How many prompts should I track?

50–150 is the band where most B2B SaaS teams operate. Below 30, you're missing pipeline-driving long-tail. Above 200, the signal-to-noise drops and the dashboard becomes unreadable. Lantern's tier structure is built around this 50/100/200/500 banding.

Should I track competitor branded prompts?

Yes for the brands you can credibly displace, no for the rest. 'Profound alternatives' (assuming you're a Profound alternative) is a 5-intent prompt for you. 'Salesforce' is too broad and doesn't drive your pipeline.

How do I find new prompts to track?

Three sources: (1) HubSpot Marketing Analytics > Sources > Search > what your existing organic traffic searched, (2) SEO tools' related-keywords on your top pages, (3) ask your sales team what prospects say in discovery — those are the prompts that drove the call.

Can I automate the priority scoring?

Partially. The pipeline_produced column is data; the pipeline_intent score is judgment. Lantern automates the data side and surfaces a recommended priority list, but the final intent-scoring is human and should stay that way.

How to audit prompts for pipeline intent (2026)

Most AEO programs track 100+ prompts but only 20–30 of them drive measurable pipeline. The rest are vanity tracking — they generate citation count but no contacts and no deals. Here's the prompt audit that cuts the noise and concentrates AEO investment on the queries that actually move money.

Required tools

Your tracked prompt list (export from Lantern, Profound, etc.)
AI source data on your contacts (the ai_first_prompt property)
A spreadsheet for the scoring matrix
30 minutes of CMO + Head of Content time for the priority decision

The steps

Pull your full tracked prompt list and tag each by buyer journey stage

For each prompt, tag: TOFU (awareness — 'what is AEO'), MOFU (consideration — 'AEO tools comparison'), BOFU (decision — 'Lantern vs Profound', 'Lantern pricing'). BOFU prompts drive 60–80% of pipeline despite being only 20% of prompts.

Score each prompt 1–5 on 'pipeline intent'

5 = explicit purchase intent (vendor-name comparisons, pricing queries, 'buy', 'best for [my situation]'). 4 = strong consideration (specific use-case 'how to do X with Y'). 3 = problem-aware research (category-level). 2 = problem-discovery ('why is my X broken'). 1 = pure education ('what is X'). Score honestly — most TOFU prompts are 1s and 2s.

Cross-reference each prompt against your AEO-attributed contacts

If you've wired ai_first_prompt on the contact object, you can now see which prompts produced contacts. Add a column 'contacts produced last 90 days' to your prompt list. Many '5-rated' prompts will have produced 0 contacts — they're high-intent but you're not winning them yet.

Cross-reference against AEO-attributed pipeline (if available)

For prompts that produced contacts that became deals, sum the deal Amount. Add column 'pipeline produced last 90 days'. The intersection of high pipeline-intent score AND high actual pipeline = your top-priority prompts.

Compute a priority score: pipeline_intent × log(pipeline_produced + 1)

The log dampens runaway prompts (one big deal shouldn't make a prompt the only priority). The product surfaces prompts with both high intrinsic intent AND demonstrated revenue link. Sort descending; the top 30% are your priority list.

Drop the bottom 30% of prompts: low intent + zero pipeline

These prompts are wasting your monitoring budget and diluting your dashboard. Remove them. Most AEO tools price per prompt monitored, so this also reduces cost. Re-audit quarterly.

For the top 30%, build a content-coverage matrix

For each priority prompt, do you have a page that should rank? If yes, is it cited on 3+ engines? If no, why not (page outdated, missing schema, wrong angle)? If you don't have a page, what's the 1,000-word page you should publish? Output: a prioritized content backlog.

Common mistakes

Equating citation count with pipeline impact — many high-citation prompts are low-intent and produce no contacts.
Ignoring 5-intent prompts where you're not yet cited — these are your biggest opportunities, not your weak spots.
Auditing once and forgetting — re-audit quarterly because intent maps shift as the category matures.
Letting the SEO team's existing keyword list drive the prompt list — SEO and AEO prompts overlap only ~40%; an AEO-native list outperforms.

Where this fits in the AEO pipeline attribution stack

The steps above are one link in a longer chain. In order: you pick prompts to monitor, you track AI-referred sessions, you tag contacts in your CRM, you roll attribution up to the Deal object, you report pipeline dollars to the CFO. If you skip any link, the chain breaks and the number you quote to finance can't be defended in an audit.

If you're still evaluating which tool to run this workflow on, Lantern's AEO tool comparison hub has honest head-to-head pages for Profound, Scrunch, Peec AI, AthenaHQ, and HubSpot's own AEO product — scored on the dimensions that matter for a CMO buyer (CRM integration depth, reporting quality, prompt-scaling economics).

If you're about to walk this work into a budget review, the CFO's Guide to AEO Budget Defense has the memo template, the five-slide deck structure, the attribution-math cheat sheet, and the three most-common CFO objections with counter-arguments. It's the long-form companion to this how-to and was written for the renewal conversation specifically.

The operational rhythm that works: run the steps above once to set up, then review the output monthly in a 15-minute standing meeting with your Head of Growth and RevOps lead. Quarterly, re-audit your prompt list, your content backlog, and your attribution lookback window. Annual: present the full-year AEO ROI trend to the board. That cadence is what separates teams who ship an AEO dashboard once from teams who run AEO as an ongoing budget-defensible channel.

How to audit prompts for pipeline intent