In 2024, Target's sales decreased by approximately 0.8 percent when compared to the previous year. As of that year, Target had net sales of 106.6 billion U.S. dollars.

Target reported $25.21B in Sales Revenues for its fiscal quarter ending in August of 2025. Data for Target | TGT - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Target reported $18.54B in Cost of Sales for its fiscal quarter ending in August of 2025. Data for Target | TGT - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last August in 2025.

target.com, operated by Target Corporation, is an online store with nationally-focused sales. Its eCommerce net sales are generated almost entirely in the United States. With regards to the product range, target.com is an all-round online store, with products on offer that cover different categories, such as “Toys, Hobby & DIY”, “Electronics & Media” as well as “Furniture & Appliances”. The online store was launched in 1999. *Forecast

Target current p/s ratio as of July 09, 2025 is 0.45. Target average p/s ratio for 2024 was 0.63, a 8.62% decline from 2023. Target average p/s ratio for 2023 was 0.58, a 27.5% increase from 2022. Target average p/s ratio for 2022 was 0.8, a 22.33% decline from 2021. P/s ratio can be defined as the price to sales or PS ratio is calculated by taking the latest closing price and dividing it by the most recent sales per share number. The PS ratio is an additional way to assess whether a stock is over or under valued and is used primarily in cases where earnings are negative and the PE ratio cannot be utilized.

Target reported $6.68B in Gross Profit on Sales for its fiscal quarter ending in July of 2025. Data for Target | TGT - Gross Profit On Sales including historical, tables and charts were last updated by Trading Economics this last August in 2025.

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United States JR: Same-Store Sales: TD: Target Corp data was reported at 3.000 % in Apr 2018. This records a decrease from the previous number of 3.600 % for Jan 2018. United States JR: Same-Store Sales: TD: Target Corp data is updated quarterly, averaging 1.600 % from Jan 2011 (Median) to Apr 2018, with 30 observations. The data reached an all-time high of 5.300 % in Apr 2012 and a record low of -2.500 % in Jan 2014. United States JR: Same-Store Sales: TD: Target Corp data remains active status in CEIC and is reported by Redbook Research Inc.. The data is categorized under Global Database’s USA – Table US.H012: Johnson Redbook Same-Store Sales Index: Quarterly: YoY%.

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

Target is focusing on driving sales from digital channels, for instance, it has been striving to develop a digital assistant for its mobile application, which can provide recommendations to customers based on their search patterns and purchase history. Read More

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Key Features & Stats:

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Company Insights: Size, Revenue, Funding data, Industry, and Tech Stack for a complete profile.

Location: HQ and regional offices to target local, national, or international markets.

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As of February 1, 2025, the end of the 2024 financial year, Target had a total of 1,978 stores open throughout the United States. This was an increase of 22 from a year earlier. Target Corporation operates a chain of general merchandise stores, which offer a wide variety of general merchandise and food products to their customers. The company has operated primarily in the United States since its inception. Target Corporation is one of the most valuable retail brands in the world. The development of an American retail giant The company started out as the Dayton Dry Goods Company in 1902, later changing its name to the Dayton Company, but more commonly known as Dayton’s. The company was run under the Dayton-Hudson Corporation banner up until the year 2000, when it was renamed Target Corporation. The company spread across the United States and even entered the Canadian market for a brief period, but all of the company’s Canadian stores were closed in 2015. In financial year 2024, Target had revenues amounting to approximately 106.6 billion U.S. dollars, making it one of the leading American retailers. What does Target sell? Target Corporation sells a wide range of goods, such as food, apparel, household essentials, and seasonal offerings, as well as many others. As of 2024, the majority of sales came from the food and beverage segment. The company also sells products online, through target.com. In 2024, the share of Target's online sales amounted to almost 20 percent.

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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TA 35 Index Target Price Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Real estate sale cases actual transaction registration information, including target location (de-identification), area, total price, and other information. (Linkou District)

Real estate sales cases real price registration information, including target location (de-identified), area, total price, and other information. - Ruifang District

United States - Retail Sales: Jewelry Stores was 29.50000 % Chg. from Preceding Period in February of 2021, according to the United States Federal Reserve. Historically, United States - Retail Sales: Jewelry Stores reached a record high of 204.70000 in May of 2020 and a record low of -79.40000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Retail Sales: Jewelry Stores - last updated from the United States Federal Reserve on August of 2025.

In 2024, holiday retail sales in the United States were forecast to reach about ***** billion U.S. dollars. This figure was given as a conservative value; retail sales over the holiday season was projected to be between ***** billion U.S. dollars to *** billion U.S. dollars in 2024. Holiday retail sales have risen substantially since the turn of the century, with holiday retail sales amounting to approximately *** billion U.S. dollars back in 2002. Holiday retail sales are a fraction of total retail sales in the United States which were around ************** U.S. dollars in 2023. Holiday season e-commerce is also on the rise, with increasing numbers of retailers and consumers going digital. What makes up the winter holiday season in the United States? The winter holiday season includes shopping occasions such as Thanksgiving weekend - which is made up of Black Friday, Small Business Saturday, and Cyber Monday, Super Saturday – the last Saturday before Christmas, and Christmas itself. Thanksgiving weekend is a very popular time for consumers to partake in holiday shopping. In 2022, over *** million U.S. consumers shopped on Black Friday. Leading companies in U.S. retail The domestic retail market in the United States is very competitive, with many companies recording substantial retail sales. Walmart, a retail chain offering low prices and a wide selection of products, is the leading retailer in the United States. Amazon, The Kroger Co., Costco, and Target are a selection of other leading U.S. retailers.

In 2024, U.S. retail sales of toys in the United States amounted to an estimated ** billion U.S. dollars, a slight drop compared to the previous year. The toys and games market consists of total revenues generated through the sale of action figures, dolls, games and puzzles, plush toys, vehicles, and other toys. Toy Industry Play is a child's "work" and toys are the tools children use in play. Toys do more than entertain and keep children occupied. Properly chosen, they should aid a child's physical, mental, social, and emotional development. Play is universally recognized as a vital part of learning and growing and, because toys are such an important ingredient of play, they are invaluable to a child's development into a mature, confident adult.No less today than through the history of civilization, toys reflect the times and cultures and provide children with the tools that help them relate to the world in which they live. Today's toy manufacturers keep pace with the rapidly changing world and provide youngsters with correspondingly appropriate playthings for their enjoyment and to challenge their creativity and imagination. Video games Toy and game market growth is being fueled by video, console, and computer games, with the industry also benefiting from a growing adult consumer base as this group takes a greater interest in games as a popular leisure pursuit. Video game industry leaders are focusing their marketing efforts on teenagers and adults, with young children no longer being considered the industry’s main target demographic.

United States - Retail Sales: Grocery Stores was 76181.00000 Mil. of $ in June of 2025, according to the United States Federal Reserve. Historically, United States - Retail Sales: Grocery Stores reached a record high of 76181.00000 in June of 2025 and a record low of 27338.00000 in March of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Retail Sales: Grocery Stores - last updated from the United States Federal Reserve on August of 2025.