Verified against ChatGPT · 2026-08-09
Map a paid-acquisition funnel stage by stage so every ad dollar has a named next step
Builds a paid-acquisition funnel around what actually happens after someone clicks an ad — the specific asset, channel, and trigger at each stage — instead of a generic awareness/consideration/decision diagram that doesn't say who does what.
The prompt
Ready to copy — highlighted parts are example details you can swap.
Map the paid-acquisition funnel for this specific offer, stage by stage, so that every stage names a real asset, a real channel, and the specific event that moves someone to the next stage. Do not produce an abstract awareness/consideration/decision diagram — every stage must be concrete enough that someone could build it this week. OFFER AND PRICE POINT B2B SaaS analytics tool, $400/month, annual contracts preferred. CURRENT PAID CHANNELS IN USE LinkedIn ads to a gated whitepaper, Google Search on branded and competitor terms. KNOWN DROP-OFF STAGE Whitepaper download to demo request — high download volume, very few demo bookings. SALES CYCLE LENGTH Average 45 days, involves at least one internal stakeholder besides the first contact. EXISTING RETARGETING ASSETS A 3-email nurture sequence after whitepaper download, and a customer case study video not yet used in ads. For each funnel stage, name: the specific ad or content asset a prospect sees at that stage, the channel it runs on, the trigger event that moves them forward (a click, a form fill, a watch-through threshold, an add-to-cart), and the one metric that proves the stage is working versus leaking. If sales_cycle_length is longer than a single session, the funnel must include at least one retargeting or nurture stage between first click and purchase — a funnel that jumps straight from ad click to purchase for anything with a multi-day consideration period is describing wishful thinking, not the actual path a buyer takes. Build the stage immediately before and after known_drop_off_stage in more detail than the rest of the funnel, since that is where the actual problem lives, and propose one specific change to the asset or trigger at that junction rather than a general "improve nurture" note. If existing_retargeting_assets are given, slot them into the funnel where they actually belong rather than inventing new assets from scratch when a usable one already exists. OUTPUT FORMAT A numbered stage-by-stage table: Stage | Asset | Channel | Trigger to next stage | Health metric. Follow it with a short paragraph on the known_drop_off_stage specifically — what's likely happening there and the one change to test first. End with a one-line gap check: any stage in the table with no assigned owner-ready asset yet, named honestly rather than papered over with a placeholder.
Customize
Optional — swap in your own details for the highlighted parts above.
Why this works
GPT-5.1's default response to "map a marketing funnel" is the textbook three- or four-stage diagram it was trained on thousands of times, because that shape is the highest-frequency pattern for the phrase — it is correct in the abstract and useless in practice because it names no actual asset, channel, or trigger a team could build against this week. Requiring a named asset, channel, and trigger event per stage forces the model out of that generic template and into a mode where it has to commit to specifics, which is where the actual value of a funnel map lives — an unbuildable funnel is not a funnel, it's a diagram. The instruction to add a mandatory retargeting stage whenever the sales cycle exceeds a single session directly counters a structural blind spot: language models asked for "a funnel" tend to default to the simplest possible path (ad click to purchase) unless explicitly told the offer's price point and consideration period make that path implausible, and for anything with a multi-stakeholder B2B sales cycle that default path is simply wrong. Concentrating detail on the stage immediately before and after the known drop-off point, rather than spreading equal attention across every stage, mirrors how a real growth analyst would spend their limited time — the rest of the funnel matters less than fixing the one leak that's actually costing pipeline, and asking the model to weight its output that way keeps the response focused on the decision that matters rather than exhaustively even-handed across a diagram nobody asked to have equally documented.
What you get back
Stage 3 — Whitepaper Download to Demo Request | Asset: 3-email nurture sequence (existing) + unused case study video | Channel: Email + LinkedIn retargeting | Trigger: video watch-through past 50% triggers a demo-request CTA email | Health metric: download-to-demo conversion rate. Diagnosis: high download volume with low demo bookings suggests the whitepaper is answering the prospect's question well enough that they don't feel an urgent need to talk to sales yet — inserting the case study video mid-nurture, timed to a stakeholder-relevant pain point, is the first thing to test rather than adding another generic "book a demo" email.
Verified against
ChatGPT GPT-5.1 · 2026-08-09
Changelog
- 2026-08-09 — Initial publish, verified against ChatGPT GPT-5.1.
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