Retail has a daily complexity problem: too many products, too much data, and not enough insight to act fast. From overflowing inventory to mismatched product listings and product disappearing margins, operational challenges are costly and constant. 67% of brands cited product content optimization as their top profitability challenge, due to rising customer expectations and outdated tech, according to Coresight Research. Rithum has built tools to tackle these challenges directly with AI designed to solve some of the biggest operational headaches for retailers and brands: product categorization and profitability benchmarking.
Make smarter inventory decisions with Rithum AI
Rithum’s AI helps brands better understand what products to keep, adjust, or remove from inventory. Instead of trimming entire categories or gut-checking what might work by relying on outdated reports, retailers and brands can make proactive choices based on clear data patterns.
Rithum’s AI:
- Uses a multi-language large language model (LLM) trained on commerce-specific data so it can adjust listings based on language and regional standards
- Combines AI embeddings with Rithum’s AI DataCore for smarter template mapping
- Integrates human-in-the-loop (HITL) feedback to improve accuracy over time
- Is benchmarked across 50 channels with 70% top-five prediction accuracy
Why it matters for retailers and brands:
- Improve search visibility and conversions with higher listing quality
- Automatically comply with local marketplace data requirements
- Drastically reduce manual effort and errors
Real-World Example: Instead of removing an entire category because of returns, one European fashion brand used Rithum to single out just a few high-return SKUs. In another case, updated descriptions for apparel fit led to higher conversions and fewer returns.
Get profitability benchmarking that goes deeper than a spreadsheet
Even the most polished listings and inventory plans can fall short if you don’t know where you’re losing margin. While other tools hand you raw data, Rithum’s AI delivers granular SKU and variant-level analysis to identify underperformers, predict assortment success, and benchmark performance against peers. Highlight exactly what’s costing you money and see how to fix it.
Rithum’s AI identifies and assesses:
- High-return SKUs and variations (e.g. size or color mismatches)
- Root causes of returns (like fit descriptors or faulty product data)
- Future assortment recommendations using predictive modeling
- SKU-level profitability (costs, fees, refunds, conversions)
- Peer benchmarking based on cross-network insights
Why it matters for retailers and brands:
- Improve margins by focusing on high-performing SKUs
- Avoid over-discounting with smarter pricing models
- Cut returns by identifying and fixing product issues
- Align teams across finance, merchandising, and operations
Real-World Example: Instead of assuming jeans are being returned because of pricing or product type, one brand used Rithum AI to discover how fit descriptors, such as slim or tapered fit, were the real problem. Once added, return rates dropped and conversions increased.
See what your data has been trying to tell you. Schedule a demo of Rithum’s AI today.
Sebastian Spiegler is Senior Director, Artificial Intelligence at Rithum.
