Week 2 · Intent-Bucket Mapping for Healthcare
Turn raw conversational queries into a working bid strategy. This week you'll build a four-stage intent map, learn how CPC bands move across stages, and pressure-test it against a real anonymised ICG engagement.
What you'll learn this week
Last week you set up your first ChatGPT Ads account and watched the conversational auction behave nothing like a keyword auction. This week the job is different: you're going to take the raw stream of queries hitting your healthcare account and sort it into a small number of intent buckets that each get their own bid, their own copy angle, and their own landing destination.
By the end of this session you'll have a four-stage bucket map you can apply to any specialty, a working sense of how CPC bands stretch across those four stages in the Indian healthcare vertical, and a template you can adapt for whichever specialty your account serves — fertility, dermatology, dental, diagnostics, or a multi-specialty hospital group. The only prerequisite is Week 1: you need to already understand that ChatGPT Ads runs an intent-first auction and that conversation-completion attribution, not click attribution, is what the platform optimises toward. If either of those feels shaky, go back to Week 1 before continuing, because everything below assumes it.
Set aside about an hour. Bring four to eight weeks of query data from whatever channel you currently have — Search Console, your existing paid search account, or your AI-assistant referral data if you're already live. You'll use it in the exercise at the end.
Core concept 1 — why buyer readiness, not keyword volume, is the sorting axis
The instinct almost every team brings into their first bucket map is to sort by topic. Fertility queries in one pile, IVF-cost queries in another, clinic-comparison queries in a third. That instinct is wrong, or at least incomplete, and it's the single biggest reason first-draft bucket maps underperform once real budget starts flowing through them.
Topic tells you what the person is talking about. Buyer readiness tells you how close they are to converting. A query about symptoms and a query about booking a consultation can both mention the same specialty, the same city, even the same clinic name, and still sit at completely different points on the buyer journey. If you bucket by topic alone, you end up bidding the same amount for a person who is three weeks away from any decision and a person who is three minutes away from calling — and that mismatch is exactly where budget leaks.
The fix is to sort by intent stage first, and treat topic as a second-layer tag inside each stage, not the primary axis. We use four stages for almost every healthcare account we run: awareness, evaluation, comparison, and conversion-ready. Awareness queries are exploratory — a person typing something close to "why does my skin break out after 30" hasn't decided they need a dermatologist yet, they're still working out what's happening to them. Evaluation queries have named a category of solution — "best treatment options for adult acne" — but haven't picked a provider or even a city. Comparison queries have narrowed to a short list and are actively weighing it — "dermatology clinic vs skin specialist for acne scarring in Pune." Conversion-ready queries name a specific, bookable action — "book dermatologist consultation Pune this week."
Each of those four stages behaves like a different market inside the same auction. Awareness queries are high-volume and cheap because almost anyone researching a topic falls into that bucket, and the platform doesn't yet have strong signal that a conversion is close. Conversion-ready queries are low-volume and expensive because the pool of people typing something that specific is small and, on conversation-completion attribution, disproportionately likely to convert. If your bucket map doesn't separate these, your bidding can't separate them either, and you'll structurally overpay for awareness traffic while underbidding the conversion-ready traffic that actually pays for the account.
One more thing worth internalising before you build your own map: the four-stage structure is a starting point, not a law. Some specialties genuinely compress to three stages because the evaluation and comparison behaviours blur together — diagnostics is a good example, where people often move from "what does this test show" straight to "book blood test near me" with very little comparison shopping in between. Others, particularly high-consideration specialties like fertility and cosmetic surgery, sometimes justify a fifth micro-stage between comparison and conversion-ready, where the person is comparing pricing and financing rather than clinical quality. Start with four, and only split further once your own query data gives you a clear reason to.
Core concept 2 — setting CPC bands per stage without guessing
Once your queries are sorted into stages, the next job is deciding what each stage is worth to you in CPC terms. This is where teams either get conservative and starve the conversion-ready bucket of budget, or get aggressive everywhere and blow through spend without the volume to show for it. Neither works. The way through is to build your CPC bands from your own conversion data, stage by stage, rather than importing a rule of thumb from a different vertical or a different platform.
Start with your conversion-ready bucket, because it's the easiest to price. You already know, or can estimate from four to eight weeks of data, roughly what percentage of conversion-ready conversations turn into a booked lead, and you know what a booked lead is worth to your business — either from your average deal value or from whatever cost-per-qualified-lead figure your finance side already tracks. Work backward from that: if a conversion-ready lead is worth ₹4,000 to you and one in five conversion-ready conversations converts, you can justify a CPC materially higher than what you'd pay for an awareness click, because the unit economics support it.
Then set the other three stages relative to that anchor, not in isolation. In the Indian healthcare accounts we run, a rough but consistently observed pattern is that comparison-stage CPC sits at roughly half to two-thirds of conversion-ready CPC, evaluation sits at roughly a quarter to a third, and awareness sits at a fifth or less. Those ratios move by specialty — fertility and cosmetic surgery tend to compress the gap between comparison and conversion-ready because the decision cycle is long and comparison-stage engagement is itself a strong predictor of eventual conversion, while high-volume, low-consideration categories like general dermatology or dental cleaning stretch the gap wider because a much larger share of comparison-stage traffic never converts at all.
| Intent stage | Typical query shape | Relative CPC band | Primary optimisation goal |
|---|---|---|---|
| Awareness | Symptom or condition question, no solution named | Baseline (1x) | Volume + brand presence |
| Evaluation | Solution category named, no provider named | 1.5x–2x baseline | Education + list-building |
| Comparison | Provider or option shortlist named | 3x–4x baseline | Differentiation + trust signals |
| Conversion-ready | Specific action + location named | 5x–8x baseline | Lead capture |
Why not just bid conversion-ready everywhere? Because conversion-ready volume is small by definition — it's the narrow top of the funnel, not the wide base. An account that only bids on conversion-ready queries runs out of impressions within days and stops learning. The awareness and evaluation bands exist to keep the account fed with volume the platform can use to refine who eventually reaches conversion-ready, which under conversation-completion attribution matters more than it did under click attribution.
Recalibrate these bands monthly once the account is live, and weekly for the first six weeks of any new campaign. Auction conditions in ChatGPT Ads are still settling in the Indian healthcare vertical through 2026, and a band that was right in July can be meaningfully off by September as more advertisers enter your specialty and city combinations.
Core concept 3 — building specialty-specific bucket templates
A generic four-stage map is a useful starting skeleton, but it will underperform a specialty-specific version within a month, because the vocabulary, the pacing between stages, and the CPC band widths all shift by specialty. Building the specialty-specific version is mostly a vocabulary exercise plus a pacing check, and it's worth doing properly rather than treating it as a formality.
Start with vocabulary. Pull your query data and, within each of the four stages, list the actual phrases people use. Fertility awareness queries lean heavily on symptom and timeline language — "trying to conceive for a year," "irregular cycles causes." Dermatology awareness queries lean on visual and comparative language — "why is my skin worse than before," "acne types explained." Dental awareness queries are often pain-driven and urgent even at the awareness stage, which is itself a specialty-specific insight, because it means dental awareness queries sometimes deserve a higher CPC band than the generic template would suggest, since urgency compresses the gap between awareness and conversion-ready.
Next, check pacing — how long, on average, does a person in your specialty take to move from one stage to the next. Diagnostics and general dentistry tend to move fast, often within a single session, because the decision is low-risk and low-cost. Fertility, cosmetic surgery, and complex orthopaedic procedures move slowly, sometimes over weeks, with the same person re-entering your funnel at the evaluation or comparison stage multiple times before ever reaching conversion-ready. That pacing difference should change how you interpret repeat queries from what looks like the same conversational thread: in a fast-pacing specialty, a repeat evaluation-stage query after a week is a warning sign that something in your comparison-stage content isn't landing; in a slow-pacing specialty, it's entirely normal and shouldn't trigger a bucket reassignment.
Finally, build the template as a living document, not a one-time spreadsheet. The cleanest version we've found is a four-row table per specialty — one row per stage — with columns for the representative query phrases, the current CPC band, the landing destination, and the primary ad copy angle. Keep it in whatever tool your team already uses for campaign planning, and treat every monthly recalibration as an update to that same document rather than a fresh build, so you can see how the bands and vocabulary drift over time.
Worked example — a real ICG engagement pattern (anonymised)
One of our multi-specialty hospital group engagements gives a useful, honest picture of how this plays out in practice. The client runs several specialties under one brand, and when we inherited their ChatGPT Ads account it had a single, undifferentiated bid across every query — a classic topic-only bucket, not an intent-stage bucket. Spend was being absorbed almost entirely by high-volume awareness queries in their two most-searched specialties, while conversion-ready queries in lower-volume specialties were losing every auction because the bid simply wasn't competitive at that stage.
We rebuilt the account around the four-stage structure described above, running one bucket map per specialty rather than one shared map for the whole hospital group — five specialties meant five templates, each with its own vocabulary and CPC bands, because a shared template would have flattened exactly the differences that mattered. The fertility specialty ended up with the widest gap between awareness and conversion-ready CPC of the five, consistent with the long, high-consideration decision cycle described above. The dental specialty ended up with the narrowest gap, because dental queries in this client's data were urgency-driven even at the awareness stage, so the four bands compressed toward each other more than the generic template predicted.
Within the first month of running specialty-specific buckets instead of one flat bid, the account's conversion-ready bucket — previously starved of budget — began winning a meaningfully higher share of its auctions, and the overall blended cost per qualified lead across the hospital group's ChatGPT Ads spend improved, because budget that had been diffusing across cheap, high-volume awareness traffic in the two loudest specialties was now reaching the narrower, more valuable conversion-ready traffic across all five. The bucket maps have been recalibrated monthly since, and two of the five specialty templates have shifted noticeably from where they started, which is itself the point: a bucket map built once and never revisited degrades quietly, and the degradation shows up as rising cost per lead long before anyone notices the underlying cause.
Exercise for you to do this week
Time required: roughly 45–60 minutes.
- Pull four to eight weeks of query-level data from your primary channel — existing paid search, Search Console, or your live AI-assistant referral data if you have it.
- Read through at least 100 queries and manually tag each one into one of the four stages: awareness, evaluation, comparison, conversion-ready. Resist the urge to sort by topic first — sort by readiness.
- For each stage, write down three to five representative query phrases in your own specialty's vocabulary. This becomes the first draft of your specialty template.
- Estimate a conversion-ready CPC anchor using your own lead value and rough conversion rate, then apply the relative band ratios from the table above to draft awareness, evaluation, and comparison CPC bands.
- Check pacing: pick five queries that look like they came from the same person across multiple sessions, and note roughly how long the gap was between stages. Does it match a fast-pacing or slow-pacing specialty pattern?
- Save the result as a four-row table — one row per stage, columns for query phrases, CPC band, landing destination, and copy angle. This is your working bucket map going into Week 3.
Don't aim for a perfect map on the first pass. The goal this week is a defensible first draft you can pressure-test with real spend and refine monthly — the recalibration habit matters more than getting the first version exactly right.
Common failure modes to avoid
- Sorting by topic instead of readiness. This is the single most common mistake, and it's covered in detail above — if your bucket map's first sort is specialty or keyword rather than intent stage, rebuild it before moving to Week 3.
- Copying another specialty's template wholesale. A fertility bucket map and a dental bucket map should never look identical. If two of your specialty templates are near-duplicates, one of them almost certainly wasn't built from that specialty's own query data.
- Setting CPC bands once and never revisiting them. Auction conditions move, especially while ChatGPT Ads adoption is still growing across Indian healthcare advertisers through 2026. A monthly recalibration habit is not optional overhead — it's the difference between a bucket map that keeps working and one that quietly decays.
- Treating a repeat query as a bucket reassignment trigger without checking specialty pacing. A person re-entering the evaluation stage after two weeks means something different in fertility than it does in general dentistry — check pacing before you react to it.
- Building five stages when the data only supports four. More granularity feels rigorous but often just adds noise once your query volume per bucket gets too thin to read reliably. Only split a stage further when your own data gives you a clear, repeated reason to.
