From Disorganized Listings to Seamless Shopping – How Indium’s AI-Powered Categorization Boosted Conversions
Client Overview
The client owns an AI-powered e-commerce aggregator that enhances shopping experiences by enabling seamless product comparison across multiple retailer websites. However, inconsistent product categorization across retailers’ websites created challenges in assigning the same product to a unified category, compromising search accuracy and user experience. To solve this problem, they sought a trusted technology partner with advanced AI and machine learning expertise who would ultimately improve search quality, refine recommendations, and deliver a smarter, more seamless shopping experience
Mismatch Mayhem:
The Hidden Barriers to Smarter Shopping
The Categorization Chaos
Retailers defined product categories differently, making grouping identical products under a single classification challenging. It also caused 1 search inefficiencies from the same product showing up in sev
The Search Accuracy Dilemma
Misclassified products resulted in inaccurate search results, frustrating users and reducing conversion rates. A lack of standardization 2 meant inconsistent search suggestions and filters, leading to poor product discovery.
The Broken User Experience:
Users couldn’t compare products easily due to category mismatches across different retailers. Inconsistent categorization indirectly 3 impacted recommendations and up-selling, leading to lost revenue opportunities.
Traditional rule-based categorization failed to address the scale and complexity of multiple retailer taxonomies. A sophisticated AI-driven approach was required to intelligently classify and map products across different sources.
Strategic Thinking, AI Execution: Indium’s AI-Driven Approach for Product Categorization
Product categorization is highly significant for e-commerce websites. Displaying the most popular categories up front speeds up free text searches and improves user experience.
To tackle the challenge of inconsistent product categorization, we implemented a strategic, AI-driven approach that ensured accurate classification and seamless product discovery. The solution was structured into multiple stages, leveraging advanced techniques to enhance efficiency and precision.
Data Sampling – Ensuring Balanced Representation
To create a well-distributed dataset, a Random Sampling with a Stratification approach was employed, ensuring a fair representation of each product category.
Pre-Processing Phase – Converting Text into Usable Data
Before training, product data was transformed into numerical representations using techniques such as TF-IDF, N-grams, stop word removal, and stemming/lemmatization to refine
text-based inputs.
Model Training – Building Intelligent Classification Models
The processed data was trained using
Support Vector Machines (SVM) and
Naïve Bayes models, ensuring robust
categorization and high accuracy.
Parameter Tuning – Optimizing for Maximum Accuracy
Cross-validation and Grid Search (Scikit-Learn) were used to fine-tune model performance and identify the best parameters for improved classification
Model Nesting – Structuring for Hierarchical Categorization
A hierarchical approach was applied
to classification by developing
separate models for different category levels using Group By formulas, for-loops, and subsetting techniques.
Deployment & Production – Enabling Seamless Integration
The final trained model was integrated into the client’s system using JavaScript, Django, and Pickling, ensuring smooth deployment and real-time categorization.
The solution delivered accurate, scalable, and AI-powered product classification through this structured approach, enhancing search results, recommendations, and overall user experience.
Precision in Product Categorization, Powering Business Impact at Scale
By leveraging AI-driven driven approaches, Indium transformed the client’s product categorization process, ensuring accuracy, consistency, and seamless product discovery. Our strategic approach not only optimized search relevance but also
delivered a measurable business impact at scale.
75% Accuracy – Smarter AI, Sharper Categorization
The AI model achieved great accuracy in predicting categories for new products, ensuring precise classification and improved product discovery.
3% Conversion Boost:
An increase in conversion rates across all product categories contributed to a 20% rise in Gross Merchandise Value (GMV), driving significant business impact.
Better Categorization, Bigger Revenue
Enhanced product categorization and superior indexing led to better search results, directly improving product discovery and user experience.
About Indium
Indium is an Al-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative Al, Product Engineering, Intelligent Automation, Data & Al, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact.
With 5,000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology-driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an Al-first world.
