1002 Active Global Products Catalog buyers list and Global Products Catalog importers directory compiled from actual Global import shipments of Products Catalog.

Establishment specific sampling results for Siluriformes Product sampling projects. Current data is updated quarterly; archive data is updated annually. Data is split by FY. See the FSIS website for additional information.

6 Active Global Product Catalogue buyers list and Global Product Catalogue importers directory compiled from actual Global import shipments of Product Catalogue.

Snapshot img

Home And Garden Products B2C E-Commerce Market Size 2025-2029

The home and garden products b2c e-commerce market size is forecast to increase by USD 49.62 billion, at a CAGR of 13.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing trend towards online shopping and the widespread adoption of smartphones. The convenience and accessibility offered by e-commerce platforms have led to a rise in consumer spending in this sector. The emergence of omnichannel retailing further enhances the shopping experience, allowing consumers to seamlessly transition between online and offline channels. However, this market also faces challenges, most notably the criticality of efficient logistics management.
This overhead cost can significantly impact profitability and requires strategic planning and investment in technology and infrastructure. Companies seeking to capitalize on market opportunities and navigate challenges effectively must focus on optimizing their logistics networks and leveraging technology to enhance the customer experience. With the rise in online sales, ensuring timely and cost-effective delivery has become a major concern for retailers. Payment gateways facilitate seamless transactions, while influencer marketing and customer lifetime value strategies foster brand loyalty.

What will be the Size of the Home And Garden Products B2C E-Commerce Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The home and garden B2C e-commerce market continues to evolve, driven by shifting consumer preferences and advancements in technology. Averaging an impressive growth rate, this sector encompasses a wide range of products, from cleaning supplies and bath linens to small appliances, hand tools, and decorative accents. Pricing strategies vary, with some retailers focusing on competitive pricing to attract customers, while others differentiate through offering premium products and exceptional customer service. Storage solutions, a crucial aspect of home organization, are increasingly being addressed through smart home devices and digital marketing efforts.

Lawn mowers and gardening tools are popular seasonal items, requiring efficient order fulfillment and shipping logistics. E-commerce platforms provide essential infrastructure, enabling features like marketing automation, search engine optimization, and product catalog management. Product sourcing and supply chain optimization are ongoing concerns, with inventory management and returns processing playing significant roles in maintaining customer satisfaction. Home improvement projects often involve large purchases, necessitating careful consideration of product descriptions, customer ratings, and reviews. Outdoor furniture, lighting fixtures, and patio heaters are popular choices for enhancing living spaces. User experience, including website design and mobile commerce, is paramount in attracting and retaining customers.

Security systems and home automation add convenience and peace of mind, integrating with smart home devices and influencing the market's future direction. Pest control and irrigation systems cater to specific niches, while power tools and gardening equipment cater to DIY enthusiasts. Data analytics and social media marketing provide valuable insights into consumer behavior and trends. The home and garden B2C e-commerce market remains dynamic, with continuous shifts in consumer demands, technological advancements, and business strategies. Embracing these changes through effective pricing, storage solutions, smart home devices, payment gateways, influencer marketing, customer service, and e-commerce platforms is essential for success.

How is this Home And Garden Products B2C E-Commerce Industry segmented?

The home and garden products b2c e-commerce industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Home decor
  Home improvement products
  Others


End-user

  Commercial
  Household


Distribution Channel

  Online marketplaces
  Direct-to-consumer
  Specialty retailers
  Subscription-based platforms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The home decor segment is estimated to witness significant growth during the forecast period. The market encompasses a wide range of items, including cleaning supplies, bath linens, small appliances, hand tools, and more. Customer experience plays a pivotal role in t

39 Active Global Product Catalogue suppliers, manufacturers list and Global Product Catalogue exporters directory compiled from actual Global export shipments of Product Catalogue.

The data below contains newly reported, active covered outpatient drugs which were reported by participating drug manufacturers since the last quarterly update of the Drug Products in the Medicaid Drug Rebate Program (MDRP) database.

Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition

This repository contains the data synthesis pipeline and synthetic product recognition datasets proposed in [1].

Data Synthesis Pipeline:

We provide the Blender 3.1 project files and Python source code of our data synthesis pipeline pipeline.zip, accompanied by the FastCUT models used for synthetic-to-real domain translation models.zip. For the synthesis of new shelf images, a product assortment list and product images must be provided in the corresponding directories products/assortment/ and products/img/. The pipeline expects product images to follow the naming convention c.png, with c corresponding to a GTIN or generic class label (e.g., 9120050882171.png). The assortment list, assortment.csv, is expected to use the sample format [c, w, d, h], with c being the class label and w, d, and h being the packaging dimensions of the given product in mm (e.g., [4004218143128, 140, 70, 160]). The assortment list to use and the number of images to generate can be specified in generateImages.py (see comments). The rendering process is initiated by either executing load.py from within Blender or within a command-line terminal as a background process.

Datasets:

• SG3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 851,801 instances of 3,234 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SG3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3k - Synthetic GroZi-3.2k (SG3k) dataset, consisting of 10,000 synthetic shelf images with 838,696 instances of 1,063 GroZi-3.2k products. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SGI3kt - Domain-translated version of SGI3k, utilizing GroZi-3.2k as the target domain. Instance-level bounding boxes and generic class labels are provided for all product instances.
• SPS8k - Synthetic Product Shelves 8k (SPS8k) dataset, comprised of 16,224 synthetic shelf images with 1,981,967 instances of 8,112 supermarket products. Instance-level bounding boxes and GTIN class labels are provided for all product instances.
• SPS8kt - Domain-translated version of SPS8k, utilizing SKU110k as the target domain. Instance-level bounding boxes and GTIN class labels for all product instances.

Table 1: Dataset characteristics.

Sample Format

A sample consists of an RGB image (i.png) and an accompanying label file (i.txt), which contains the labels for all product instances present in the image. Labels use the YOLO format [c, x, y, w, h].

¹SG3k and SG3kt use generic pseudo-GTIN class labels, created by combining the GroZi-3.2k food product category number i (1-27) with the product image index j (j.jpg), following the convention i0000j (e.g., 13000097).

²SGI3k and SGI3kt use the generic GroZi-3.2k class labels from https://arxiv.org/abs/2003.06800.

Download and Use
This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].

[1] Strohmayer, Julian, and Martin Kampel. "Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition." International Conference on Computer Analysis of Images and Patterns. Cham: Springer Nature Switzerland, 2023.

BibTeX citation:

@inproceedings{strohmayer2023domain,
 title={Domain-Adaptive Data Synthesis for Large-Scale Supermarket Product Recognition},
 author={Strohmayer, Julian and Kampel, Martin},
 booktitle={International Conference on Computer Analysis of Images and Patterns},
 pages={239--250},
 year={2023},
 organization={Springer}
}

List of pesticide products licensed for distribution and sale in the state of Hawaii, including currently licensed and expired. This list is provided for informational purposes only. Restricted use pesticides can only be distributed and sold by a licensed dealer and only to certified applicators. It is a violation of state and federal laws to use these restricted use pesticides unless the person is a certified pesticide applicator or under the direct supervision of a certified pesticide applicator.

The Total Product Life Cycle (TPLC) database integrates premarket and postmarket data about medical devices. It includes information pulled from CDRH databases including Premarket Approvals (PMA), Premarket Notifications (510[k]), Adverse Events, and Recalls. You can search the TPLC database by device name or procode to receive a full report about a particular product line.

Collection of annual data on processed seafood products. The Division provides authoritative advice, coordination and guidance on matters related to the collection, analysis and dissemination of biological, economic, market and sociological statistics by NMFS and state agencies. This data set contains quantity and value data for processed seafood products as well as employment data for included...

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Introduction

Amazon catalog consists of billions of products that belong to thousands of browse nodes (each browse node represents a collection of items for sale). Browse nodes are used to help customer navigate through our website and classify products to product type groups. Hence, it is important to predict the node assignment at the time of listing of the product or when the browse node information is absent.

As part of this hackathon, you will use product metadata to classify products into browse nodes. You will have access to product title, description, bullet points etc. and labels for ~3MM products to train and test your submissions. Note that there is some noise in the data - real world data looks like this!!

Data Description

Full Train/Test dataset details:

Key column – PRODUCT_ID Input features – TITLE, DESCRIPTION, BULLET_POINTS, BRAND Target column – BROWSE_NODE_ID Train dataset size – 2,903,024 Number of classes in Train – 9,919 Overall Test dataset size – 110,775

Evaluation Criteria

This contest uses Accuracy as the evaluation metric to measure submissions quality. Since this is a multiclass classification problem, we are interested in subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of ground truth labels.

Submission File

For each PRODUCT_ID in the test data set, you are required to provide a browse node id prediction. The submission file should be a csv and contain a header followed by pairs of PRODUCT_ID, BROWSE_NODE_ID.

By Hackerearth

Table 13. Tobacco Product Use in the Past Month, by Age Group and State: Percentages, Annual Averages Based on 2013 and 2014 NSDUHs

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Innovation, product innovation introduced, by North American Industry Classification System (NAICS) and enterprise size for Canada and regions from 2007 to today.

Industrial product price index (IPPI) by industry, by North American Industry Classification System (NAICS) 2017 Version 3.0. Monthly data are available from January 1956. The table presents data for the most recent reference period and the last four periods. The base period for the index is (202001=100).

Gross domestic product is the market value of goods and services produced by labor and property in the United States. The U.S. Bureau of Economic Analysis estimates GDP for each quarter and releases new statistics every month. Quarterly GDP data are seasonally adjusted at annual rates.

Dataset

This dataset was created by ahmed elmokhraty

Contents

Contains data for FDA Hydrolyzed Vegetable Protein (HVP) Containing Products recalls since February, 2010.

No description found