ECommerce Product 3 Products V2

## Overview

ECommerce Product  3 Products  V2 is a dataset for object detection tasks - it contains Televisions Phones Laptops annotations for 7,139 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The "E-commerce Product Dataset" is a comprehensive collection tailored for the e-commerce sector, featuring a wide range of products from 16 main categories including shoes, hats, bags, furniture, digital products, jewelry, and more. With over 200k SKUs, this dataset is equipped with bounding boxes and category tags, making it a pivotal resource for product classification and inventory management.

Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

1. Ecommerce Product Data: APISCRAPY's AI-driven approach ensures the extraction of detailed Ecommerce Product Data, including product specifications, images, and pricing information. This comprehensive data is valuable for market analysis and strategic decision-making.

2. Data Customization: APISCRAPY enables users to customize the data extraction process, ensuring that the extracted ecommerce data aligns precisely with their informational needs. This customization option adds versatility to the service.

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4. Realtime Insights: Businesses can gain real-time insights into the dynamic Ecommerce Market by accessing rapidly extracted data. This real-time information is crucial for staying ahead of market trends and making timely adjustments to business strategies.

5. Scalability: The technology behind APISCRAPY allows scalable extraction of ecommerce data from various sources, accommodating evolving data needs and handling increased volumes effortlessly.

Beyond the broader market, a deeper dive into specific products can provide invaluable insights. APISCRAPY excels in collecting Ecommerce product data, enabling businesses to analyze product performance, pricing strategies, and customer reviews.

To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

[Tags: Ecommerce data, Ecommerce Data Sample, Ecommerce Product Data, Ecommerce Datasets, Ecommerce market data, Ecommerce Market Datasets, Ecommerce Sales data, Ecommerce Data API, Amazon Ecommerce API, Ecommerce scraper, Ecommerce Web Scraping, Ecommerce Data Extraction, Ecommerce Crawler, Ecommerce data scraping, Amazon Data, Ecommerce web data]

-> If you use Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset please cite: https://dergipark.org.tr/en/pub/cukurovaummfd/issue/28708/310341

@research article { cukurovaummfd310341, journal = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi}, issn = {1019-1011}, eissn = {2564-7520}, address = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi Yayın Kurulu Başkanlığı 01330 ADANA}, publisher = {Cukurova University}, year = {2016}, volume = {31}, pages = {464 - 482}, doi = {10.21605/cukurovaummfd.310341}, title = {Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme}, key = {cite}, author = {Gözükara, Furkan and Özel, Selma Ayşe} }

https://doi.org/10.21605/cukurovaummfd.310341

-> Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset is composed as below: ->-> Top 50 E-commerce sites in Turkey are crawled and their comments are extracted. Then randomly 2000 comments selected and manually labelled by a field expert. ->-> After manual labeling the selected comments is done, 600 negative and 600 positive comments are left. ->-> This dataset contains these comments.

-> English_Movie_Reviews_by_Pang_and_Lee_2004 ->-> Pang, B., Lee, L., 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | polarity dataset v2.0 - review_polarity.tar.gz

-> English_Movie_Reviews_Sentences_by_Pang_and_Lee_2005 ->-> Pang, B., Lee, L., 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115-124), Association for Computational Linguistics ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | sentence polarity dataset v1.0 - rt-polaritydata.tar.gz

-> English_Product_Reviews_by_Blitzer_et_al_2007 ->-> Article of the dataset: Blitzer, J., Dredze, M., Pereira, F., 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, In ACL (Vol. 7, pp. 440-447). ->-> Source: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ | processed_acl.tar.gz

-> Turkish_Movie_Reviews_by_Demirtas_and_Pechenizkiy_2013 ->-> Demirtas, E., Pechenizkiy, M., 2013. Cross-lingual polarity detection with machine translation, In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 9). ACM. ->-> http://www.win.tue.nl/~mpechen/projects/smm/#Datasets Turkish_Movie_Sentiment.zip

-> The dataset files are provided as used in the article. -> Weka files are generated with Raw Frequency of terms rather than used Weighting Schemes

-> The folder Cross_Validation contains 10-fold cross-validation each fold files. -> Inside Cross_Validation folder, each turn of the cross-validation is named as test_X where X is the turn number -> Inside test_X folder * Test_Set_Negative_RAW: Contains raw negative class Test data of that cross-validation turn * Test_Set_Negative_Processed: Contains pre-processed negative class Test data of that cross-validation turn * Test_Set_Positive_RAW: Contains raw positive class Test data of that cross-validation turn * Test_Set_Positive_Processed: Contains pre-processed positive class Test data of that cross-validation turn * Train_Set_Negative_RAW: Contains raw negative class Train data of that cross-validation turn * Train_Set_Negative_Processed: Contains pre-processed negative class Train data of that cross-validation turn * Train_Set_Positive_RAW: Contains raw positive class Train data of that cross-validation turn * Train_Set_Positive_Processed: Contains pre-processed positive class Train data of that cross-validation turn * Train_Set_For_Weka: Contains processed Train set formatted for Weka * Test_Set_For_Weka: Contains processed Test set formatted for Weka

-> The folder Entire_Dataset contains files for Entire Dataset * Negative_Processed: Contains all negative comments processed data * Positive_Processed: Contains all positive comments processed data * Negative_RAW: Contains all negative comments RAW data * Positive_RAW: Contains all positive comments RAW data * Entire_Dataset_WEKA: Contains all documents processed data in WEKA format

Asos

Using web scraping, we collected information on over 30,845 clothing items from the Asos website. The dataset can be applied in E-commerce analytics in the fashion industry. The dataset is similar to SheIn E-Commerce Dataset.

  💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset






  Dataset Info

For each item, we extracted:

url - link to the item on the… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/asos-e-commerce-dataset.

Introducing E-Commerce Product Datasets!

Unlock the full potential of your product strategy with E-Commerce Product Datasets. Gain invaluable insights to optimize your product offerings and pricing, analyze top-selling strategies, and assess customer sentiment.

Our E-Commerce Datasets Source:

1. Amazon: Access accurate product data from Amazon, including categories, pricing, reviews, and more.

2. Walmart: Receive comprehensive product information from Walmart, covering pricing, sellers, ratings, availability, and more.

E-Commerce Product Datasets provide structured and actionable data, empowering you to understand customer needs and enhance product strategies. We deliver fresh and precise public e-commerce data, including product names, brands, prices, number of sellers, review counts, ratings, and availability.

You have the flexibility to tailor data delivery to your specific needs:

• Receive datasets in various formats, including JSON and CSV.
• Choose delivery via SFTP or directly to your cloud storage (e.g., AWS S3, Google Cloud Storage).
• Select from one-time, monthly, quarterly, or bi-annual data delivery frequencies.

Why Choose Oxylabs E-Commerce Datasets:

1. Fresh and accurate data: Access clean and structured public e-commerce data collected by our leading web scraping professionals.

2. Time and resource savings: Let our experts handle data extraction at an affordable cost, allowing you to focus on your core business objectives.

3. Customizable solutions: Share your unique business needs, and our team will craft customized dataset solutions tailored to your requirements.

4. Legal compliance: Partner with a trusted leader in ethical data collection, endorsed by Fortune 500 companies and fully compliant with GDPR and CCPA regulations.

Pricing Options:

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the potential of your e-commerce strategy with E-Commerce Product Datasets!

Dataset Description

The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

Zoro.com Product Data Sample – Explore Structured E-commerce Product Listings

This dataset is a sample extraction of product listings from Zoro.com, a leading industrial supply e-commerce platform. It provides structured product-level data that can be used for market research, price comparison engines, product matching models, and e-commerce analytics.

The sample includes a variety of products across tools, hardware, safety equipment, and industrial supplies — with clean, structured fields suitable for both analysis and model training.

Also available: Grainger Product Datasets – structured data from a top industrial supplier.

Ideal for previewing before requesting larger or full Zoro datasets

Use Cases:

• Building product comparison or search engines

• Price intelligence and competitor monitoring

• Product classification and attribute extraction

• Training data for e-commerce AI models

Want More?

This is a sample of a much larger dataset extracted from Zoro.com.
👉 Contact us to access full datasets or request custom category extractions.

Ecommerce Product Photography Market size was valued at USD 163.91 Million in 2023 and is projected to reach USD 342.27 Million by 2031, growing at a CAGR of 11.1% during the forecast period 2024-2030.

Global Ecommerce Product Photography Market Drivers

The market drivers for the Ecommerce Product Photography Market can be influenced by various factors. These may include:

Growth of E-commerce: The industry's explosive growth is primarily due to the need for high-quality product photographs to draw in and convert online shoppers.

Rising Customer Expectations: The need for professional photography services is being driven by consumers' growing expectations of high-resolution photographs, multiple viewpoints, and precise close-ups.

Global Ecommerce Product Photography Market Restraints

Several factors can act as restraints or challenges for the Abc. These may include:

High Costs: Investing in high-quality photos can be limited for small and medium-sized enterprises due to the high cost of professional product photography.

Technological Barriers: Despite the advancements in technology, some organizations lack the necessary resources or experience to utilize the newest photography tools and software, resulting in a lapse in quality.

Online conversion rates of e-commerce sites were the highest in the food & beverage sector, at 3.1 percent in the fourth quarter of 2024. Beauty & skincare followed, with a three percent conversion rate. For comparison, the average conversion rate of e-commerce sites across all selected sectors stood at just over two percent. How does conversion vary by region and device? The conversion rate, which indicates the proportion of visits to e-commerce websites that result in purchases, varies by country and region. For instance, since at least 2023, e-commerce sites have consistently recorded higher conversion rates among shoppers in Great Britain compared to those in the United States and other global regions. Furthermore, despite the increasing prevalence of mobile shopping worldwide, conversions remain more pronounced on larger screens such as tablets and desktops. Online shopping cart abandonment on the rise Recently, the rate at which consumers abandon their online shopping carts has been gradually rising to more than 70 percent in 2024, showing a higher difficulty for e-commerce sites to convert website traffic into purchases. By the end of that year, food and beverage was one of the product categories with the lowest online cart abandonment rate, confirming the sector’s relatively high conversion rate. In the United States, the primary reason why customers abandoned their shopping carts is that extra costs such as shipping, tax, and service fees were too high at checkout.

Ecommerce Product Descriptions Dataset

  Overview

This dataset contains product descriptions for various ecommerce products. Each sample is stored in a separate text file, with features space-separated on a single line. The dataset is structured to be compatible with Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application.

  Dataset Structure

The dataset is organized into the following structure: Ecommerce_Product_Descriptions/… See the full description on the dataset page: https://huggingface.co/datasets/LuminaAI/RCL-Ecommerce-Product-Descriptions.

Mobile phones dominate global digital commerce website visits and contribute to the largest share of online orders. As of the fourth quarter of 2024, smartphones constituted around 78 percent of retail site traffic globally, responsible for generating 68 percent of online shopping orders. Marketplace momentum Retail e-commerce has significantly increased globally over the past few years. Currently, the leading countries in retail e-commerce growth, such as the Philippines, have seen an increase of up to 24 percent. In 2024, the majority of online purchases worldwide were made on online marketplaces, incurring around a 30 percent share of consumer purchases. The top four retail websites for consumers to visit globally were all marketplaces, with the leading website being Amazon.com. Converting clicks When shopping online, website visits often do not end in purchases. This can be due to having second thoughts when online shopping, or simply due to consumers using the platforms to search for products. In 2024, the conversion rate of online shoppers globally was just over two percent, with food and beverages incurring the highest conversion rate from online purchases. Across the globe, almost 20 percent of all retail sales were conducted online. This figure is forecast to increase to at least 21 percent by 2027.

Maximize your online sales potential with our e-commerce data and analytics solutions. Our comprehensive suite of data sources includes real-time information on market trends, consumer behavior, and product pricing. With our advanced analytics tools, you can unlock the power of data-driven insights to optimize your online sales strategy, improve customer engagement, and drive revenue growth.

Whether you want to identify new opportunities, streamline your operations, or stay ahead of the competition, our e-commerce data and analytics product can help you achieve your goals.

Sources: Cubus Official COS Boozt BIK BOK AS Royal Design Group Holding AB Bagaren och Kocken AB Rum21 Svenskt Tenn Kökets favoriter lannamobler.se KWA Garden furniture Confident Living Stalands Möbler Trendrum AB Svenssons Nordiska Galleriet Jotex Jollyroom Monki New Bubbleroom Sweden AB Wegot KitchenTime AB Lindex NA-KD.com Olsson & Gerthel Nordic Nest Bonprix Nederland Vero Moda Care of Carl Cervera Zoovillage ARKET Kappahl DesignTorget Mio AB Afound

Find out import shipments and details about Ecommerce Product Solutions Llc Import Data report along with address, suppliers, products and import shipments.

Get the needed Amazon product review data right from the data extractor! Collect Amazon review information from 19 Amazon countries from the following domains: - amazon.com - amazon.com.au - amazon.com.br - amazon.ca - amazon.cn - amazon.fr - amazon.de - amazon.in - amazon.it - amazon.com.mx - amazon.nl - amazon.sg - amazon.es - amazon.com.tr

Request Ecommerce Product Review dataset by: - keyword - category - seller - product ID (ASIN)

Amazon E-commerce Reviews Data datasets gathered by keyword, seller, category, or ASIN contain: - Product ID (can be extended to the full product information) - Review content and rating - Review metadata

Amazon extraction results can be delivered by schedule or API request, so the data can be extracted in real-time.

DATAANT uses the in-house web scraping service with no concurrency limitations, so unlimited data extractions can be performed simultaneously.

Output can and attributes can be customized to fit your particular needs.

The eCommerce product photography services market is experiencing robust growth, fueled by the booming e-commerce sector and the increasing demand for high-quality visuals to enhance online sales. While precise market sizing data is unavailable, considering the average CAGR for similar visual services and the rapid expansion of online retail, we can reasonably estimate the 2025 market size to be around $2.5 billion. This figure is projected to grow steadily, driven by factors such as the rising adoption of social commerce, the proliferation of marketplaces, and the increasing sophistication of consumer expectations regarding product imagery. Key trends shaping this market include the rise of AI-powered photo editing tools, the increasing use of 3D and augmented reality (AR) product visualization, and a growing demand for sustainable and ethical photography practices. Constraints on growth could include pricing pressures from emerging competitors, fluctuations in advertising spend by e-commerce businesses, and the ongoing need for skilled photographers to meet the rising demand for high-quality content. Market segmentation includes services ranging from basic product shots to sophisticated lifestyle photography, with variations tailored to specific product categories and e-commerce platforms. This diverse range of services caters to businesses of all sizes, from individual entrepreneurs to large multinational corporations. The competitive landscape is fragmented, with a variety of companies offering diverse services and price points. Established players like Squareshot and Pencil One compete alongside smaller, specialized studios, highlighting the opportunities for both large-scale providers and niche businesses. Geographic variations in market size reflect the uneven distribution of e-commerce activity globally, with North America and Europe currently holding significant shares but considerable potential for growth in developing economies with expanding online retail markets. Further growth is expected throughout the forecast period (2025-2033), driven by continuous innovation in photography technology and evolving consumer preferences towards visually compelling online shopping experiences. The market is dynamic and adaptable, constantly responding to technological advancements and shifting consumer demands. A sustained focus on quality, innovation, and customer satisfaction will be critical for success in this competitive and rapidly expanding market.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Success.ai’s Ecommerce Leads Data for Retail, E-commerce & Consumer Goods Executives Worldwide delivers a robust and comprehensive dataset designed to help businesses connect with decision-makers and professionals in the global retail and e-commerce sectors. Covering industry leaders, marketing strategists, product managers, and logistics executives, this dataset offers verified contact details, business locations, and decision-maker insights.

With access to over 700 million verified global profiles and actionable data from retail and consumer goods companies, Success.ai ensures your outreach, market research, and business development initiatives are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution equips you to succeed in the competitive e-commerce landscape.

Why Choose Success.ai’s Ecommerce Leads Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of e-commerce executives, retail leaders, and consumer goods professionals worldwide.
   • AI-driven validation ensures 99% accuracy, optimizing outreach efforts and minimizing errors in communication.

2. Comprehensive Global Coverage

   • Includes profiles of professionals from major e-commerce hubs such as North America, Europe, Asia-Pacific, and the Middle East.
   • Gain insights into global trends in online retail, logistics, and consumer goods.

3. Continuously Updated Datasets

   • Real-time updates capture leadership changes, business expansions, and emerging e-commerce strategies.
   • Stay ahead of industry trends and align your efforts with evolving market conditions.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible use and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with retail, e-commerce, and consumer goods professionals worldwide.
• Leadership and Decision-Maker Insights: Engage with C-suite executives, product managers, and logistics leaders driving the e-commerce industry.
• Verified Contact Details: Access work emails, phone numbers, and business addresses for targeted engagement.
• Market Intelligence: Gain visibility into e-commerce trends, customer engagement strategies, and emerging technologies.

Key Features of the Dataset:

1. Professional Profiles in E-commerce and Retail

   • Identify and connect with decision-makers responsible for product development, logistics, marketing strategies, and digital transformations.
   • Target professionals managing online marketplaces, omnichannel retail strategies, and supply chain efficiencies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (apparel, consumer electronics, food and beverage), geographic location, or job function.
   • Tailor campaigns to address specific market needs such as logistics optimization, digital marketing, or inventory management.

3. Industry and Regional Insights

   • Leverage data on market trends, consumer preferences, and e-commerce growth across key regions.
   • Align your strategies with regional demands and opportunities to maximize impact.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Design targeted campaigns to promote logistics solutions, digital marketing tools, or consumer goods to professionals in retail and e-commerce.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital marketing.

2. Product Development and Innovation

   • Utilize e-commerce insights to guide product development and align offerings with global consumer demands.
   • Collaborate with product managers and marketing strategists to refine product lines or launch new initiatives.

3. Partnership Development and Collaboration

   • Build relationships with e-commerce platforms, logistics providers, and retail brands seeking strategic alliances.
   • Foster partnerships that expand market reach, enhance customer experiences, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze global trends in e-commerce, retail, and consumer goods to refine your business strategies.
   • Benchmark against competitors to identify market gaps, growth opportunities, and emerging technologies.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce leads data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics tools, or marketing pla...

Apparel and accessories was the e-commerce category with the highest share in total retail sales in the United States as of February 2023. 18.3 percent of all retail sales in the fashion segment were generated online. The category with the second-highest percentage of retail sales in the U.S. was furniture, at 15.7 percent. At the other end of the spectrum, office equipment and supplies had the lowest share of retail sales, below two percent.

The continuous rise of e-commerce

Online shopping is on the rise in the United States. The share of all retail sales in the U.S. stemming from online shopping has increased from 4.2 percent in the first quarter of 2010 to a record-breaking 15.6 percent in the third quarter of 2023. Consequently, the sales of retail e-commerce have surged from 39 million U.S. dollars in the first quarter of 2010 to more than 280 billion U.S. dollars in the third quarter of 2023. This boom is forecast to continue over the next few years, with the estimated revenue from online sales, including digital services, reaching 1.72 trillion U.S. dollars by 2027.

The king of e-commerce

In the United States, the number of online shoppers continues to grow. In 2023, there were more than a quarter of a million online shoppers, and the number is forecast to reach over 300 million by 2028. The most popular online shopping destination in the U.S., Amazon, sees a surge of shoppers during certain shopping occasions, which feature appealing bargains that encourage extravagant spending. In 2023, the biggest increase in sales occurred on Black Friday, with an increase of nearly 35 percent. Amazon's deal event for Prime members, Prime Day, saw 17 percent more sales. As for Cyber Monday, sales went up by 13 percent.

• Don't worry about solving CAPTCHAs, rotating proxies or installing headless browsers
• No need to update scrapers with every minor or major website layout or design change

• Simple pricing, pay per successful result only. Say goodbye to being charged for failed requests.

• Filter results by number of reviews, date

• Review data includes meta data about customers such as avatar, location, profile url, etc.

• Get page meta data like product price information, rating distribution, etc.