Analysis

The Analysis section goes beyond surface-level rankings to explain why your brand performs the way it does. It breaks down performance into three diagnostic scores and surfaces the specific gaps driving your competitive position.

​

The three diagnostic scores

​

Authority

What it measures: How well your brand is cited and endorsed by trusted, authoritative sources that AI assistants use as context. AI shopping assistants rely heavily on third-party sources — editorial reviews, expert comparisons, trusted publications — when forming their responses. A brand with strong authority signals is consistently cited by high-quality sources. Low authority indicators:

• Your brand appears in AI results mainly as a self-reference (your own website)
• Competitors dominate editorial “best of” lists in your category
• Your brand is rarely mentioned in comparison articles from trusted domains

How to improve: Build third-party coverage — pitch your products to relevant publications, pursue editorial reviews, and earn mentions from authoritative comparison sites in your category.

---

​

Relevance

What it measures: How well your products match what each specific prompt is asking for. Even if AI assistants know about your brand, they may not surface it for a given query if your products aren’t clearly positioned for that specific use case, feature, or buyer context. Low relevance indicators:

• Your brand appears for broad queries but not specific ones (e.g., visible for “running shoes” but not “trail running shoes for overpronation”)
• Product titles and descriptions don’t match the language buyers use in their queries
• Missing attributes like use case, terrain, feature specs, and size fit

How to improve: Align your product content with the language in your tracking prompts. Update titles, descriptions, and metadata to reflect specific use cases and buyer intents.

---

​

Readiness

What it measures: How complete and structured your product data is for AI consumption. AI assistants parse product information to generate recommendations. Gaps in structured data — missing categories, sparse descriptions, absent tags, no schema markup — make it harder for AI to confidently recommend your products. Low readiness indicators:

• Missing product metafields (material, fit, terrain, use case)
• Short or generic product descriptions
• No structured data (JSON-LD schema) on product pages
• Collections that don’t reflect how buyers search

How to improve: Follow the recommendations in the Actions tab. Readiness fixes are often catalog and schema tasks that can be resolved systematically.

---

​

Competitive gap analysis

The competitive gap view shows a head-to-head breakdown for each competitor: on which prompts they outrank you, and why. For each gap, you’ll see which of the three scores — Authority, Relevance, or Readiness — is the primary driver. This lets you prioritize your effort: if Authority is the root cause of most gaps, invest in editorial coverage. If Readiness is the bottleneck, focus on catalog enrichment.