Yes — Lantern ships an API for prompt data, citation data, and attribution data. Most growth teams pipe the data into their existing experiment-tracking stack (Amplitude, Mixpanel, Segment) and run AEO experiments inside their normal dashboards.

How fast does data refresh?

Citation data refreshes daily. Attribution data (HubSpot writes) is near-real-time on session tagging and daily on opportunity association. Fast enough for weekly experiment cadence.

Can I run experiments at the prompt level?

Yes — this is the core use case. You can define 'prompt cohorts' and track content variants against each. The output is a pipeline-attributed experiment result per prompt cohort. No other AEO tool exposes data at that granularity.

$99/mo Starter includes the API and prompt-level data. Enterprise adds volume scale and custom data exports for very large prompt libraries.

AEO Pipeline Attribution for Growth Marketers: Experiments at the Prompt Level

Growth runs on experiments with observable outputs. AEO is the first channel where the atomic unit (prompt × engine × citation) is measurable but most tools don't expose it at that granularity. Lantern does, because Growth Marketers are the buyer.

What's stopping you

The three pain points most Growth Marketers bring to the first Lantern conversation in 2026 — in the language they use:

You're a 'Gen Marketer' — the AI-native generalist. Your tools haven't caught up.

Emily Kramer's 2026 term, adopted broadly. You run multi-channel experiments, ship landing-page variants, build custom GPTs for segments. Your tooling pool assumes a 2019 marketing stack. The AEO tool you evaluated last month had a dashboard you looked at once and closed. It did not give you an experiment-ready API or a prompt-level data export.

You want to A/B test a prompt. Your AEO tool reports weekly, not per-experiment.

Growth ops is daily or faster. Scrunch's 'weekly' data cadence is a deal-breaker for your experiment velocity. By the time you see the result, you've already moved to the next three tests. You need the data at the rhythm your experiment pipeline runs — not at the rhythm the vendor ships reports.

The 6x conversion lift from LLM traffic is real but you can't isolate which content is driving it.

Webflow's public stat: ChatGPT referrals convert 6x better than Google search. You've seen similar in your own HubSpot anecdotally. But you can't tell which specific piece of content is pulling those high-converting sessions. Can't optimize what you can't isolate.

What you're measured on

The KPIs on a Growth Marketer's review in 2026. AEO attribution shows up on the last one — and in 2026, that's the line that gets watched.

Growth Marketer KPI stack

Experiment velocity (tests / month)
Experiment win rate and lift magnitude
Per-experiment pipeline $ attribution
Conversion-rate delta vs. control
Time-to-signal on new prompt targeting
AEO-sourced conversion rate vs. other channels

Why AEO attribution matters specifically to a Growth Marketer

Growth Marketers run faster loops than the rest of marketing. The fundamental unit of your work is the experiment, and the fundamental input is observable data per experimental unit. Lantern is the AEO tool built for this rhythm — prompt-level data, daily cadence, API-accessible, with clean HubSpot writes so your existing experiment-tracking stack consumes it. You stop treating AEO as a 'strategic initiative' and start running it as a weekly experiment channel like any other.

“I run 8 experiments a week. I don't need a dashboard that updates weekly. Lantern is the first AEO tool built at the rhythm I actually work.” — The Growth Marketer, described in audience-brief voice

Three ways Growth Marketers use Lantern

The prompt-targeted content A/B

You ship two landing pages optimized for the same prompt cluster — different angles, different proof. Lantern tracks which one gets cited more, measures the CTR and conversion delta on AI-referred sessions, and writes the result to HubSpot. Full A/B loop in 30 days.

The extractability audit

For a tranche of 20 pieces, run one variant with schema + definitional sentence + list structure, one without. Lantern tracks citation-rate lift. Typical outcome: 2-4x citation rate on the extractable variant within 21 days. That becomes the production standard.

The prompt-harvest pipeline

Every week, Lantern surfaces new prompts competitors are being cited on that you aren't. You sort by buyer-intent signal and pipeline-potential, pick the top 3, and ship targeted content. This is the growth-ops version of keyword mining, at AI speed.

AEO prompts a Growth Marketer would track

The prompt set is where AEO programs either produce or die. Start with the buyer-intent queries your ICP types into ChatGPT when evaluating your category. A Growth Marketer's first-cut list typically looks like this:

1Compare [your product] vs [competitor] feature-by-feature
2[Your category] workflow for [specific use case]
3Is [your product] worth it for [specific ICP]

Expand to 50-150 tracked prompts within 30 days. Every prompt becomes a reportable unit — citation rank, content match, pipeline $ attributed. That's how AEO becomes a channel, not a buzzword.

Where this sits in the Lantern stack

Lantern is AEO pipeline attribution for B2B SaaS — AI citation monitoring across ChatGPT, Perplexity, Claude, and Gemini, wired to the HubSpot deal record so pipeline $ flows cleanly into your CRM-level reporting. V1 ships on HubSpot; Salesforce follows in V1.5. Pricing: $99/mo Starter, Enterprise for custom prompt/engine/brand scale.

If you're mid-evaluation against Profound, Scrunch, or AthenaHQ, the AEO tool comparison hub has head-to-head pages on each. If you're about to defend an AEO line item to the CFO, the CFO's Guide to AEO Budget Defense has the memo template, math cheat sheet, and objection handling for the renewal conversation.