Every product in your catalog receives a composite health score derived from four independent dimensions: title quality, description completeness, image coverage, and SEO signal density. Scores update in real time as your catalog changes.

Critical issues surface first. A severity-ranked issue queue means your team always knows where to focus — no manual triage, no guesswork. Filter by score range, category, or issue type to build targeted remediation workflows.

Benchmarking data across your category means you know not just how a product scores in isolation, but how it compares to the competitive landscape.

Real search volume data gives you real demand signals, not guesswork. See exactly which keywords your products are targeting, which they are missing, and what the monthly search volume is for each.

Gap analysis compares your catalog content against high-value keywords in your category. Opportunities are ranked by search volume and content gap severity — so you always know which changes will have the highest organic impact.

Keyword density and placement recommendations are built into the content scoring model, bridging the gap between SEO strategy and product listing optimization at scale.

Generative Engine Optimization (GEO) is the discipline of making your products discoverable in AI-powered search interfaces — ChatGPT, Perplexity, Google AI Overviews, and their successors. Traditional SEO does not fully translate to this environment.

EcomIQX scores your product content against the structural and semantic signals that AI search engines use to construct citations and recommendations. You see exactly which products are being cited, in which engines, and what content changes would improve visibility.

Content structure recommendations target the specific deficiencies that reduce AI citation likelihood — missing specifications, ambiguous entity references, thin factual density.

Run controlled experiments on product content changes — titles, descriptions, keywords, translations. Measure real revenue and conversion impact before committing changes across the catalog. Every experiment is isolated, with a defined control and treatment group.

A Bayesian analysis engine computes statistical confidence, primary metric lift, and generates roll-out or roll-back recommendations automatically. Decisions are backed by data — not recency bias or gut instinct.

The experiment lifecycle is fully managed: baseline collection, controlled test period, automated analysis, and one-click rollout or rollback. Every state transition is logged with a full audit trail, giving teams complete visibility into what changed, when, and why.

Agents are goal-driven — you define the objective ("lift CTR 15% on low-score products"), not the task. The agent picks which products to work on, proposes or applies changes, and measures the lift with 14-day attribution data from Google Search Console, Shopify, and your connectors.

Autonomy is graduated. Report-only scans and surfaces insights without changing anything. Propose drafts rewrites or translations for your review — nothing goes live without approval. Autonomous applies changes directly, gated by a hard monthly credit cap per agent to prevent runaway spend.

The feedback loop is the core: after each applied change, attribution data accumulates for 14 days. Winning patterns (headline structure, keyword placement, tone per category) are boosted on the next run; losing patterns are dropped. Your agents compound wins instead of running forever on the same heuristics.