Analisis Market dengan total penjualan (Sales) dan profit (Profit) tertinggi pada Coffee Chain Dataset
Balqis Chaessany Shadrina-M0725005
2026-04-14
Deskripsi Insight
Insight yang diambil dalam analisis ini adalah untuk mengetahui Market dengan total penjualan (Sales) dan profit (Profit) tertinggi pada Coffee Chain Dataset. Analisis ini dilakukan dengan membandingkan kinerja Sales dan Profit di setiap wilayah pasar (Market).
Tujuannya adalah untuk melihat wilayah mana yang memiliki penjualan tinggi sekaligus mana yang benar-benar menghasilkan keuntungan yang lebih optimal. Dari hasil analisis juga terlihat bahwa penjualan yang tinggi tidak selalu diikuti oleh profit yang tinggi. Hal ini penting untuk menjadi bahan evaluasi, terutama terkait biaya operasional, penetapan harga, serta promosi di masing-masing wilayah.
Connect Data SQL Ke R
library(DBI)
library(odbc)## Warning: package 'odbc' was built under R version 4.5.2odbcListDrivers()## name attribute
## 1 SQL Server APILevel
## 2 SQL Server ConnectFunctions
## 3 SQL Server CPTimeout
## 4 SQL Server DriverODBCVer
## 5 SQL Server FileUsage
## 6 SQL Server SQLLevel
## 7 SQL Server UsageCount
## 8 MySQL ODBC 8.0 ANSI Driver UsageCount
## 9 MySQL ODBC 8.0 Unicode Driver UsageCount
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## 11 Microsoft Access Driver (*.mdb, *.accdb) APILevel
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## 13 Microsoft Access Driver (*.mdb, *.accdb) DriverODBCVer
## 14 Microsoft Access Driver (*.mdb, *.accdb) FileUsage
## 15 Microsoft Access Driver (*.mdb, *.accdb) FileExtns
## 16 Microsoft Access Driver (*.mdb, *.accdb) SQLLevel
## 17 Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb) UsageCount
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## 19 Microsoft Excel Driver (*.xls, *.xlsx, *.xlsm, *.xlsb) ConnectFunctions
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## 37 0DBI::dbConnect(odbc(),
Driver = "MySQL ODBC 8.0 ANSI Driver",
Server = "127.0.0.1",
UID = "root",
PWD = "Pemalang07_",
Port = 3306)## <OdbcConnection> root@127.0.0.1 via TCP/IP
## Database: null
## MySQL Version: 8.0.43con1 = DBI::dbConnect(odbc(),
Driver = "MySQL ODBC 8.0 ANSI Driver",
Server = "127.0.0.1",
UID = "root",
PWD = "Pemalang07_",
Port = 3306)
con2 = DBI::dbConnect(odbc(),
Driver = "MySQL ODBC 8.0 ANSI Driver",
Server = "127.0.0.1",
UID = "root",
PWD = "Pemalang07_",
Port = 3306,
Database = "coffe_database")dbDisconnect(con1)
dbListTables(con2)## [1] "facttable" "location" "product"dbListFields(con2, "facttable")## [1] "Profit" "Margin" "Sales" "COGS"
## [5] "Total Expenses" "Marketing" "Inventory" "Budget Profit"
## [9] "Budget COGS" "Budget Margin" "Budget Sales" "Area Code"
## [13] "ProductId" "Date"dbListFields(con2, "location")## [1] "Area Code" "State" "Market" "Market Size"coffee_data <- dbGetQuery(con2, "Select * from facttable")
coffee_data## Profit Margin Sales COGS Total Expenses Marketing Inventory Budget Profit
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## 4248 120 170 290 206 3
## Date
## 1 2012-01-01 00:00:00
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## 4248 2013-12-01 00:00:00Eksplorasi Data
head(coffee_data)## Profit Margin Sales COGS Total Expenses Marketing Inventory Budget Profit
## 1 94 130 219 89 36 24 777 100
## 2 68 107 190 83 39 27 623 80
## 3 101 139 234 95 38 26 821 110
## 4 30 56 100 44 26 14 623 30
## 5 54 80 134 54 26 15 456 70
## 6 53 108 180 72 55 23 558 80
## Budget COGS Budget Margin Budget Sales Area Code ProductId
## 1 90 130 220 719 1
## 2 80 110 190 970 2
## 3 100 140 240 970 3
## 4 30 50 80 303 13
## 5 60 90 150 303 5
## 6 80 130 210 720 6
## Date
## 1 2012-01-01 00:00:00
## 2 2012-01-01 00:00:00
## 3 2012-01-01 00:00:00
## 4 2012-01-01 00:00:00
## 5 2012-01-01 00:00:00
## 6 2012-01-01 00:00:00summary(coffee_data)## Profit Margin Sales COGS
## Min. :-638.0 Min. :-302.00 Min. : 17 Min. : 0.00
## 1st Qu.: 17.0 1st Qu.: 52.75 1st Qu.:100 1st Qu.: 43.00
## Median : 40.0 Median : 76.00 Median :138 Median : 60.00
## Mean : 61.1 Mean : 104.29 Mean :193 Mean : 84.43
## 3rd Qu.: 92.0 3rd Qu.: 132.00 3rd Qu.:230 3rd Qu.:100.00
## Max. : 778.0 Max. : 613.00 Max. :912 Max. :364.00
## Total Expenses Marketing Inventory Budget Profit
## Min. : 10.00 Min. : 0.00 Min. :-3534.0 Min. :-320.00
## 1st Qu.: 33.00 1st Qu.: 13.00 1st Qu.: 432.0 1st Qu.: 20.00
## Median : 46.00 Median : 22.00 Median : 619.0 Median : 40.00
## Mean : 54.06 Mean : 31.19 Mean : 749.4 Mean : 60.91
## 3rd Qu.: 65.00 3rd Qu.: 39.00 3rd Qu.: 910.5 3rd Qu.: 80.00
## Max. :190.00 Max. :156.00 Max. : 8252.0 Max. : 560.00
## Budget COGS Budget Margin Budget Sales Area Code
## Min. : 0.00 Min. :-210.0 Min. : 0.0 Min. :203.0
## 1st Qu.: 30.00 1st Qu.: 50.0 1st Qu.: 80.0 1st Qu.:417.0
## Median : 50.00 Median : 70.0 Median : 130.0 Median :573.0
## Mean : 74.83 Mean : 100.8 Mean : 175.6 Mean :582.3
## 3rd Qu.: 90.00 3rd Qu.: 130.0 3rd Qu.: 210.0 3rd Qu.:772.0
## Max. :450.00 Max. : 690.0 Max. :1140.0 Max. :985.0
## ProductId Date
## Min. : 1.000 Length:4248
## 1st Qu.: 4.000 Class :character
## Median : 6.000 Mode :character
## Mean : 6.887
## 3rd Qu.:10.000
## Max. :13.000dim(coffee_data)## [1] 4248 14tail(coffee_data)## Profit Margin Sales COGS Total Expenses Marketing Inventory Budget Profit
## 4243 68 71 128 49 25 13 845 50
## 4244 19 32 60 24 19 7 567 20
## 4245 34 80 155 65 57 24 403 30
## 4246 76 96 188 80 45 24 1079 60
## 4247 86 104 188 72 46 23 461 80
## 4248 30 145 266 105 125 95 716 50
## Budget COGS Budget Margin Budget Sales Area Code ProductId
## 4243 40 70 110 425 9
## 4244 20 30 50 206 4
## 4245 60 80 140 509 5
## 4246 70 100 170 360 6
## 4247 80 120 200 360 2
## 4248 120 170 290 206 3
## Date
## 4243 2013-12-01 00:00:00
## 4244 2013-12-01 00:00:00
## 4245 2013-12-01 00:00:00
## 4246 2013-12-01 00:00:00
## 4247 2013-12-01 00:00:00
## 4248 2013-12-01 00:00:00names(coffee_data)## [1] "Profit" "Margin" "Sales" "COGS"
## [5] "Total Expenses" "Marketing" "Inventory" "Budget Profit"
## [9] "Budget COGS" "Budget Margin" "Budget Sales" "Area Code"
## [13] "ProductId" "Date"str(coffee_data)## 'data.frame': 4248 obs. of 14 variables:
## $ Profit : int 94 68 101 30 54 53 99 0 33 17 ...
## $ Margin : int 130 107 139 56 80 108 171 87 80 72 ...
## $ Sales : int 219 190 234 100 134 180 341 150 140 130 ...
## $ COGS : int 89 83 95 44 54 72 170 63 60 58 ...
## $ Total Expenses: int 36 39 38 26 26 55 72 87 47 55 ...
## $ Marketing : int 24 27 26 14 15 23 47 57 19 22 ...
## $ Inventory : int 777 623 821 623 456 558 1091 435 336 338 ...
## $ Budget Profit : int 100 80 110 30 70 80 110 20 40 20 ...
## $ Budget COGS : int 90 80 100 30 60 80 140 50 50 40 ...
## $ Budget Margin : int 130 110 140 50 90 130 160 80 70 70 ...
## $ Budget Sales : int 220 190 240 80 150 210 300 130 120 110 ...
## $ Area Code : int 719 970 970 303 303 720 970 719 970 719 ...
## $ ProductId : int 1 2 3 13 5 6 8 9 10 11 ...
## $ Date : chr "2012-01-01 00:00:00" "2012-01-01 00:00:00" "2012-01-01 00:00:00" "2012-01-01 00:00:00" ...Interpretasi :
Eksplorasi data dilakukan untuk melihat struktur awal dataset, termasuk jumlah data, tipe variabel, serta ringkasan statistik
Agregasi Total Sales dan Profit per Market
coffee_data <- dbGetQuery(con2, "
SELECT
l.Market,
SUM(f.Sales) AS Total_Sales,
SUM(f.Profit) AS Total_Profit
FROM facttable AS f
JOIN location AS l
ON f.`Area Code` = l.`Area Code`
GROUP BY l.Market
ORDER BY Total_Sales DESC
")
coffee_data## Market Total_Sales Total_Profit
## 1 West 272264 73996
## 2 Central 265045 93852
## 3 East 178576 59217
## 4 South 103926 32478Interpretasi :
Data kemudian diagregasi untuk menghitung total Sales dan Profit pada setiap Market. Hasil ini digunakan untuk membandingkan performa antar wilayah pasar.
Grafik Total Penjualan per Market
ggplot(coffee_data,
aes(x = reorder(Market, Total_Sales),
y = Total_Sales,
fill = Total_Sales)) +
geom_bar(stat = "identity") +
scale_fill_gradient(low = "#80CBC4", high = "#00695C") +
coord_flip() +
labs(title = "Total Penjualan per Market",
x = "Market",
y = "Total Penjualan") +
theme_minimal() +
theme(legend.position = "none")
## Grafik Total Profit per Market
library(ggplot2)
library(scales)
ggplot(coffee_data,
aes(x = reorder(Market, Total_Profit),
y = Total_Profit,
fill = Total_Profit)) +
geom_bar(stat = "identity") +
scale_fill_gradient(low = "#E1BEE7", high = "#4A148C") +
coord_flip() +
labs(title = "Total Profit per Market",
x = "Market",
y = "Total Profit") +
theme_minimal() +
theme(legend.position = "none")
## Interpretasi: Berdasarkan kedua grafik tersebut, terlihat bahwa
Market West memiliki total penjualan paling tinggi dibandingkan wilayah
lain. Namun, Market Central justru memperoleh total profit yang lebih
besar. Hal ini menunjukkan bahwa penjualan yang tinggi di Market West
tidak selalu menghasilkan keuntungan yang lebih besar, sehingga dapat
dikatakan bahwa Market Central memiliki tingkat keuntungan yang lebih
baik.
Analisis Statistika
query_corr <- "
SELECT Sales, Profit
FROM facttable
"
dt_corr <- dbGetQuery(con2, query_corr)
cor_test <- cor.test(dt_corr$Sales,
dt_corr$Profit,
method = "pearson")
cor_test##
## Pearson's product-moment correlation
##
## data: dt_corr$Sales and dt_corr$Profit
## t = 86.084, df = 4246, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7861073 0.8080289
## sample estimates:
## cor
## 0.7973309Interpretasi
Hasil korelasi Pearson sebesar 0.797 menunjukkan hubungan positif yang kuat antara Sales dan Profit. Hal ini berarti peningkatan penjualan cenderung diikuti oleh peningkatan profit pada seluruh market.
Selain itu, nilai p-value yang sangat kecil (< 0.05) menunjukkan bahwa hubungan tersebut signifikan secara statistik, sehingga hubungan ini tidak terjadi secara kebetulan melainkan benar-benar saling berkaitan satu sama lain.
Kesimpulan
Berdasarkan hasil analisis Coffee Chain Dataset, terdapat perbedaan kinerja antar market berdasarkan total penjualan dan profit, di mana Market Central dan West menunjukkan performa yang lebih tinggi dibandingkan market lainnya. Market West memiliki penjualan tertinggi, namun Market Central lebih efisien karena mampu menghasilkan profit lebih besar.
Selain itu, terdapat hubungan positif yang cukup kuat dan signifikan antara Sales dan Profit, yang berarti semakin tinggi penjualan maka profit juga cenderung meningkat.