Shopping Results

Brand monitoring shows whether you're mentioned; Shopping Results reveals which specific offerings AI systems favor and why. This granularity enables SKU-level optimization, pricing tier recommendations, and product portfolio decisions. A brand might have strong overall visibility but discover their flagship product is under-represented while a legacy product dominates mentions. Shopping Results guides resource allocation, inventory decisions, and product marketing strategies based on what AI systems actually recommend to buyers.

Absolutely. The platform's Shopping Results identifies which products lack visibility despite competitive advantages, revealing content gaps or positioning weaknesses. For example, discovering your premium product appears in only 10% of AI recommendations despite superior features signals a content opportunity. By analyzing high-performing products (what attributes AI systems highlight, what contexts they're mentioned in), you create optimization playbooks for underperforming products. Applying learnings from top performers can dramatically improve visibility for previously invisible products.

Shopping Results reveals which price points and feature combinations AI systems recommend most frequently, indicating market-perceived value. When ChatGPT consistently recommends your mid-tier plan over enterprise offerings, it signals pricing perception or feature set misalignment. Similarly, if AI responses describe your €199 product as 'premium' when you position it as 'affordable,' there's a pricing strategy opportunity. The platform's Shopping Results provides market perception data that validates pricing decisions and identifies packaging opportunities aligned with how AI systems (and by extension, buyers) categorize your offerings.