Retail & E-Commerce AI

Where Open Active Comes In - Experienced Project Management

Project managers (PMs) are essential in orchestrating the development and enhancement of Retail & E-Commerce AI systems.

We handle strategic oversight, team coordination, and quality assurance, with a strong focus on training and onboarding workers to curate the data that powers these customer-facing systems.

Training and Onboarding

PMs design and implement training programs to ensure workers understand retail contexts, customer behavior, and annotation goals. For example, in sentiment analysis, PMs might train workers to detect sarcasm in reviews, using sample texts and guidelines. Onboarding includes hands-on tasks like tagging images or simulating chats, feedback sessions, and calibration exercises to align worker outputs with AI needs. PMs also establish workflows, such as tiered reviews for complex inventory data.

Task Management and Quality Control

Beyond onboarding, PMs define task scopes (e.g., labeling 20,000 product images) and set metrics like accuracy, consistency, or relevance. They monitor progress via dashboards, address inefficiencies, and refine guidelines based on worker feedback or evolving e-commerce trends.

Collaboration with AI Teams

PMs connect data curators with machine learning engineers, translating technical requirements (e.g., recommendation precision) into actionable tasks. They also manage timelines to ensure data delivery aligns with AI deployment cycles.

We Manage the Tasks Performed by Workers

The annotators, taggers, or analysts perform the detailed work of preparing high-quality datasets for retail and e-commerce applications. Their efforts are precise and customer-focused, requiring attention to detail and context.

Common tasks include:

Labeling and Tagging

For product classification, we might tag an image as “sneakers” or “red.” In chatbot training, they label a query like “return policy” with the intent “assistance.”

Contextual Analysis

For sentiment analysis, our team assesses reviews, tagging “great fit” as positive. In forecasting, they analyze sales data, tagging patterns like “seasonal peak.”

Flagging Violations

In image tagging, our employees and subcontractors flag blurry or mislabeled products, ensuring quality. In search training, they mark irrelevant results like “unrelated item.”

Edge Case Resolution

We handle complex cases—like ambiguous sentiments or multi-product images—often requiring discussion or escalation to retail experts.

We can quickly adapt to and operate within our clients’ annotation platforms, such as proprietary e-commerce tools or industry-standard systems, efficiently processing batches of data ranging from dozens to thousands of items per shift, depending on task complexity.

Data Volumes Needed to Improve AI

The volume of curated data required to train and refine Retail & E-Commerce AI systems is significant, driven by the diversity of products and customer interactions. While specifics vary by task and model, general benchmarks include:

Baseline Training

A functional model might require 10,000–50,000 labeled samples per category (e.g., 50,000 tagged product images). For tasks like chatbot training, this could rise to 100,000 to cover query variations.

Iterative Refinement

To improve accuracy (e.g., from 85% to 95%), an additional 5,000–20,000 samples per issue (e.g., misclassified sentiments) are often needed. For example, refining recommendations might demand 10,000 new interactions.

Scale for Robustness

Large-scale systems (e.g., global e-commerce platforms) require datasets in the millions to handle edge cases, languages, or rare products. A search engine model might start with 200,000 annotated queries, expanding by 50,000 annually.

Active Learning

Advanced systems use active learning, where AI flags uncertain data for review. This reduces volume but requires ongoing curation—perhaps 1,000–5,000 samples weekly—to maintain performance.

The scale demands distributed teams, often hundreds or thousands of workers globally, coordinated by PMs to ensure consistency and quality.

Multilingual & Multicultural Retail & E-Commerce AI

We can assist you with your retail and e-commerce AI needs across diverse linguistic and cultural contexts.

Our team is equipped to curate and process data for global marketplaces, ensuring accurate and culturally relevant datasets tailored to your objectives.

We work in the following languages: