FOH KPI (Dinner April 11-16)

Appetizers and Sharables vs Total Sales

Note: Rank based on Appetizers and Sharables sales

dinner_dine_in <- dinner %>%
  filter(`Dining Option` == "Dine In")
total_sales <- dinner_dine_in %>%
  group_by(Server) %>%
  summarize(Total_Sales = sum(`Net Price`))

app_sales <- dinner_dine_in %>%
  filter(`Menu Group` %in% c("Appetizers & Salads (D)","Sharables (D)","Appetizers & Salads","All Night Happy Hour","Happy Hour")) %>%
  group_by(Server) %>%
  summarise(App_Sales = sum(`Net Price`)) %>%
  left_join(total_sales, app_sales, by = "Server") %>%
  mutate(App_Sales_Percentage = round(App_Sales/Total_Sales *100, 2)) %>%
  arrange(desc(App_Sales))
as.data.frame(app_sales)

##                       Server App_Sales Total_Sales App_Sales_Percentage
## 1                    Kyle Vu     454.0     3026.48                15.00
## 2                Brian Diggs     411.0     3326.00                12.36
## 3           Emily Willenborg     294.0     2051.25                14.33
## 4             Brittany Knott     279.0     1527.50                18.27
## 5                 Amy Nguyen     256.0     1272.00                20.13
## 6              Delaney Walsh     227.2     1134.20                20.03
## 7          Amy (Linh) Nguyen     193.0     1015.50                19.01
## 8            Emily Cipollone     164.0     1038.65                15.79
## 9  Christiana Kummer-Salazar      94.0      914.25                10.28
## 10               Katey Peace      54.0      254.50                21.22
## 11               Adam Kanoza      21.0       27.75                75.68
## 12            Edward Conklin       0.0        0.00                  NaN
## 13                Elena Hand       0.0        0.00                  NaN

NA Beverages vs Total Sales

Note: Rank based on NA Beverages

dinner_dine_in <- dinner %>%
  filter(`Dining Option` == "Dine In")
total_sales <- dinner_dine_in %>%
  group_by(Server) %>%
  summarize(Total_Sales = sum(`Net Price`))


NA_beverage_sales <- dinner_dine_in %>%
  filter(`Menu Group` == "NA Beverages") %>%
  group_by(Server) %>%
  summarise(NA_Beverage_Sales = sum(`Net Price`)) %>%
  left_join(total_sales, NA_beverage_sales, by = "Server") %>%
  mutate(NA_Beverages_Percentage = round(NA_Beverage_Sales/Total_Sales *100, 2)) %>%
  arrange(desc(NA_Beverage_Sales))
as.data.frame(NA_beverage_sales)

##                       Server NA_Beverage_Sales Total_Sales
## 1           Emily Willenborg              99.5     2051.25
## 2                Brian Diggs              98.5     3326.00
## 3                    Kyle Vu              90.5     3026.48
## 4             Brittany Knott              65.0     1527.50
## 5                 Amy Nguyen              63.0     1272.00
## 6          Amy (Linh) Nguyen              42.5     1015.50
## 7            Emily Cipollone              41.5     1038.65
## 8  Christiana Kummer-Salazar              32.0      914.25
## 9                Katey Peace              22.0      254.50
## 10             Delaney Walsh              13.0     1134.20
##    NA_Beverages_Percentage
## 1                     4.85
## 2                     2.96
## 3                     2.99
## 4                     4.26
## 5                     4.95
## 6                     4.19
## 7                     4.00
## 8                     3.50
## 9                     8.64
## 10                    1.15

Alcohol Sales vs Total Sales

Note: Rank based on Alcohol sales

dinner_dine_in <- dinner %>%
  filter(`Dining Option` == "Dine In")
total_sales <- dinner_dine_in %>%
  group_by(Server) %>%
  summarize( Total_Sales = sum(`Net Price`))

drink_sales <- dinner_dine_in %>%
  filter(`Sales Category` %in% c("Liquor","Draft Beer", "Wine","Bottled Beer"))%>%
  group_by(Server) %>%
  summarise(Alcohol_Sales = sum(`Net Price`)) %>%
  left_join(total_sales, drink_sales, by = "Server") %>%
  mutate(Alcohol_Sales_Percentage = round(Alcohol_Sales/Total_Sales *100, 2)) %>%
  arrange(desc(Alcohol_Sales))
as.data.frame(drink_sales)

##                       Server Alcohol_Sales Total_Sales Alcohol_Sales_Percentage
## 1                Brian Diggs        608.50     3326.00                    18.30
## 2                    Kyle Vu        527.99     3026.48                    17.45
## 3              Delaney Walsh        289.00     1134.20                    25.48
## 4           Emily Willenborg        227.00     2051.25                    11.07
## 5             Brittany Knott        212.00     1527.50                    13.88
## 6  Christiana Kummer-Salazar        206.50      914.25                    22.59
## 7                 Amy Nguyen        171.50     1272.00                    13.48
## 8          Amy (Linh) Nguyen        107.00     1015.50                    10.54
## 9            Emily Cipollone         90.00     1038.65                     8.67
## 10               Katey Peace         11.00      254.50                     4.32
## 11               Adam Kanoza          6.00       27.75                    21.62
## 12                Bao Nguyen          0.00        0.00                      NaN