Transforming content search and discovery for a digital advertising and ecommerce leader
A prominent US-based multinational in digital marketing and ecommerce, specializing in internet-related services, has partnered with us to transform its online content and product metadata while elevating the efficiency of content search on its ecommerce platform.
The Challenge
Addressing misclassification in ecommerce
In the fast-moving world of digital storefronts and e-marketplaces, customers expect an exceptional buying experience. When search results miss the mark or contextual ads and marketing content feel irrelevant, many simply leave. One of the main reasons for unsatisfactory customer experiences is misclassified data and product attributes in content designed for ecommerce platforms. Therefore, our client aimed to establish a quality framework with the necessary procedures and personnel to manage taxonomy and perform quality assurance checks on algorithmically and manually classified data.
The Objective
Optimizing data quality and workflow efficiency
- Enhancing the quality of both machine data classification and human adjudication.
- Deliver highest level of data quality for scaled labeling workflows.
- Provide actionable feedback to the platform engineering team to enhance data collection and annotation processes.
- Raise the quality of training data used in the machine-learning algorithm.
The Solution
Centralized and autonomous quality framework
- Audit data classification quality
We developed a comprehensive quality rubric, setting clear evaluation criteria and audit parameters across three critical areas: business, customer and compliance. - Quality insights and categorization
Our team created detailed quality reports covering data labeling operations for multiple product categories. We conducted root cause analysis (RCA) to spot both systemic and one-off issues, then shared recommendations for corrective and preventive action. - Calibration
We introduced a weekly calibration process to minimize discrepancies and sharpen labeling accuracy. Insights from these sessions shaped policy reviews and updates, which we rolled out to upstream data labelers through training sessions. - Dashboarding and reporting
We built dashboards to track key quality KPIs, including quality pass/fail rates, rebuttal percentage (disputed cases accepted as quality passed) and machine classification accuracy. These insights highlighted process and training gaps, leading to continuous improvements.
The Impact
Streamlining workflows for optimized ecommerce operations
- Delivered reliable and actionable insights that improved the quality and effectiveness of data labeling and machine classifications.
- Increased data labeling quality from 86% to 98% — earning recognition from the client as a top performer in data classification and labeling quality.
- Implemented thorough training procedures and developed clear process documentation to support new product launches, policy updates and evolving data classification guidelines.
By focusing on pragmatic solutions and continuous improvement, we helped our client create a more reliable, efficient and customer-centric ecommerce experience.
