> ## Documentation Index
> Fetch the complete documentation index at: https://citdhq.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Analysis
> Deep-dive scoring to understand exactly why competitors outrank you in AI search results.
Analysis features are available on paid plans.
The **Analysis** page is the root-cause layer of Citd. It goes beyond scorekeeping and explains why your performance looks the way it does.
## What Analysis contains
Analysis is built around four connected views:
* **Authority**
* **Relevance**
* **Readiness**
* **Competitive gap**
Together, they answer whether the problem is missing trust, weak product-query fit, incomplete catalog structure, or a direct competitor advantage.
## Authority
**What it measures:** How well your brand is cited and supported by external sources that influence tracked responses.
The Authority section includes summary metrics plus a **citation gap table** showing which source domains cite competitors but not you. For more detail on which specific sources are helping or hurting you, see [Sources](/features/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. The [Actions](/features/actions) queue surfaces specific outreach recommendations tied to your citation gaps.
## Relevance
**What it measures:** How closely your products match the attributes and intent being asked for in each prompt.
This section aggregates repeated attribute gaps across responses so you can see which missing or weak signals keep hurting you.
**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 do not 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. [Actions](/features/actions) will have content and catalog recommendations for the highest-impact gaps.
## Readiness
**What it measures:** How complete and structured your product data is for AI consumption.
When Shopify is connected, Readiness uses richer catalog data. Without it, the section can still surface limited or proxy-style readiness signals where available.
**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 do not reflect how buyers search
**How to improve:** Follow the recommendations in the [Actions](/features/actions) tab. Use [Products](/features/products) to inspect the exact affected rows and jump to Shopify to fix them. For AI-assisted improvements to titles, descriptions, tags, SEO fields, metafields, and alt text, use [Enrichment](/features/enrichment). Readiness work is often the most systematic to fix because it maps directly to catalog, schema, media, taxonomy, and metadata improvements.
## Competitive gap
**What it measures:** Where specific competitors outperform you in the same prompts.
This view shows which prompts you share with a competitor, how your position compares, and which attributes the competitor uses that you do not. It is the fastest way to see what a winning brand is doing differently.
**How to improve:** Use the competitive gap findings to prioritize your [Actions](/features/actions) queue — focus on the prompts and attributes where you are closest to parity, since those are usually the quickest wins.
## When to use Analysis
Open Analysis when Overview tells you something is wrong but does not explain why.
**Typical workflow:**
1. Spot a drop on [Overview](/features/overview).
2. Open Analysis to identify the dominant failure mode.
3. Open [Sources](/features/sources) if the issue looks citation-driven.
4. Open [Actions](/features/actions) once you know what to fix.
5. Open [Products](/features/products) or [Enrichment](/features/enrichment) to inspect and fix the exact catalog rows.
## Next steps
Turn your analysis findings into a prioritized fix queue.
Drill into the citation gaps driving your Authority score.
Inspect the specific products behind your Readiness score.
Use AI to improve product titles, descriptions, tags, and metafields.