 3
## # Groups: Gender_Category [3]
## Gender_Category Product_Name total_units
## <fct> <fct> <int>
## 1 Kids Blazer Mid 107
## 2 Kids SuperRep Go 103
## 3 Kids Waffle One 99
## 4 Men Flex Trainer 111
## 5 Men Air Jordan 109
## 6 Men Phantom GT 103
## 7 Women ZoomX Invincible 108
## 8 Women React Infinity 103
## 9 Women LeBron 20 102# Plot with facets by gender
ggplot(top_products_gender, aes(x = reorder(Product_Name, total_units), y = total_units, fill = Gender_Category)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = total_units), hjust = -0.05, size = 4) +
coord_flip() +
facet_wrap(~ Gender_Category, scales = "free_y") +
theme_minimal() +
labs(
title = "Top 3 Products by Units Sold per Gender",
x ="Product Name",
y = "Total Units Sold"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.45)))
From above, Blazer Mid is most popular amidst Kids
Flex Trainer is most popular amidst Men
Zoomx Invincible is most popular amidst Women.
Step16:Check the TOP 3 Products by Units Sold Per Region
top_products_region <- sales_details %>%
group_by(Region, Product_Name) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE)) %>%
arrange(Region, desc(total_units)) %>%
slice_head(n = 3)## `summarise()` has grouped output by 'Region'. You can override using the
## `.groups` argument.top_products_region## # A tibble: 27 × 3
## # Groups: Region [9]
## Region Product_Name total_units
## <fct> <fct> <int>
## 1 Bangalore Metcon 7 32
## 2 Bangalore Flex Trainer 29
## 3 Bangalore Blazer Mid 27
## 4 Bengaluru Metcon 7 44
## 5 Bengaluru Waffle One 33
## 6 Bengaluru React Infinity 32
## 7 Delhi SuperRep Go 74
## 8 Delhi Air Jordan 61
## 9 Delhi Premier III 60
## 10 Hyd Waffle One 22
## # ℹ 17 more rows# Plot with facets by region
ggplot(top_products_region, aes(x = reorder(Product_Name, total_units), y = total_units, fill = Region)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = total_units), hjust = -0.05, size = 4) +
coord_flip() +
facet_wrap(~ Region, scales = "free_y") +
theme_minimal() +
labs(
title = "Top 3 Products by Units Sold per Region",
x = "Product Name",
y = "Total Units Sold"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.45)))
SuperRep Go is more popular in Delhi,Mumbai and Pune
Metcon 7 is more popular in Bangalore and Bengaluru
Waffle One is more popular in Hyd.
Dunk Low is more popular in Hyderabad
Air Jordan is more popular in Hyderbad
Lebron 20 is more popular in Kolkata
Step 17: Check Top 3 Products by UNITS SOLD PER SALES CHANNEL
top_products_channel <- sales_details %>%
group_by(Sales_Channel, Product_Name) %>%
summarise(total_units = sum(Units_Sold, na.rm = TRUE)) %>%
arrange(Sales_Channel, desc(total_units)) %>%
slice_head(n = 3)## `summarise()` has grouped output by 'Sales_Channel'. You can override using the
## `.groups` argument.# Plot with facets by sales channel
ggplot(top_products_channel, aes(x = reorder(Product_Name, total_units), y = total_units, fill = Sales_Channel)) +
geom_col(show.legend = FALSE) +
geom_text(aes(label = total_units), hjust = -0.05, size = 4) +
coord_flip() +
facet_wrap(~ Sales_Channel, scales = "free_y") +
theme_minimal() +
labs(
title = "Top 3 Products by Units Sold per Sales Channel",
x = "Product Name",
y = "Total Units Sold"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.45)))
ZoomX invincible has the highest order online with just a margin over SuperRep Go SuperRep Go has the highest order in retail stores leading with just four orders.
FINDINGS
Training is the Top performing Product Line and SuperRep Go is the top performing Product.
Delhi has the highest orders in all the regions and Online Channel contributes the more to sales.
Blazer Mid is more popular amidst kids, Flex Trainer; amidst Men and Zoomx Invincible amidst Women.
From the data, Training generated the highest Revenue for the year.
CONCLUSION AND DISCUSSION
Nike’s Sales performance is strongly driven by the Training and Lifestyle Product Lines, with SuperRep Go standing out as the overall top performer.
Kids and Men are the most active customer segment, Delhi is the highest-order Region and Online Channel is more active.
Nike Store should stock more of Training Product Line since it generated the highest revenue for the year.
Lifestyle Product Line should be stocked as it is popular amidst Kids.
Other Product Line have been active across all Regions, more strategies should be carried out to promote the other Product Lines(Promotions, Advertisement).
Increase Staff and activities in retail stores to increase sales in the channel.
Delhi is the centre of sales; more retail shops should be opened in each channel to increase the number of sales across all regions.