In 2024, 80.4 percent of Target Corporation's sales came from physical stores, a slight decrease from the previous year. As of 2023, Target accounted for roughly 1.9 percent of U.S. retail e-commerce sales.

Target Corporation operates a chain of general merchandise stores, which offer a wide variety of general merchandise and food to their customers. The company has operated primarily in the United States since its inception. Target did have a number of stores in operation in the Canadian market, but these were all closed in 2015. Target Corporation had net sales amounting to approximately 106.6 billion U.S. dollars in 2024, making it one of the leading American retailers. 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 brief period. Target Corporation has an impressive number of stores in the United States. Today, Target is one of the most valuable retail brands in the world. Where does Target’s revenue come from? Target Corporation sells a wide range of goods, such as food, apparel, household essentials, and seasonal offerings, as well as many others. The company also sells products online, through target.com. Target’s sales share is quite evenly distributed, with several high revenue segments.

United States - E-Commerce Retail Sales as a Percent of Total Sales was 16.30% in April of 2025, according to the United States Federal Reserve. Historically, United States - E-Commerce Retail Sales as a Percent of Total Sales reached a record high of 16.30 in April of 2020 and a record low of 0.60 in October of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - E-Commerce Retail Sales as a Percent of Total Sales - last updated from the United States Federal Reserve on December of 2025.

Data Set Description This dataset simulates a retail environment with a million rows and 100+ columns, covering customer information, transactional data, product details, promotional information, and customer behavior metrics. It includes data for predicting total sales (regression) and customer churn (classification).

Detailed Column Descriptions Customer Information:

customer_id: Unique identifier for each customer. age: Age of the customer. gender: Gender of the customer (e.g., Male, Female, Other). income_bracket: Income bracket of the customer (e.g., Low, Medium, High). loyalty_program: Whether the customer is part of a loyalty program (Yes/No). membership_years: Number of years the customer has been a member. churned: Whether the customer has churned (Yes/No) - Target for classification. marital_status: Marital status of the customer. number_of_children: Number of children the customer has. education_level: Education level of the customer (e.g., High School, Bachelor's, Master's). occupation: Occupation of the customer. Transactional Data:

transaction_id: Unique identifier for each transaction. transaction_date: Date of the transaction. product_id: Unique identifier for each product. product_category: Category of the product (e.g., Electronics, Clothing, Groceries). quantity: Quantity of the product purchased. unit_price: Price per unit of the product. discount_applied: Discount applied on the transaction. payment_method: Payment method used (e.g., Credit Card, Debit Card, Cash). store_location: Location of the store where the purchase was made. Customer Behavior Metrics:

avg_purchase_value: Average value of purchases made by the customer. purchase_frequency: Frequency of purchases (e.g., Daily, Weekly, Monthly, Yearly). last_purchase_date: Date of the last purchase made by the customer. avg_discount_used: Average discount percentage used by the customer. preferred_store: Store location most frequently visited by the customer. online_purchases: Number of online purchases made by the customer. in_store_purchases: Number of in-store purchases made by the customer. avg_items_per_transaction: Average number of items per transaction. avg_transaction_value: Average value per transaction. total_returned_items: Total number of items returned by the customer. total_returned_value: Total value of returned items. Sales Data:

total_sales: Total sales amount for each customer over the last year - Target for regression. total_transactions: Total number of transactions made by each customer. total_items_purchased: Total number of items purchased by each customer. total_discounts_received: Total discounts received by each customer. avg_spent_per_category: Average amount spent per product category. max_single_purchase_value: Maximum value of a single purchase. min_single_purchase_value: Minimum value of a single purchase. Product Information:

product_name: Name of the product. product_brand: Brand of the product. product_rating: Customer rating of the product. product_review_count: Number of reviews for the product. product_stock: Stock availability of the product. product_return_rate: Rate at which the product is returned. product_size: Size of the product (if applicable). product_weight: Weight of the product (if applicable). product_color: Color of the product (if applicable). product_material: Material of the product (if applicable). product_manufacture_date: Manufacture date of the product. product_expiry_date: Expiry date of the product (if applicable). product_shelf_life: Shelf life of the product (if applicable). Promotional Data:

promotion_id: Unique identifier for each promotion. promotion_type: Type of promotion (e.g., Buy One Get One Free, 20% Off). promotion_start_date: Start date of the promotion. promotion_end_date: End date of the promotion. promotion_effectiveness: Effectiveness of the promotion (e.g., High, Medium, Low). promotion_channel: Channel through which the promotion was advertised (e.g., Online, In-store, Social Media). promotion_target_audience: Target audience for the promotion (e.g., New Customers, Returning Customers). Geographical Data:

customer_zip_code: Zip code of the customer's residence. customer_city: City of the customer's residence. customer_state: State of the customer's residence. store_zip_code: Zip code of the store. store_city: City where the store is located. store_state: State where the store is located. distance_to_store: Distance from the customer's residence to the store. Seasonal and Temporal Data:

holiday_season: Whether the transaction occurred during a holiday season (Yes/No). season: Season of the year (e.g., Winter, Spring, Summer, Fall). weekend: Whether the transaction occurred on a weekend (Yes/No). Customer Interaction Data:

customer_support_calls: Number of calls made to customer support. email_subscription...

In the time period between 2017 and 2020, the US-based retail corporation Target experienced the largest year-over-year sales growth in 2020, when the company's sales increased by **** percent compared to the previous year. Target is estimated to have a *** percent growth in sales in 2021 compared to sales in 2020. Forecast suggests that the company is to increase its sales by *** percent in 2026.

United States - E-Commerce Retail Sales as a Percent of Total Sales was 17.90% in October of 2024, according to the United States Federal Reserve. Historically, United States - E-Commerce Retail Sales as a Percent of Total Sales reached a record high of 17.90 in October of 2024 and a record low of 0.70 in October of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - E-Commerce Retail Sales as a Percent of Total Sales - last updated from the United States Federal Reserve on December of 2025.

Snapshot img

The online retail market share in the US is expected to increase to USD 460.13 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 11.64%.

The report extensively covers online retail market in the US segmentation by the following:

Product - Apparel, footwear, and accessories, consumer electronics and electricals, food and grocery, home furniture and furnishing, and others
Device - Smartphones and tablets and PCs

The US online retail market report offers information on several market vendors, including Amazon.com Inc., Apple Inc., Best Buy Co. Inc., Costco Wholesale Corp., eBay Inc., Kroger Co., Target Corp., The Home Depot Inc., Walmart Inc., and Wayfair Inc. among others.

This online retail market in the US research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches.

What will the Online Retail Market Size in the US be During the Forecast Period?

Download the Free Report Sample to Unlock the Online Retail Market Size in the US for the Forecast Period and Other Important Statistics

Online Retail Market in the US: Key Drivers, Trends, and Challenges

The growing seasonal and holiday sales is notably driving the online retail market growth in the US, although factors such as transportation and logistics may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the online retail industry in the US. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key US Online Retail Market Driver

The growing seasonal and holiday sales is one of the key drivers supporting the US online retail market growth. For instance, from November 1 to December 24, e-commerce sales in the US increased by 11% in 2021, when compared to a massive 47.2% growth in the holiday season of 2020. E-commerce sales made up 20.9 % of total retail sales in the holiday season of 2021, slightly higher than 20.6 percent in 2020. Thanksgiving, Black Friday, and Cyber Monday are the days that see a high amount of online shopping. Apparel, footwear and accessories, consumer electronics, computer hardware, and toys are the largest gaining product categories during the holiday season. Consumers in the US spent $204.5 billion online in November and December 2021, up 8.6% over the same period in 2020. Such exciting sales and offers are driving the market growth.

Key US Online Retail Market Trend

Omni-channel retailing is one of the key US online retail market trends fueling the market growth. It is rapidly becoming the norm for many retailers in the US. It offers consumers the option to shop online and pick up the merchandise from the store nearest to their location on the same day. Retailers are observing a high web influence on their in-store sales. For instance, Best Buy is integrating its offline and online stores to boost revenues. As a part of its omnichannel strategy, the retailer is utilizing physical stores as distribution centers for online purchases. According to Best Buy, 40% of its online shoppers prefer picking up their purchases from physical stores. Best Buy also challenges online and discount retailers with its match-to-price strategy, claiming to offer gadgets at or below the price offered by competitors. Such strategies are expected to boost market growth during the forecast period.

Key US Online Retail Market Challenge

Transportation and logistics are some of the factors hindering the US online retail market growth. Product procurement or sourcing, shipment of ordered items, and delivery to customers are the three major processes where the intervention of transportation and logistics come into the picture. All these processes require a high investment of both time and money, which challenges the efficiency and effectiveness of retailers and their costing strategies. The higher cost incurred from transportation and logistics reduces the margin of retailers, and most of the time, retailers are unable to break even. Between rising fuel prices, driver shortages, as well as a governmental and societal push for increased digitization and sustainability, transport and logistics will continue to be under a lot of pressure. Such factors will negatively impact the market growth during the forecast period.

This online retail market in the US analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2022-2026.

Who are the Major Online Retail Market Vendors in the US?

The report analyzes the market’s competitive landscape and offers information on several market vendors, includi

In the financial year 2024, 22.36 percent of Target Corporation's merchandise sales corresponded to the food and beverage segment. Meanwhile, household essentials represented 17.47 percent of the total merchandise sales. Merchandise sales represent the vast majority of Target's revenues. The company also has other streams of revenue, including credit card profit-sharing income from their arrangement with the TD Bank Group. Beauty at Target In a 2024 survey among Generation Z in the United States, 10 percent of teenage girls named Target as a shopping destination they visited to buy beauty products. This may not sound high, but it earned Target third place of all shops in the country, ahead of other major retailers Walmart and Amazon. It was, however, a considerable distance behind the two most popular destinations, specialist beauty brands Sephora and Ulta. These findings are reflected in a different study of the same retailers, with Target having the third lowest average age of female beauty consumers, at 44 years old. Gen Z clothing purchases There is also a large Generation Z market available to Target in the clothing category. In 2023, Gen Z consumers voted big box stores, such as Target and Walmart, as the second most popular shopping destination for apparel, with 16 percent of responses. This was only one percentage point behind online stores.

United States - Advance Retail Sales: Retail Trade was 624258.00000 Mil. of $ in March of 2025, according to the United States Federal Reserve. Historically, United States - Advance Retail Sales: Retail Trade reached a record high of 691312.00000 in December of 2024 and a record low of 130683.00000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Advance Retail Sales: Retail Trade - last updated from the United States Federal Reserve on December of 2025.

Economic Target: Retail Sales of Consumer Goods: Expected Growth: Jilin data was reported at 6.000 % in 2025. This stayed constant from the previous number of 6.000 % for 2024. Economic Target: Retail Sales of Consumer Goods: Expected Growth: Jilin data is updated yearly, averaging 11.000 % from Dec 2010 (Median) to 2025, with 8 observations. The data reached an all-time high of 17.000 % in 2012 and a record low of 6.000 % in 2025. Economic Target: Retail Sales of Consumer Goods: Expected Growth: Jilin data remains active status in CEIC and is reported by The Central People's Government. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Target: Retail Sales of Consumer Goods.

Economic Target: Retail Sales of Consumer Goods: Expected Growth: Chongqing data was reported at 5.000 % in 2025. This records a decrease from the previous number of 6.000 % for 2023. Economic Target: Retail Sales of Consumer Goods: Expected Growth: Chongqing data is updated yearly, averaging 7.250 % from Dec 2003 (Median) to 2025, with 10 observations. The data reached an all-time high of 15.000 % in 2013 and a record low of 5.000 % in 2025. Economic Target: Retail Sales of Consumer Goods: Expected Growth: Chongqing data remains active status in CEIC and is reported by The Central People's Government. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OT: Target: Retail Sales of Consumer Goods.

The Retail Trade sector entered 2025 on a muted footing, with revenue growth of just 0.2% to reach $7.4 trillion. E-commerce remains a bright spot, with steady mid-single-digit gains in recent years, boosted by younger consumers' strong preference for digital channels. Yet, the sector's gains in digital shopping are balanced by ongoing challenges in discretionary spending, high operating costs and tariffs that threaten earnings. Profit has been pressured by steep price competition online and inflation-related expenses, though essential retailers in sub-sectors like food and health have managed steadier performance. Current efforts around omnichannel strategies, technology-driven efficiencies and sustainability reflect the sector's dual focus: capturing digital momentum while offsetting erosion in traditional store-based sales. Over the current period, the sector's revenue expanded at a modest CAGR of 2.2%, highlighting how the pandemic's volatility gave way to cautious but relatively stable expansion. Revenue streams benefited from major operations like Target, Walmart and Amazon reshaping retail into one-stop ecosystems that blend products and services, diversifying into groceries, healthcare, beauty and wellness. Automation adoption--from self-checkout kiosks to advanced inventory management--helped mitigate rising wage costs and sharpened efficiency, while marketing automation improved customer engagement through more tailored promotions. Still, profit took hits from inflation, heightened competition and consumers trading down to value alternatives amid tightening budgets. Consumer priorities for sustainability have altered market dynamics, leading to investments in resale programs and greener programs. The sector's growth is expected to slow, with revenue climbing at an anticipated 1.3% CAGR through 2030, reaching $7.9 trillion. While consumer disposable income is set to strengthen modestly, fragile sentiment from inflation, tariffs and labor market uncertainty may temper spending power. Technology will be a key driver in reshaping operations and growth opportunities. AI is poised to enhance inventory control, price optimization, delivery logistics and fraud prevention. Extended reality innovations, from AR try-ons to immersive VR shopping, will engage younger consumers and potentially redefine customer experiences, though costs and adoption hurdles remain. Reverse logistics and the circular economy will gain ground as sustainability priorities align with value-seeking behavior. Discounters and warehouse clubs are expected to capture share in the near term as households continue trading down, though specialty and discretionary retail could stage a rebound later in the outlook period as consumer confidence improves.

The GlobalMart Supermarket Sales Dataset contains 5,000 transaction records representing sales operations across multiple international regions. It reflects the activities of a supermarket chain that operates in North America, South America, Europe, and Asia, offering a broad mix of products in categories such as electronics, furniture, and home appliances. Each record captures details of the sales process, including order transactions, customer segments, sales representatives, teams, product categories, and financial performance metrics. With fields covering revenue, sales targets, deal sizes, and sales pipeline stages, the dataset supports analysis of critical key performance indicators (KPIs) such as revenue growth, regional comparisons, target achievement, and conversion rates. The inclusion of region and country fields enables geographic mapping of sales trends at both macro (regional) and micro (country) levels. Similarly, sales team and representative fields allow for performance evaluation at an individual and team scale.

• Order_ID – Unique identifier for each order/transaction
• Order_Date – Date of the order (YYYY-MM-DD format)
• Region – Sales region (North, South, East, West)
• Country – Country within the region (e.g., USA, Canada, India, Brazil, UK, Germany)
• Sales_Rep – Name of the sales representative handling the transaction
• Team – Assigned sales team (Team A, Team B, Team C)
• Customer_ID – Unique customer identifier
• Customer_Segment – Customer type/segment (Retail, Corporate, SME)
• Product_Category – Broad category of the product sold (Electronics, Furniture, Appliances)
• Product_Name – Specific product name (e.g., Laptop Pro X, Office Chair, Refrigerator)
• Stage – Sales pipeline stage (Won, Lost, Opportunity)
• Units_Sold – Number of units sold (if won or opportunity)
• Revenue – Total revenue generated from the transaction
• Target – Sales target linked to the order or reporting period
• Deal_Size – Value of the sales deal (useful for pipeline analysis and averages)

Overview

This dataset is a comprehensive time-series resource designed for advanced demand forecasting and integrated retail analytics. It combines granular, store- and department-level Weekly Sales with a rich set of external features, including: - Promotional activity (MarkDowns) - Macroeconomic indicators (CPI, Unemployment) - Competitive response variables

This dataset is ideal for machine learning projects focused on: - Optimizing store performance - Quantifying the ROI of promotions - Understanding consumer demand sensitivity to economic shifts

Data Structure and Variables

The dataset merges three distinct files, providing over three years (2010–2013) of weekly observations across 45 stores and 99 departments.

1. Sales Data (sales data-set.csv)

2. Feature Data (Features data set.csv)

This table provides weekly economic and promotional features at the store level, crucial for modeling demand drivers.

3. Store Data (stores data-set.csv)

This static table provides key metadata for each store.

About Dataset: Walmart Sales Forecast

This dataset focuses on predicting weekly store sales at Walmart by examining holiday effects, temporal patterns, and other influential factors. The goal is to enable efficient stock planning, revenue calculations, and strategic decision-making by understanding patterns related to seasonal sales fluctuations. This machine learning model is developed based on resources from : https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/overview/evaluation .

Dataset Overview

1. Test Data Contains 115,064 rows with information: Store, Department, Date, IsHoliday. "IsHoliday" indicates whether the week includes a special holiday. Holidays tend to show higher average sales than non-holiday periods.

2. Train Data Also contains 115,064 rows with Store, Department, Date, IsHoliday, Weekly Sales. Weekly sales are the recorded weekly sales for specific departments at certain stores.

3. Features Data Consists of 8,190 rows with variables such as Temperature, Fuel Price, CPI, Unemployment, Markdown 1-5, IsHoliday * Temperature: Average temperature (Fahrenheit) in a region. * Fuel Price: Can impact consumer spending and sales. * Markdowns 1-5: Promotional markdowns (missing values marked as NA). * CPI: Consumer Price Index (reflects inflation/deflation). * Unemployment: Unemployment rate in a region that affects consumer spending.

4.Store Data Includes details about Walmart stores such as store numbers, store types, and store sizes. Walmart has 45 stores categorized into 3 types: * Type A: Sizes from 39.690 to 219.622 * Type B: Sizes from 34.875 to 140.167 * Type C: Sizes from 39.690 to 42.988 The target variables for prediction are weekly sales, is holiday, and date. The other features are explored to identify patterns and generate insights to build accurate prediction models.

Modeling Objective

The goal is to predict the impact of holidays on weekly store sales. To achieve this, a Time Series modeling approach was applied using variables such as date, weekly sales, is holiday, lag features, rolling averages, and XGBoost. The evaluation metric used was Weighted Mean Absolute Error (WMAE), which emphasizes periods of higher significance, such as holidays.

Final Model Metrics: * Weighted Mean Absolute Error = 211 * Error rate relative to average weekly sales = ~1.32%.

The low error percentage highlights the model's accuracy in forecasting weekly sales and assessing seasonal fluctuations.

Insights

• The analysis provides actionable insights by identifying holiday effects on sales trends.
• This supports better stock planning, strategic financial planning, and risk management.

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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.

United States - Retail Sales: Retail Trade and Food Services was 630964.00000 Mil. of $ in February of 2025, according to the United States Federal Reserve. Historically, United States - Retail Sales: Retail Trade and Food Services reached a record high of 786673.00000 in December of 2024 and a record low of 146376.00000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Retail Sales: Retail Trade and Food Services - last updated from the United States Federal Reserve on November of 2025.

Analysis of ‘Big Mart Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/big-mart-sales on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The data scientists at Big Mart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and predict the sales of each product at a particular outlet.

Using this model, Big Mart will try to understand the properties of products and outlets which play a key role in increasing sales.

Please note that the data may have missing values as some stores might not report all the data due to technical glitches. Hence, it will be required to treat them accordingly.

Content

The dataset provides the product details and the outlet information of the products purchased with their sales value split into a train set (8523) and a test (5681) set. Train file: CSV containing the item outlet information with sales value Test file: CSV containing item outlet combinations for which sales need to be forecasted

Variable Description

• ProductID : unique product ID
• Weight : weight of products
• FatContent : specifies whether the product is low on fat or not
• Visibility : percentage of total display area of all products in a store allocated to the particular product
• ProductType : the category to which the product belongs
• MRP : Maximum Retail Price (listed price) of the products
• OutletID : unique store ID
• EstablishmentYear : year of establishment of the outlets
• OutletSize : the size of the store in terms of ground area covered
• LocationType : the type of city in which the store is located
• OutletType : specifies whether the outlet is just a grocery store or some sort of supermarket
• OutletSales : (target variable) sales of the product in the particular store

Inspiration

Sales of a given product at a retail store can depend both on store attributes as well as product attributes. The dataset is ideal to explore and build a data science model to predict the future sales.

--- Original source retains full ownership of the source dataset ---

The modern trade retail market is experiencing robust growth, projected to reach a market size of $5.30 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 4.29% from 2019 to 2033. This expansion is fueled by several key drivers, including the increasing adoption of e-commerce and omnichannel strategies by major players like Walmart, Amazon, and Target. Consumers are increasingly demanding convenient shopping experiences, including online ordering, in-store pickup, and seamless delivery options, pushing retailers to invest heavily in digital infrastructure and logistics. Furthermore, the rising disposable incomes in developing economies and the growing preference for branded and packaged goods are contributing to market expansion. The market is segmented by various retail formats such as supermarkets, hypermarkets, convenience stores, and department stores, each catering to specific consumer needs and preferences. Competitive pressures are high, with established giants vying for market share against emerging players and innovative business models. While challenges such as fluctuating economic conditions and supply chain disruptions exist, the long-term outlook for the modern trade retail market remains positive, driven by continued technological advancements and evolving consumer behavior. The forecast period from 2025 to 2033 anticipates continued growth, with the market size expanding significantly. This growth will be influenced by ongoing investment in technology, including data analytics to personalize customer experiences and improve supply chain efficiency. Strategic mergers and acquisitions will likely reshape the competitive landscape, as companies seek to consolidate their market positions and diversify their offerings. Expansion into new geographical markets, particularly in rapidly developing economies, presents significant opportunities for growth. However, sustaining this growth will require retailers to adapt to changing consumer preferences, manage costs effectively, and navigate increasingly complex regulatory environments. The focus on sustainability and ethical sourcing will also play a crucial role in shaping future market dynamics. Key drivers for this market are: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Potential restraints include: Rapid Expansion of Urban Areas, Rise of E-commerce and Omnichannel Retailing. Notable trends are: Emergence of Omnichannel Retailing is Driving the Market.