Persona x Journey Stage Matrix

You own or run a brand in a category buyers research before they purchase — wedding, pet, sleep & wellness, beauty, electronics, or a B2B product that gets compared before anyone books a demo. There's no data team, no growth-marketing hire, no agency on retainer. You handle everything. If you sell through Shopify, Citable's storefront integration ties everything below directly to orders.

How you got here

The moment usually happens in two beats. First, you notice a new referral source in GA4 — chatgpt.com or perplexity.ai sending visitors to your store — and those visitors seem to buy. Then you open ChatGPT and ask the question your own customers would ask: "best [your category] for [your buyer's situation]." Competitors you know well appear by name, with descriptions and links. You either don't appear, or you appear and realize you have no idea why — or what's being said about you in the thousands of similar conversations you'll never see.

Is this worth your time?

The questions are practical: Is AI actually influencing my category, or is this hype? Can I fix my visibility myself? What would it cost to know? The market data says the channel is real — AI-referred traffic to US retailers grew 393% year over year (Adobe, Q1 2026), and those buyers convert 42% better, up to 4x versus Google Search per public data. Whether it's real *for your category* is exactly what a baseline scan answers.

The gap, where it exists, is fixable — but through consistent, structured presence in the sources AI engines trust for your category, not sporadic effort. You don't need a hire; you need the loop handled: scanning, explanation, content, measurement.

What solving this looks like

A scan that runs without you managing it, built from the questions your actual buyers ask. A clear picture of which competitors get recommended and why. A content queue you review in a couple of hours a week — not a production process to manage. And the number that settles whether this channel matters: what AI-referred visitors actually do on your storefront.

The Audit plan ($99/mo, 7-day free trial) establishes your baseline against competitors. Optimize ($299/mo) adds Perplexity and Gemini coverage, evolving personas, and the weekly Action Engine cadence once you've decided the gap is worth closing.

You're the head of growth, marketing lead, or e-commerce manager at a brand where AI-referred traffic is no longer a curiosity — it's a visible line in your analytics, and it converts. Your problem isn't discovering the channel. It's that you can't explain it, defend it, or grow it with the tools you have.

How you got here

You've watched the AI-referral segment grow quarter over quarter. Leadership is asking what's driving it and whether to invest. Your web analytics shows the sessions but nothing upstream of them: not which questions produced the recommendation, not what the assistant said about you versus rivals, not why the channel surged or dipped. You may have trialed a GEO monitoring tool and found it reports mentions without connecting them to the revenue you're being asked about.

What you actually need

Three connected answers. Upstream: which buyer questions and personas drive AI recommendations in your category, and where you win or lose them. Midstream: what the assistants actually say — sentiment, positioning, the sources they trust. Downstream: what AI-referred sessions convert into, so the channel can be defended in the same language as every other channel — orders. That chain (user → agent → conversion) is exactly what Citable provides; web analytics can't see it, because the deliberation never touches your property.

What solving this looks like

Optimize ($299/mo) gives you the system: ChatGPT, Perplexity, and Gemini coverage, 150 tracked questions, evolving personas, competitor intelligence, and the weekly Action Engine cadence your team can execute alongside existing work. Grow ($799/mo) adds daily tracking across all six engines (any three per scan, including Google AI Overview), done-for-you execution through the Citable Agent, the Agentic Storefront for Shopify, and a dedicated manager.

You run a small agency or work as an independent consultant. Your e-commerce clients are asking about AI search, and you've added — or want to add — it to your service offering. The problem is that most tools you've evaluated only monitor: they show clients where they're invisible but don't produce the work that fixes it, or the revenue story that justifies the retainer.

How you got here

You tested or evaluated monitoring tools and found that a mentions report isn't a sellable deliverable for e-commerce clients. Clients want output they can see — content shipped, sources earned, recommendations improving — and a connection to the metric they actually pay retainers for: orders. Without an execution and attribution layer, AI-search work becomes a monthly report that clients eventually stop paying for.

Can you build a scalable AI-search service without building the stack yourself?

The questions are operational: Can this support multiple client accounts? What does client-facing reporting look like? How much editing does the content need before it's client-presentable? The model that works is using Citable as the execution and measurement layer — scanning, gap analysis, content generation, attribution — while you own strategy, brand voice, and the client relationship. That division is where your margin lives.

What solving this looks like

Separate workspace, persona configuration, and question suites per client. A content queue that reaches client-presentable quality with light editing. Reports you can put in front of clients showing movement in citations, share of voice, and AI-referred conversions. A plan structure that makes multi-client management financially viable.

Grow ($799/mo) is the right starting point for a single high-value client pilot — daily tracking, all six engines available, done-for-you execution you curate. Scale (custom) is designed for agencies managing multiple clients, with workspaces, reporting, and pricing structured around that model.