The AI Buyer Journey Matrix.

Most AI visibility tools report one blended score. Monroya separates LLM visibility into the three stages buyers actually move through — Discovery, Evaluation, and Decision — so you can tell whether you have a discoverability problem, a comparison problem, or a vendor-selection problem. The fix is different for each, and only a stage-by-stage view shows which one is costing you pipeline.

Three numbers from Monroya's aggregated scans across ChatGPT, Claude, Gemini, and Perplexity explain why stage segmentation matters:

• Average discovery-stage inclusion is 7.6%, versus 37.7% at decision stage — brands are roughly 5× less visible when buyers are still framing the problem than when they're comparing named vendors.
• Evaluation-stage inclusion averages 23.3%, sitting much closer to discovery than decision — the middle of the funnel is where most brands quietly lose the buyer.
• 92% of tracked brands have a 0% citation rate — they get mentioned in AI answers, but no source link points back to their site. Citation gaps are far more common than mention gaps.

Aggregated across Monroya customer scans, ChatGPT / Claude / Gemini / Perplexity, 90-day rolling window. Refreshed with each scan cycle.

AI Buyer Journey Intelligence is the measurement of how a brand appears across the full sequence of questions a buyer asks a generative AI assistant — from first problem framing through final vendor selection — segmented by funnel stage and persona.

The AI Buyer Journey Matrix is the canonical instrument: a grid with three journey stages — discovery, evaluation, decision — on one axis and buyer personas on the other. Each cell reports its own AI Share of Voice, its top competing brand, and the specific prompts driving the result across ChatGPT, Claude, Gemini, and Perplexity.

A single average AI visibility score is misleading by design. A 42% score can mean dominance in discovery and invisibility in decision — a deal-loss pattern — or the reverse, a brand-strength pattern with a discoverability gap. The fix for each is different, and only a stage-by-persona view surfaces which one you have.

• Discovery. Problem-framing prompts, before a buyer knows a category exists. Example: "why are our AI answers inconsistent across providers". Winning here means category-defining content on third-party sources AI assistants pull from.
• Evaluation. Options-and-approaches prompts, once the category is understood. Example: "best tools to monitor brand visibility in ChatGPT". Winning here means comparison and alternative pages, and structured feature breakdowns on your own domain.
• Decision. Vendor-selection and head-to-head prompts. Example: "monroya vs otterly pricing". Winning here means fact sheets, reviewer evidence, current pricing, and direct competitor pages.

We generate a draft prompt set from your category and ICP, organized by stage and persona. You review and edit. Prompts are tagged automatically, then scanned daily across every major AI assistant so each cell is auditable back to the raw responses. Each cell carries its own share of voice, its own top competitor, and its own opportunity queue.

Three patterns recur. If you're weak in discovery, invest in category-defining content on third-party sources where AI answers pull from. If you're weak in evaluation, ship comparison and alternative pages on your own domain. If you're weak in decision, publish structured fact sheets, reviewer evidence, and direct competitor pages with current pricing.

Most AI visibility tools report one number. We separated the journey because operators kept telling us the average was useless — they needed to know which specific stage was bleeding pipeline. The matrix is the first thing every Monroya customer opens.