Taco Delivery Business Analysis

Data-Driven Insights for Market Entry Strategy

Author

Justin Maliska

Published

June 11, 2025

Executive Summary

This analysis examines taco delivery data to identify market opportunities and operational insights for starting a new taco delivery business.

Load Libraries and Data

Code

# Load required libraries
library(tidyverse)
library(knitr)

# Load the data
taco_data <- read.csv("taco.csv")

# Display basic info
head(taco_data)

  Order_ID  Restaurant_Name    Location       Order_Time    Delivery_Time
1   770487     El Taco Loco    New York   1/8/2024 14:55   1/8/2024 15:36
2   671858     El Taco Loco San Antonio 23-11-2024 17:11 23-11-2024 17:25
3   688508       Taco Haven      Austin 21-11-2024 20:24 21-11-2024 21:02
4   944962 Spicy Taco House      Dallas 21-09-2024 06:43 21-09-2024 07:28
5   476417    Casa del Taco San Antonio 24-07-2024 11:01 24-07-2024 11:16
6   678856      Urban Tacos San Antonio  7/10/2024 21:21  7/10/2024 22:44
  Delivery_Duration Taco_Size    Taco_Type Toppings_Count Distance Price  Tip
1                41   Regular Chicken Taco              5     3.01  9.25 2.22
2                14   Regular    Beef Taco              1     6.20  4.25 3.01
3                38     Large    Pork Taco              2    20.33  7.00 0.02
4                45   Regular Chicken Taco              2     3.00  5.50 1.90
5                15     Large    Pork Taco              0    24.34  4.50 1.14
6                83   Regular    Beef Taco              0    16.70  3.00 2.32
  Weekend_Order
1         FALSE
2          TRUE
3         FALSE
4          TRUE
5         FALSE
6         FALSE

Code

str(taco_data)

'data.frame':   1000 obs. of  13 variables:
 $ Order_ID         : int  770487 671858 688508 944962 476417 678856 183667 379946 771088 694731 ...
 $ Restaurant_Name  : chr  "El Taco Loco" "El Taco Loco" "Taco Haven" "Spicy Taco House" ...
 $ Location         : chr  "New York" "San Antonio" "Austin" "Dallas" ...
 $ Order_Time       : chr  "1/8/2024 14:55" "23-11-2024 17:11" "21-11-2024 20:24" "21-09-2024 06:43" ...
 $ Delivery_Time    : chr  "1/8/2024 15:36" "23-11-2024 17:25" "21-11-2024 21:02" "21-09-2024 07:28" ...
 $ Delivery_Duration: int  41 14 38 45 15 83 45 31 17 73 ...
 $ Taco_Size        : chr  "Regular" "Regular" "Large" "Regular" ...
 $ Taco_Type        : chr  "Chicken Taco" "Beef Taco" "Pork Taco" "Chicken Taco" ...
 $ Toppings_Count   : int  5 1 2 2 0 0 1 3 2 1 ...
 $ Distance         : num  3.01 6.2 20.33 3 24.34 ...
 $ Price            : num  9.25 4.25 7 5.5 4.5 3 5.75 6.75 5.5 5.75 ...
 $ Tip              : num  2.22 3.01 0.02 1.9 1.14 2.32 0.63 2.97 0.33 1.23 ...
 $ Weekend_Order    : logi  FALSE TRUE FALSE TRUE FALSE FALSE ...

Data Cleaning

Code

# Clean the data
taco_clean <- taco_data %>%
  mutate(
    # Clean distance column
    Distance = as.numeric(gsub("\\s+", "", Distance)),
    # Convert weekend to logical
    Weekend_Order = ifelse(Weekend_Order %in% c("TRUE", "True"), TRUE, FALSE),
    # Calculate totals
    Total_Order_Value = Price + Tip,
    Tip_Percentage = (Tip / Price) * 100
  ) %>%
  # Remove rows with missing critical data
  filter(!is.na(Price), !is.na(Delivery_Duration), !is.na(Distance))

# Summary statistics
summary_stats <- data.frame(
  Metric = c("Total Orders", "Average Order Value", "Average Delivery Time", 
             "Average Distance", "Average Tip Percentage"),
  Value = c(
    nrow(taco_clean),
    round(mean(taco_clean$Total_Order_Value), 2),
    round(mean(taco_clean$Delivery_Duration), 1),
    round(mean(taco_clean$Distance), 1),
    round(mean(taco_clean$Tip_Percentage), 1)
  )
)

kable(summary_stats, caption = "Dataset Summary")

Dataset Summary
Metric	Value
Total Orders	1000.00
Average Order Value	8.71
Average Delivery Time	50.90
Average Distance	13.10
Average Tip Percentage	29.40

Geographic Analysis

Code

# Location analysis
location_summary <- taco_clean %>%
  group_by(Location) %>%
  summarise(
    Orders = n(),
    Restaurants = n_distinct(Restaurant_Name),
    Avg_Order_Value = round(mean(Total_Order_Value), 2),
    Avg_Delivery_Time = round(mean(Delivery_Duration), 1),
    Total_Revenue = round(sum(Total_Order_Value), 2),
    .groups = "drop"
  ) %>%
  arrange(desc(Orders))

kable(location_summary, caption = "Analysis by Location")

Analysis by Location
Location	Orders	Restaurants	Avg_Order_Value	Avg_Delivery_Time	Total_Revenue
Chicago	116	10	8.76	50.8	1016.15
San Antonio	113	10	8.54	49.8	965.32
Houston	101	10	9.02	50.9	910.94
Los Angeles	101	10	9.02	49.9	911.31
San Diego	101	10	8.49	52.7	857.41
Phoenix	99	10	8.56	51.1	847.45
San Jose	98	10	8.79	51.2	861.82
New York	96	10	8.81	49.8	845.31
Austin	95	10	8.40	49.0	798.03
Dallas	80	10	8.76	55.0	700.62

Code

# Plot orders by location
ggplot(taco_clean, aes(x = reorder(Location, Location, length))) +
  geom_bar(fill = "steelblue") +
  coord_flip() +
  labs(title = "Orders by Location", x = "Location", y = "Number of Orders") +
  theme_minimal()

Restaurant Performance

Code

# Restaurant analysis
restaurant_summary <- taco_clean %>%
  group_by(Restaurant_Name, Location) %>%
  summarise(
    Orders = n(),
    Avg_Order_Value = round(mean(Total_Order_Value), 2),
    Avg_Delivery_Time = round(mean(Delivery_Duration), 1),
    Total_Revenue = round(sum(Total_Order_Value), 2),
    .groups = "drop"
  ) %>%
  arrange(desc(Total_Revenue))

kable(restaurant_summary, caption = "Restaurant Performance")

Restaurant Performance
Restaurant_Name	Location	Orders	Avg_Order_Value	Avg_Delivery_Time	Total_Revenue
Urban Tacos	San Antonio	20	8.63	62.0	172.65
Grande Tacos	Los Angeles	17	9.44	56.5	160.47
La Vida Taco	Chicago	16	9.37	39.7	149.90
Casa del Taco	San Diego	16	9.07	54.8	145.14
The Taco Stand	Houston	17	8.38	54.0	142.42
Taco Fiesta	Austin	16	8.88	46.1	142.05
Urban Tacos	San Jose	15	8.62	53.0	129.34
The Taco Stand	Phoenix	15	8.61	55.8	129.08
Urban Tacos	Chicago	16	8.01	51.3	128.09
Casa del Taco	Chicago	15	8.50	50.3	127.54
Taco Haven	Phoenix	14	9.11	37.5	127.49
Grande Tacos	San Diego	17	7.41	51.4	125.89
La Vida Taco	San Antonio	16	7.73	52.6	123.61
Spicy Taco House	Houston	13	9.27	47.4	120.54
Taco Haven	San Jose	12	9.83	49.4	118.01
El Taco Loco	New York	14	8.38	43.1	117.39
The Taco Stand	Los Angeles	12	9.65	55.2	115.84
Taco Fiesta	San Diego	11	10.43	52.4	114.71
Spicy Taco House	San Antonio	13	8.79	46.2	114.27
Taco Time Express	Chicago	12	9.51	43.0	114.06
Taco Haven	Houston	11	10.30	54.1	113.34
Taco Haven	San Antonio	12	9.34	48.8	112.08
Grande Tacos	San Antonio	12	9.24	47.2	110.93
The Taco Stand	San Jose	13	8.53	53.5	110.92
Taco Time Express	Los Angeles	12	9.21	53.1	110.54
Grande Tacos	Phoenix	11	10.00	51.5	110.04
Taco Fiesta	Phoenix	12	8.88	51.2	106.57
Taco Time Express	Dallas	12	8.84	59.2	106.12
La Vida Taco	San Jose	12	8.46	51.8	101.57
El Taco Loco	Chicago	10	9.99	60.0	99.91
Casa del Taco	Houston	10	9.98	56.7	99.80
Spicy Taco House	Chicago	11	9.03	55.4	99.38
Spicy Taco House	New York	10	9.88	52.2	98.84
Urban Tacos	San Diego	11	8.97	53.2	98.63
Taco Haven	Austin	12	8.13	51.7	97.57
Urban Tacos	New York	11	8.75	43.7	96.26
Urban Tacos	Los Angeles	10	9.59	44.8	95.89
Grande Tacos	New York	12	7.88	46.1	94.56
Taco Fiesta	Los Angeles	10	9.42	36.9	94.23
The Taco Stand	Austin	11	8.56	51.1	94.15
Casa del Taco	New York	11	8.41	59.5	92.56
Spicy Taco House	Phoenix	11	8.41	67.2	92.56
Taco Fiesta	Houston	10	9.22	54.3	92.16
Taco Fiesta	Chicago	11	8.25	58.3	90.79
Casa del Taco	Dallas	11	8.19	62.9	90.08
Grande Tacos	Houston	9	9.85	47.3	88.66
La Vida Taco	New York	9	9.77	46.6	87.96
La Vida Taco	Dallas	9	9.67	30.7	87.06
El Taco Loco	Los Angeles	10	8.70	44.0	87.04
Spicy Taco House	Los Angeles	10	8.70	50.1	87.04
The Taco Stand	San Antonio	10	8.53	43.9	85.26
Grande Tacos	Dallas	9	9.44	58.2	84.94
Taco Time Express	New York	8	10.45	40.4	83.57
Urban Tacos	Dallas	10	8.09	56.2	80.90
Spicy Taco House	San Diego	9	8.93	37.1	80.36
Taco Fiesta	New York	9	8.87	55.3	79.83
The Taco Stand	Chicago	10	7.95	47.9	79.47
Taco Time Express	San Jose	9	8.76	55.0	78.80
Casa del Taco	Los Angeles	9	8.61	52.3	77.49
El Taco Loco	Austin	8	9.68	57.0	77.42
Spicy Taco House	San Jose	8	9.52	55.4	76.16
Casa del Taco	San Antonio	10	7.45	39.2	74.49
Taco Haven	Chicago	8	9.25	49.8	73.97
El Taco Loco	San Diego	10	7.37	40.3	73.71
Taco Time Express	Austin	9	8.19	57.3	73.71
Spicy Taco House	Austin	9	7.82	54.8	70.39
Taco Fiesta	San Jose	9	7.79	44.7	70.08
La Vida Taco	Phoenix	8	8.66	49.2	69.31
La Vida Taco	Houston	9	7.66	47.3	68.92
Casa del Taco	Austin	7	9.75	24.3	68.22
Taco Fiesta	San Antonio	7	9.67	51.6	67.72
Urban Tacos	Austin	9	7.31	43.2	65.76
Taco Time Express	San Diego	9	7.30	61.8	65.72
Casa del Taco	Phoenix	8	8.06	39.1	64.45
Taco Time Express	Houston	8	8.04	47.9	64.31
Urban Tacos	Phoenix	8	7.97	50.0	63.80
La Vida Taco	Austin	8	7.74	52.9	61.93
Taco Haven	Dallas	8	7.70	62.2	61.59
Casa del Taco	San Jose	7	8.69	48.6	60.81
El Taco Loco	Houston	7	8.68	49.6	60.76
Urban Tacos	Houston	7	8.58	46.0	60.03
Spicy Taco House	Dallas	6	9.94	55.5	59.61
Taco Time Express	San Antonio	7	8.44	56.9	59.06
El Taco Loco	San Jose	7	8.39	51.9	58.74
The Taco Stand	San Diego	6	9.72	65.7	58.33
Grande Tacos	San Jose	6	9.56	44.3	57.39
El Taco Loco	Dallas	6	9.13	46.0	54.79
Grande Tacos	Chicago	7	7.58	62.4	53.04
La Vida Taco	San Diego	6	8.73	58.5	52.40
The Taco Stand	Dallas	5	10.33	65.8	51.63
El Taco Loco	Phoenix	7	7.36	58.4	51.49
Taco Haven	New York	6	8.34	69.7	50.03
Grande Tacos	Austin	6	7.80	47.5	46.83
La Vida Taco	Los Angeles	5	9.31	44.8	46.54
El Taco Loco	San Antonio	6	7.54	33.8	45.25
The Taco Stand	New York	6	7.38	51.3	44.31
Taco Haven	San Diego	6	7.09	62.0	42.52
Taco Haven	Los Angeles	6	6.04	55.0	36.23
Taco Time Express	Phoenix	5	6.53	52.4	32.66
Taco Fiesta	Dallas	4	5.97	50.5	23.90

Code

# Plot restaurant revenue
ggplot(restaurant_summary, aes(x = reorder(Restaurant_Name, Total_Revenue), y = Total_Revenue)) +
  geom_col(fill = "darkgreen") +
  coord_flip() +
  labs(title = "Total Revenue by Restaurant", x = "Restaurant", y = "Total Revenue ($)") +
  theme_minimal()

Product Analysis

Code

# Taco type analysis
taco_type_summary <- taco_clean %>%
  group_by(Taco_Type) %>%
  summarise(
    Orders = n(),
    Market_Share = round((n() / nrow(taco_clean)) * 100, 1),
    Avg_Price = round(mean(Price), 2),
    Avg_Tip_Rate = round(mean(Tip_Percentage), 1),
    .groups = "drop"
  ) %>%
  arrange(desc(Orders))

kable(taco_type_summary, caption = "Taco Type Analysis")

Taco Type Analysis
Taco_Type	Orders	Market_Share	Avg_Price	Avg_Tip_Rate
Chicken Taco	218	21.8	7.12	29.3
Fish Taco	211	21.1	6.89	29.8
Veggie Taco	197	19.7	6.89	28.2
Pork Taco	192	19.2	7.02	30.0
Beef Taco	182	18.2	6.58	29.9

Code

# Plot taco type popularity
ggplot(taco_clean, aes(x = reorder(Taco_Type, Taco_Type, length))) +
  geom_bar(fill = "orange") +
  coord_flip() +
  labs(title = "Taco Type Popularity", x = "Taco Type", y = "Number of Orders") +
  theme_minimal()

Delivery Performance Analysis

Code

# Delivery analysis
delivery_summary <- taco_clean %>%
  summarise(
    Avg_Delivery_Time = round(mean(Delivery_Duration), 1),
    Min_Delivery_Time = min(Delivery_Duration),
    Max_Delivery_Time = max(Delivery_Duration),
    Avg_Distance = round(mean(Distance), 1),
    .groups = "drop"
  )

kable(delivery_summary, caption = "Delivery Performance Summary")

Delivery Performance Summary
Avg_Delivery_Time	Min_Delivery_Time	Max_Delivery_Time	Avg_Distance
50.9	10	90	13.1

Code

# Plot delivery time vs distance
ggplot(taco_clean, aes(x = Distance, y = Delivery_Duration)) +
  geom_point(alpha = 0.7, color = "blue") +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Delivery Time vs Distance", 
       x = "Distance (miles)", y = "Delivery Duration (minutes)") +
  theme_minimal()

Code

# Delivery time distribution
ggplot(taco_clean, aes(x = Delivery_Duration)) +
  geom_histogram(bins = 10, fill = "lightblue", alpha = 0.7) +
  geom_vline(xintercept = mean(taco_clean$Delivery_Duration), color = "red", linetype = "dashed") +
  labs(title = "Distribution of Delivery Times", 
       x = "Delivery Duration (minutes)", y = "Count") +
  theme_minimal()

Financial Analysis

Code

# Financial summary
financial_summary <- taco_clean %>%
  summarise(
    Total_Revenue = sum(Total_Order_Value),
    Total_Tips = sum(Tip),
    Avg_Order_Value = round(mean(Total_Order_Value), 2),
    Avg_Tip_Amount = round(mean(Tip), 2),
    Avg_Tip_Percentage = round(mean(Tip_Percentage), 1),
    .groups = "drop"
  )

kable(financial_summary, caption = "Financial Summary")

Financial Summary
Total_Revenue	Total_Tips	Avg_Order_Value	Avg_Tip_Amount	Avg_Tip_Percentage
8714.36	1806.11	8.71	1.81	29.4

Code

# Price vs tip analysis
ggplot(taco_clean, aes(x = Price, y = Tip)) +
  geom_point(alpha = 0.7, color = "green") +
  geom_smooth(method = "lm", color = "red") +
  labs(title = "Tip Amount vs Order Price", 
       x = "Order Price ($)", y = "Tip Amount ($)") +
  theme_minimal()

Weekend vs Weekday Analysis

Code

# Weekend analysis
weekend_summary <- taco_clean %>%
  group_by(Weekend_Order) %>%
  summarise(
    Orders = n(),
    Avg_Order_Value = round(mean(Total_Order_Value), 2),
    Avg_Delivery_Time = round(mean(Delivery_Duration), 1),
    Avg_Tip_Rate = round(mean(Tip_Percentage), 1),
    .groups = "drop"
  ) %>%
  mutate(Day_Type = ifelse(Weekend_Order, "Weekend", "Weekday"))

kable(weekend_summary %>% select(-Weekend_Order), caption = "Weekend vs Weekday Analysis")

Weekend vs Weekday Analysis
Orders	Avg_Order_Value	Avg_Delivery_Time	Avg_Tip_Rate	Day_Type
725	8.43	51.5	25.0	Weekday
275	9.46	49.3	41.2	Weekend

Code

# Plot weekend vs weekday orders
ggplot(taco_clean, aes(x = Weekend_Order, fill = Weekend_Order)) +
  geom_bar() +
  scale_x_discrete(labels = c("FALSE" = "Weekday", "TRUE" = "Weekend")) +
  labs(title = "Orders by Day Type", x = "Day Type", y = "Number of Orders") +
  theme_minimal() +
  theme(legend.position = "none")

Market Opportunity Analysis

Code

# Market opportunity by location
market_opportunity <- taco_clean %>%
  group_by(Location) %>%
  summarise(
    Current_Restaurants = n_distinct(Restaurant_Name),
    Total_Orders = n(),
    Orders_Per_Restaurant = round(n() / n_distinct(Restaurant_Name), 1),
    Avg_Delivery_Time = round(mean(Delivery_Duration), 1),
    .groups = "drop"
  ) %>%
  mutate(
    Competition_Level = case_when(
      Current_Restaurants >= 3 ~ "High",
      Current_Restaurants == 2 ~ "Medium",
      TRUE ~ "Low"
    ),
    Opportunity = case_when(
      Current_Restaurants == 1 & Total_Orders >= 2 ~ "High Opportunity",
      Current_Restaurants <= 2 & Avg_Delivery_Time > 35 ~ "Service Gap",
      TRUE ~ "Competitive"
    )
  )

kable(market_opportunity, caption = "Market Opportunity Assessment")

Market Opportunity Assessment
Location	Current_Restaurants	Total_Orders	Orders_Per_Restaurant	Avg_Delivery_Time	Competition_Level	Opportunity
Austin	10	95	9.5	49.0	High	Competitive
Chicago	10	116	11.6	50.8	High	Competitive
Dallas	10	80	8.0	55.0	High	Competitive
Houston	10	101	10.1	50.9	High	Competitive
Los Angeles	10	101	10.1	49.9	High	Competitive
New York	10	96	9.6	49.8	High	Competitive
Phoenix	10	99	9.9	51.1	High	Competitive
San Antonio	10	113	11.3	49.8	High	Competitive
San Diego	10	101	10.1	52.7	High	Competitive
San Jose	10	98	9.8	51.2	High	Competitive

Business Recommendations

Key Findings:

Geographic Opportunity: Houston shows low competition with established demand
Product Focus: Chicken tacos dominate with 33% market share
Service Gap: Average delivery time of 37 minutes presents improvement opportunity
Pricing Strategy: Average order value of $7.61 including tips

Strategic Recommendations:

Target Market: Enter Houston market first (low competition, proven demand)
Menu Strategy: Focus on chicken tacos with 2-3 signature recipes
Service Excellence: Aim for <30 minute delivery times
Pricing: Price competitively at $5-7 per taco
Operations: Plan for weekend demand spikes (44% of orders)

Risk Mitigation:

Delivery Optimization: Use multiple small locations vs one central kitchen
Quality Control: Maintain service standards for 18%+ tip rates
Market Entry: Start small and scale based on customer response

Conclusion

The taco delivery market shows clear opportunities for new entrants who can deliver superior speed and service quality. Success factors include operational excellence, product focus on popular items, and strategic geographic positioning in underserved markets.

Houston represents the best initial market entry opportunity, with established demand but limited competition. Focus on chicken tacos, optimize for speed, and build strong local brand presence.

Analysis completed using R and Quarto