How a wellness centre in Bangalore ran Enterprise-tier ChatGPT Ads for corporate-B2B wellness-programme intent
The situation
Picture a wellness centre in Bangalore that had built a genuinely strong consumer-facing yoga, meditation and preventive-wellness practice over several years, with a loyal individual membership base and consistent word-of-mouth growth. In 2025, the centre's founders had identified a second revenue line they believed was underexploited: corporate wellness programmes sold directly to Bangalore's dense concentration of technology and services companies, many of which were actively expanding employee-wellness budgets as part of retention strategy in a competitive hiring market.
The centre had closed a handful of corporate contracts through founder relationships and referrals, but had no repeatable, scalable channel for reaching HR decision-makers at companies they didn't already have a personal connection to. Their existing digital marketing — Instagram content and a modest Google Ads Search budget aimed at individual consumers — wasn't built to reach or convert a B2B buyer, and cold outreach to corporate HR teams had a low response rate typical of unsolicited sales emails. The centre needed a way to reach HR professionals and founders at the specific moment they were actively researching employee-wellness vendors, which is a narrower, harder-to-reach audience than the centre's consumer marketing had ever targeted.
This is a fundamentally different buying motion from the individual-consumer wellness bookings that made up the rest of the centre's business, and it's why this scenario sits apart from a typical wellness-centre ChatGPT Ads account: the buyer is an HR function evaluating a vendor relationship on behalf of an organisation, with a multi-person approval chain and a budget cycle, not an individual booking a personal wellness session on impulse or after a short research window.
The ChatGPT Ads campaign structure ICG designed
Enterprise-tier gave this account the account-based conversation architecture the B2B motion required: a dedicated conversation bucket built and continuously retrained to detect corporate-wellness-programme intent specifically, distinct from any individual-consumer wellness conversation the same underlying keyword space might otherwise surface. This distinction mattered enormously for budget efficiency — a generic "wellness centre Bangalore" intent signal captures overwhelmingly individual consumer interest, and without a purpose-built B2B classifier, Enterprise-tier budget would have been wasted competing for the same low-value consumer conversations the centre's existing Google Ads campaign already handled adequately.
The classifier was trained against phrasing patterns specific to corporate buyers: conversations referencing "employee wellness programme," "corporate wellness vendor," "wellness benefit for our team," comparative questions about programme structures or per-employee pricing, and conversations that referenced company size or HR-function language. When a conversation matched this pattern, it was routed into the high-value B2B bucket and bid aggressively, in the range of 8-12x the baseline individual-consumer bid, reflecting the materially higher lifetime value of a corporate contract relative to an individual membership.
Because the B2B sales cycle ran 4-7 weeks from first conversation to signed agreement in this scenario, single-conversation attribution wasn't sufficient — Enterprise-tier's account-based tracking matched conversations against named corporate domains where an HR contact's enquiry could be reasonably associated with a specific company, letting the centre's sales team see, at the account level, how many conversations from a given company had occurred across the sales cycle before a proposal was requested. This is meaningfully more sophisticated tracking than a typical consumer wellness account needs, and it's the specific capability that justified Enterprise-tier over a lighter-weight Scale-tier build.
Landing infrastructure routed B2B conversations to a dedicated corporate-wellness programme page distinct from the centre's consumer-facing pages — naming programme structures (on-site sessions, virtual sessions, hybrid), indicative per-employee pricing bands, and a direct "request a corporate proposal" form asking for company name, employee headcount and HR contact designation, none of which appeared on the consumer booking pages. Sales follow-up on every B2B-classified conversation went to a dedicated corporate-accounts team member rather than the centre's general front-desk booking staff, since the two conversations required entirely different follow-up skills — consultative B2B selling versus consumer appointment scheduling.
The compliance discipline
Because this centre's programme drew substantially on Ayurvedic and yoga-based modalities, AYUSH guidelines governed the framing of any wellness-outcome language in the B2B copy — no claims that the programme would cure or treat any medical condition, only permitted framing around stress-management support, general wellbeing and preventive-lifestyle benefit, consistent with how AYUSH-adjacent wellness advertising is required to describe itself regardless of whether the buyer is an individual or a corporate HR department.
NMC Section 6 and ASCI Chapter III applied as the baseline restriction against any outcome guarantee — no claim that the programme would measurably reduce sick days or improve productivity by a specific figure, since neither the centre nor ICG had controlled data to substantiate such a claim, and both frameworks prohibit unsubstantiated efficacy claims regardless of audience sophistication. This mattered specifically in the B2B copy because HR buyers often want quantified ROI language, and the discipline here was to frame the value proposition around programme structure and employee experience rather than promising a specific productivity or attendance metric the centre couldn't defend if challenged.
DPDP 2023 governed the corporate enquiry form, but with a materially different consent framing than a consumer health-data form would carry — this form collected business-contact information (company name, HR contact name, designation, business email) rather than personal health data, so the consent language reflected standard business-enquiry data handling rather than the more stringent sensitive-personal-data consent required on the centre's individual wellness-booking forms. Keeping these two consent frameworks clearly separated across the two lead types was a specific point of review, since conflating a B2B business-contact form with a patient-health-data consent standard would have been both legally imprecise and operationally confusing for the centre's own team.
The 90-day outcome pattern
Given the B2B classifier's need to mature and the inherently lower volume of a corporate-buyer audience relative to consumer wellness seekers, month one produced modest illustrative volume — roughly 8-12 B2B-classified conversations a week — with the centre's sales team using this period to refine which conversations genuinely represented HR buying intent versus individual employees casually asking about workplace wellness benefits without decision-making authority. By day 90, weekly B2B-classified conversation volume had climbed to an illustrative 20-25 a week as the classifier's precision improved and the centre's corporate-programme landing page accumulated enough citation history to surface more reliably in relevant conversational searches.
Of the cumulative B2B conversations tracked across the 90-day window, illustratively 18-22% progressed to a requested corporate proposal, and of those, roughly 25-30% converted to a signed programme agreement within the observed window, with several more still active in the sales pipeline at day 90 given the 4-7 week typical cycle length. In absolute terms this represented a small number of signed corporate accounts over the quarter — illustratively 4-6 — but each carried an annual contract value multiple times larger than the centre's average individual membership, making the unit economics favourable despite the lower headline conversation volume compared to the centre's consumer-facing channels.
In GA4, the B2B-classified traffic attributed to the AI Assistant channel showed a key-event rate on the proposal-request action that the centre's marketing lead considered strong relative to their prior cold-outreach conversion rate, though direct comparison was imperfect given the different nature of the two motions. The more meaningful internal signal was that this was the centre's first repeatable, non-referral-dependent channel for reaching corporate HR buyers — a structural gap the founders had identified as their biggest constraint on scaling the B2B revenue line, now partially addressed with a channel that, unlike founder-network referrals, could in principle scale independent of any one person's personal relationships.
What we'd do differently next time
The B2B intent classifier's first three weeks spent more calibration effort than necessary filtering out individual-consumer conversations that happened to use adjacent phrasing — an employee asking "does my company offer a wellness benefit" reads structurally similar to an HR buyer's research query without careful classifier tuning. Seeding the classifier with a larger, more deliberately curated set of known B2B phrasing patterns before launch, drawn from the centre's own past corporate-enquiry emails and call transcripts, would likely have shortened this calibration period by one to two weeks.
The centre's dedicated corporate-accounts team member was a single person managing the entire B2B pipeline from first conversation through proposal through contract negotiation, and by week eight this became a bottleneck as conversation volume grew faster than that one person's proposal-writing capacity — a second hire, or at minimum a templated proposal-generation process, should have been planned for before launch rather than reactively mid-quarter once the backlog became visible.
How this maps to your own vertical
If your wellness centre, clinic or healthcare-adjacent business has a genuine second revenue line selling into a B2B or institutional buyer — corporate wellness, insurer partnerships, or employer health benefits — rather than solely individual consumer bookings, the core lesson here isn't about wellness specifically: it's that a B2B buying motion needs its own conversation classifier, its own landing infrastructure, its own compliance consent framing, and its own sales follow-up process, run separately from your consumer channel rather than bolted onto it. Treating a B2B intent signal as just another segment of consumer intent wastes budget and produces a sales team confused about who they're actually talking to.
Enterprise-tier's account-based tracking is worth the additional structure specifically when your sales cycle runs multiple weeks and involves more than one stakeholder at the buyer's organisation — for a same-day or same-week consumer decision, that machinery is more than the buying motion needs.
