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Your next customer is asking ChatGPT, Gemini or Perplexity what to buy

Your next customer is asking ChatGPT what to buy. If you’re not present and persuasive in that answer, you’re out before the click. Here’s how LLMs evaluate brands—and how to design your site so AI assistants recommend you.

Winning the stage in ChatGPT, Gemini or Perplexity

Your next customer is asking ChatGPT what to buy.
If your brand isn’t present and persuasive in that answer, you’re out before the click.
This guide explains how LLMs evaluate pages and how to design your site so AI assistants recommend you.

1) The buying journey has moved into chat

Prospects now ask assistants questions like:

  • “Best invoice tools for freelancers under $30?”
  • “Is Brand X SOC2 compliant? Any limits on the free tier?”
  • “Brand X vs Brand Y—what’s better for a 5-person team?”

These aren’t classic navigational queries; they’re consideration questions. And assistants often deliver a single synthesized answer with a handful of citations. If you aren’t one of those sources—or worse, if your info is ambiguous—LLMs nudge buyers toward your competitors.

Implication: Optimization is no longer only “rank for keywords.” It’s “be the definitive, citable source a model uses to resolve buyer doubt.”

2) How LLMs decide what—and whom—to recommend

LLMs don’t “see” your brand like a human. They assemble answers from:

  • Retrieval pools (search indices, web crawlers, knowledge graphs, sometimes live browsing)
  • Parsable structure (JSON-LD, FAQ schema, clear headings, stable URLs)
  • Canonical clarity (short, unequivocal answers to buyer questions)
  • Topical coverage & consistency (pricing, security, integrations, refunds, use cases, comparisons)
  • Source features (authority signals, recency, coherence, lack of contradictions)

Technically, assistants combine abstractive synthesis (summarize/compose) with extractive anchors (pull concrete facts, cite canonical pages).
Your goal is to be easy to extract (clear facts) and safe to abstract (consistent, structured, up-to-date).

Need the hands-on structure and artifacts? See Answerable Brands for the strategy and JSON-LD & Q&A Bundles for templates and validation. We won’t repeat them here.

3) Design your site to “win the answer”

Think like a sales rep inside the model. What would you need to confidently recommend your product?

A. Map the buyer’s questions to pages

Create or tighten these pages (one clear URL each):

  • /pricing — tiers, numbers, limits, “is there a free plan?” (answer first)
  • /security — compliance, data location, retention, subprocessors
  • /integrations — logos + one-liners; link to deep pages
  • **/use-cases/**slug — 2–4 verticals you truly serve (each its own page)
  • **/compare/**competitor — head-to-head comparisons (table with crisp deltas)
  • /faq — 6–12 definitive Q&As (match the FAQPage JSON-LD)

Keep URLs stable. LLMs anchor to durable slugs, not “/new-pricing-2025”.

B. Write answer-first content

Put the short, definitive answer in the first sentence; details follow.
Examples:

  • “Yes. We are SOC 2 Type II. Reports available under NDA.”
  • “Starter is free. Pro is $29/mo; Team is $99/mo.”
  • “We integrate with Slack. Alerts and slash commands for quick actions.”

C. Provide structured hooks (without repeating the other posts)

  • Implement the artifacts from JSON-LD & Q&A Bundles: Organization, WebSite, FAQPage, /llm_qa.json, /llm-sitemap.json.
  • Ensure on-page text matches structured answers exactly.

D. Add comparison tables that models can lift

A scannable table outperforms a paragraph wall. Keep rows objective:

  • Feature name
  • What you do
  • What typical alternatives do
  • Limits/quotas
  • Price line item

Avoid hype; use concrete facts the model can safely reuse.

4) Simulate the assistant: conversation tests

Run these prompts in Perplexity, Copilot/Bing, and Gemini (and ChatGPT if browsing is available). Record outcomes monthly.

Prompts:

  1. “What are the best [category] tools under $30 for small teams?”
  2. “[Your Brand] vs [Competitor]. Which is better for a 5-person startup?”
  3. “Is [Your Brand] SOC2 compliant? Any data residency options?”
  4. “Does [Your Brand] integrate with Slack? What can it do?”
  5. “Any gotchas/limits with [Your Brand]’s free plan?”

Score each engine:

  • Present? (Brand mentioned?)
  • Cited? (Your URL in citations?)
  • Positioning? (First paragraph, mid, or footnote?)
  • Fact accuracy? (Any hallucinations to fix on your pages?)

If answers miss or misstate facts → tighten the source page with answer-first phrasing, add/adjust JSON-LD or Q&A bundle, and ensure the URL is linked in your /llm-sitemap.json.

5) Persuasion patterns that LLMs reward

  • Objection handling sections: “Is it too technical?”, “Will it migrate easily?”, “What if I outgrow the free plan?”
  • Risk reversal: refund policy in one line (“14-day no-questions refund.”)
  • Eligibility & fit: who should not use you (counter-intuitive trust builder)
  • Evidence: tiny case blurbs with metrics (one sentence each), link to a longer story
  • Human-readable + machine-readable: the same claim stated plainly and encoded in JSON-LD/FAQ

6) Measurement that makes sense

  • Search Console: watch /pricing, /security, /compare/* for impressions/clicks; validate structured data in Enhancements.
  • Share-of-voice spreadsheet: the five prompts above × 3 engines, monthly rows; track presence, citation, position.
  • Site analytics: lift in landing on comparison and pricing pages; scroll and time-on-page as sanity checks.
  • Change log: annotate when you updated JSON-LD, Q&A bundle, or comparison tables to correlate shifts.

Expected timelines:

  • Gemini/Google & Bing/Copilot/Perplexity (index-tied or browsing): days–weeks.
  • Non-browsing ChatGPT/Claude (foundation refresh): months—still worth preparing; today’s structure becomes tomorrow’s training data.

7) A pragmatic 7-day plan

Day 1: Run RocketRank analysis, publish Organization/WebSite JSON-LD, fix titles/descriptions.
Day 2: Rewrite /pricing answer-first; add 3 pricing FAQs; publish FAQPage JSON-LD.
Day 3: Create /security with concrete bullets (compliance, data, retention, subprocessors).
Day 4: Publish /compare/competitor-1 with a tight table and 2 honest trade-offs.
Day 5: Publish /use-cases/startups and /integrations grid.
Day 6: Ship /llm_qa.json and /llm-sitemap.json (see JSON-LD & Q&A Bundles).
Day 7: Validate in Rich Results, run the 5 prompts across engines, log presence/citations, iterate.

8) FAQ

Is this just “good content” with extra steps?
It’s “good content made legible to machines.” The difference is answer-first writing, durable URLs, and explicit structure.

Do I need backlinks?
Authority still matters—but for consideration answers, clarity and structure can get you cited even with a modest backlink profile.

What about brand safety/hallucinations?
Ambiguity invites hallucination. Publish crisp facts (and dates). If assistants still err, refine your pages and Q&A bundle to preempt the mistake.

Final step

Don’t guess—Analyze your site with RocketRank and ship the artifacts that make assistants choose you.