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Data Annotation Services for AI-Driven Ecommerce Business
Leverage AI with mind. With our ecommerce data annotation services, you get quality datasets to train machine learning models for ecommerce applications.
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Ecommerce Data Annotation Services at Label Your Data
As a trusted provider of data annotation for the ecommerce industry, we have a vast experience and expertise under our belt. Our skilled annotators work with diverse data types, including images, videos, unstructured text, and database items. Hence, we meticulously label your data, enabling effective product recognition & categorization, visual search, recommendation systems, and more.
Our team employs cutting-edge tooling and methods to ensure security, high accuracy, and scalability. Data labeling for ecommerce plays a vital role in improving search relevance, personalization, fraud detection, and customer behavior analysis for your business. Optimize your operations, enhance user experiences, and gain a competitive edge in the market with Label Your Data — your trusted partner in AI!
On photo:
Olha, Karyna
Our Data Annotation and Processing Services for Ecommerce Companies
At Label Your Data, our data annotation services are designed to meet the specific needs of your online retail business. We cover both Computer Vision and NLP (Natural Language Processing) tasks that are essential for deploying AI in e-commerce operations.
To enhance customer experience with online shopping and drive your business growth, you need to use AI data for the e-commerce industry correctly. Let us help with this intricate task by providing a complete suite of data annotation services tailored to the ecommerce industry’s specific needs:
Our annotators label image datasets with object detection, bounding boxes, or semantic segmentation to improve visual search and product presentation.
We accurately annotate product attributes such as size, color, brand, and material to enable tasks like seamless filtering and sorting for customers.
Extracting valuable insights from customer feedback and reviews helps our clients build applications to gauge product reception and sentiment.
Our experts will categorize product descriptions, specifications, and customer inquiries for improved search results and user experience.
Identifying and categorizing entities like product names and brand names in unstructured text helps to enhance searchability.
At your request, we can gather relevant and targeted data from various sources to build a comprehensive dataset for your ecommerce operations.
Extracting product data, pricing information, and customer reviews from websites is performed to enrich your ecommerce database.
Also, we can efficiently organize and manage your ecommerce database to ensure data integrity and accessibility.
Our annotators can accurately and timely perform data entry for product details, inventory updates, and customer information to maintain an up-to-date database.
Organizing products into appropriate categories and subcategories by our team will enhance discoverability and user navigation.
We can also create data annotation guidelines and prompts to ensure consistency and quality in the annotation process.
Our annotators label image datasets with object detection, bounding boxes, or semantic segmentation to improve visual search and product presentation.
We accurately annotate product attributes such as size, color, brand, and material to enable tasks like seamless filtering and sorting for customers.
Extracting valuable insights from customer feedback and reviews helps our clients build applications to gauge product reception and sentiment.
Our experts will categorize product descriptions, specifications, and customer inquiries for improved search results and user experience.
Identifying and categorizing entities like product names and brand names in unstructured text helps to enhance searchability.
At your request, we can gather relevant and targeted data from various sources to build a comprehensive dataset for your ecommerce operations.
Extracting product data, pricing information, and customer reviews from websites is performed to enrich your ecommerce database.
Also, we can efficiently organize and manage your ecommerce database to ensure data integrity and accessibility.
Our annotators can accurately and timely perform data entry for product details, inventory updates, and customer information to maintain an up-to-date database.
Organizing products into appropriate categories and subcategories by our team will enhance discoverability and user navigation.
We can also create data annotation guidelines and prompts to ensure consistency and quality in the annotation process.
Industry-Specific Expertise
With our rich experience in data labeling for the ecommerce industry, we can adeptly handle complex ecommerce data and provide accurate annotations. This ensures that your machine learning models make precise predictions and recommendations.
Agile Workflow
Our ecommerce data annotation services revolve around unmatched flexibility. The team adapts to your timeframe and unique requirements. Also, seamless integration with your preferred tooling ensures efficient collaboration for annotation, database management, and data entry.
Consistent Annotation Support
With our dedicated FTE (Full-Time Equivalent) teams, we ensure a seamless learning curve and deliver stable, high-quality results. This way, you experience faster project turnaround, increased precision, and greater adaptability to the specific requirements.
Why Choose Data Annotation for Ecommerce at Label Your Data?
Our company holds certifications for PCI DSS (level 1) and ISO:27001, and we adhere to the regulations outlined by GDPR, CCPA, and HIPAA. With 10+ years of experience and 500+ specialists on board, we provide customized data labeling services for ecommerce for enterprise and R&D projects in55 languages.
Our Success Stories
Main challenge:
Handling diverse text-based tasks on a large ecommerce platform across multiple languages.
Solution:
Assembling a multilingual annotation team proficient to effectively process and manage the text data.
Multilingual Text Data Annotation for Ecommerce Platform
This ongoing project involves multiple tasks for our Client who is developing a large ecommerce platform. Based on the Client’s request, the data annotation for ecommerce tasks encompass NER for customer support interactions, audio transcriptions of customer calls, product title labeling, creation of search results and prompts, and providing translation services. To handle the text data effectively, we’ve assembled a multilingual team proficient in English, Spanish, German, French, and Portuguese.
Main challenge:
Organizing a vast clothing item database for an AI-driven fashion brand.
Solution:
Forming a dedicated team to categorize and label items for seamless ML algorithm integration.
Data Labeling and Categorization for Personalized Customer Journey
For a US-based AI-driven fashion brand, we did a short-term data labeling for ecommerce project. Our task involved meticulous labeling and categorization of the Client`s diverse clothing items. Additionally, we managed image renaming and database administration. To maintain efficiency, we allocated 1-5 annotation experts based on demand and seasonality. Overall, we processed 10,000 clothing items, enabling the Client`s ML algorithm to deliver personalized shopping experiences.
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Label Your Data were genuinely interested in the success of my project, asked good questions, and were flexible in working in my proprietary software environment.
Kyle Hamilton
PhD Researcher at TU Dublin
Trusted by ML Professionals
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FAQs
What is data analysis in ecommerce?
Data analysis in ecommerce refers to the process of examining and interpreting the vast amounts of data generated by online retail activities through expert data annotation services for ecommerce. This helps to gain insights, identify patterns, and make informed business decisions that optimize sales, marketing, and overall performance.
What are the best practices for finance data collection to ensure accuracy and reliability in financial analysis?
The ecommerce data types typically include:
Customer information (e.g., demographics, preferences),
Transactional data (e.g., purchases, order history),
Website analytics (e.g., traffic, click-through rates),
Product data (e.g., descriptions, prices),
Marketing data (e.g., campaign performance, customer engagement).
What are the key steps and methodologies for effectively analyzing ecommerce data?
Ecommerce analysis involves collecting and processing data, exploring it statistically, using visualizations, annotating data for ecommerce machine learning models, and applying ML algorithms to uncover patterns and optimize online retail.