South Dakota State University
Assignment 12
Ecuadorian Sales Analysis
Judge Eli and Valerie Janis
STAT-442-ST1
FA2024 Semester
November 25, 2024
Abstract
This report explores sales patterns and trends for Favorita stores across Ecuador using extensive transactional data. The analysis examines factors such as geographical distribution of stores, temporal sales trends, product category performance, and store type growth. Quantitative analyses investigate the impact of promotions and oil price fluctuations on sales. Key findings include the importance of population density in store location strategy, the seasonal and yearly trends driving sales, and the significant influence of promotional activity on revenue. Additionally, oil price volatility is shown to negatively impact sales, underscoring the economic sensitivity of the region. This report provides actionable insights for optimizing marketing strategies, inventory management, and regional store performance.
Dataset Overview
This dataset contains information about millions of sales sold at Favorita stores located in Ecuador. The data includes dates, store and product information, whether that item was being promoted, as well as the sales and transaction numbers.
Variables
id
date
store_nbr: Identifies the store at which the products are sold.
product_category: Identifies the type of product sold.
sales: Gives the total sales for a product category at a particular store at a given date. Fractional values are possible since products can be sold in fractional units (1.5 kg of cheese, for instance, as opposed to 1 bag of chips).
items_promoted: Gives the total number of items in a product category that were being promoted at a store on a given date.
transactions: Gives the total number of transactions at a store on a given date.
city: Store metadata for the city.
state: Store metadata for the state.
store_type: Store metadata for the type.
store_cluster: Is a grouping of similar stores.
oil_price: Daily oil price of “dcoilwtico” reflecting the cost per barrel in U.S. dollars. Ecuador is an oil-dependent country and it’s economical health is highly vulnerable to shocks in oil prices.
event_type: Categorical variable indicating the category of the event (e.g., Disaster, Holiday, Shopping Event, or Sport Event).
event_description: Detailed description of the event corresponding to its type (e.g., Navidad, Mundial de fútbol, or Carnaval).
Additional Notes
Wages in the public sector are paid every two weeks on the 15th and on the last day of the month. Supermarket sales could be affected by this.
A magnitude 7.8 earthquake struck Ecuador on April 16, 2016. People rallied in relief efforts donating water and other first need products which greatly affected supermarket sales for several weeks after the earthquake.
Dataset Structure
Data Frame Summary
dataset
Dimensions: 3000888 x 14Duplicates: 0
| Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| date [Date] |
|
1684 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| store_nbr [numeric] |
|
54 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| product_category [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| sales [numeric] |
|
379610 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| items_promoted [numeric] |
|
362 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| transactions [numeric] |
|
4993 distinct values |  |
245784 (8.2%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| city [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| state [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| store_type [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| store_cluster [character] |
|
|
 |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| oil_price [numeric] |
|
1352 distinct values |  |
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| event_type [character] |
|
|
 |
2802030 (93.4%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| event_description [character] |
|
|
 |
2802030 (93.4%) |
Generated by summarytools 1.0.1 (R version 4.4.0)
2024-11-21
Exploratory Data Analysis
1) Map of Ecuador Showing Store Locations by City Population
Geographically map the store locations across Ecuador while incorporating population data for each city.
Interpretation
The map displays store locations across Ecuador, with point sizes representing city populations. Larger points, concentrated in cities like Quito and Guayaquil, indicate higher populations and likely strategic placement to maximize customer reach. Smaller points highlight stores in less populated areas, suggesting limited market demand or potential opportunities for expansion. By combining store locations and population data, the visualization effectively shows coverage and identifies regions that may benefit from further market exploration.
2) Trends in Total Transactions Over Time by State
Analyze transaction trends over time by state to identify patterns and variations.
Interpretation
This line chart tracks total transactions over time, segmented by state. The most prominent pattern is the consistent periodic spikes in transaction volumes, reflecting seasonality, likely due to events such as holidays or payday effects. States like Pichincha show significantly higher transaction levels compared to others, highlighting regional differences in consumer activity. The visualization simplifies complex time-series data into state-wise trends, allowing for quick identification of high-performing regions and periods of heightened activity. This data can inform targeted marketing strategies and inventory planning to optimize store performance across states.
3) Average Daily Sales by Product Category
Identify the contribution of different product categories to average daily sales.
Interpretation
The bar chart illustrates the average daily sales for various product categories, highlighting Grocery I as the top-performing category, with significantly higher sales compared to others. Categories such as Beverages, Produce, and Cleaning also contribute substantially, while some categories like Automotive and Baby Care have minimal sales. This breakdown helps prioritize key product categories that drive revenue and identify low-performing categories that may require strategic adjustments or promotion.
4) Average Daily Sales Per Year by Store Type
Compare yearly growth in average daily sales across different store types.
Interpretation
This line chart shows the trend of average daily sales for each store type from 2013 to 2017. Store Type A consistently outperforms others, with significant growth over the years, while Types B, D, and E follow with more gradual increases. Store Type C shows the slowest growth, maintaining relatively low sales levels. This indicates that Store Type A may cater to higher demand or operate in more profitable locations, while lower-performing types may benefit from targeted strategies to boost sales. The chart highlights the importance of store type in overall revenue growth.
5) Trends in Average Sales by Day, Month, Year, and Week
Examine sales patterns across different time intervals to identify trends and seasonality.
Interpretation
The set of charts reveals sales trends over various time intervals. Sales are highest on Sundays, steadily increasing from Monday, suggesting weekends drive customer activity. December shows the highest monthly sales, likely due to holiday shopping, with a gradual increase from January. Yearly sales demonstrate consistent growth from 2013 to 2017, reflecting overall expansion. Weekly sales maintain a consistent pattern but exhibit slight peaks during specific weeks. These trends highlight key periods for maximizing sales efforts and planning inventory, particularly for weekends, December, and steadily increasing yearly demand.
Quantitative Analysis
6) Relationship Between Sales and Promotions
Determine the correlation between average sales and promotional activity over time.
## [1] "Correlation between sales and promotions: 0.575"
Interpretation
The scatter plot shows a positive correlation (r = 0.575) between average sales and the number of promotions, indicating that higher promotional activity generally leads to increased sales. The line chart on the right demonstrates the growth of promotions over time, with a noticeable increase starting around the middle of 2014. While promotions are effective in boosting sales, the correlation suggests other factors may also influence sales. This analysis highlights the importance of strategic promotions in driving revenue but also suggests the need to explore complementary strategies.
7) Relationship Between Sales and Oil Prices
Assess the impact of oil price fluctuations on average sales over time.
## [1] "Correlation between sales and oil prices: -0.628"
Interpretation
The scatter plot indicates a negative correlation (r = -0.628) between average sales and oil prices, suggesting that higher oil prices are associated with lower sales. The line chart shows a significant drop in oil prices from mid 2014 to 2016, which may have contributed to economic challenges influencing consumer spending. This relationship highlights the sensitivity of sales to fluctuations in oil prices, particularly in oil-dependent economies like Ecuador. Understanding this correlation can help businesses anticipate and adapt to market conditions during periods of volatile oil prices.
Conclusion
This analysis highlights several critical insights for Favorita stores in Ecuador. Store locations are strategically aligned with population density, but underserved regions offer potential for expansion. Temporal trends reveal the importance of weekends and December for maximizing sales, while year-over-year growth reflects successful long-term strategies. Product categories like Grocery I and store types like Type A consistently outperform others, providing a focus for revenue optimization. Promotions are shown to positively correlate with sales, but oil price volatility poses challenges to consumer spending. Addressing these economic sensitivities and leveraging high-performing categories and strategies can drive future growth. Further research could explore specific regional needs and consumer behaviors to refine these strategies.