Case Study 04 · DocTutorials
Medical EdTech · NEET PG, FMGE, INI-CET · India
Paid enrolments multiplied year on year. Acquisition cost went the other way.
DocTutorials prepares MBBS students and foreign medical graduates for NEET PG and FMGE, in a category owned by large, well-funded incumbents. I lead marketing as an adviser for the KIMS Group family office.
- Paid enrolments
- Multiplied
- Cost per acquisition
- Down ~⅓
- Signed creator collaborations
- Hundreds
- Creator discovery, automated
- Daily
FY25 → FY26
Same period
From a roster that did not exist
New NEET PG and FMGE creators every day
At the client's request this case study names the company but carries no absolute figures; growth is described in words.
01 — The Situation
A crowded category where every incumbent buys the same keywords.
Marrow, PrepLadder, DAMS, Cerebellum and Unacademy all compete for the same two to three lakh aspirants a year. Paid search alone could not be the growth engine; trust had to be earned where students already were.
Where it started
- Acquisition leaning on performance media in an auction every competitor also bids in.
- Influencer work ad hoc; no roster, no exclusion list, no outcome tracking.
- Content production gated on faculty time.
- Attribution loose enough that cost per acquisition could not be defended.
Where it is now
- An influencer engine running daily: discovery, scoring, ready-to-send outreach, exclusion roster, outcome tracker.
- AI-first marketing ops: lead qualification and predictive scoring inside the CRM, plus LLM content pipelines.
- An FMGE Instagram content engine with a faculty review board, and a scheduler that turns the master timetable into calendar invites for nineteen faculty.
- Measurement that makes cost per acquisition a number leadership can act on weekly.
02 — What I Did
Build the channel the incumbents could not simply outbid.
Every day
Discover and score
Nano and micro medical-education creators found automatically, then scored for trend and exam fit.
Then
Outreach, ready to send
A drafted message per creator, checked against a never-pitch roster of competitor-affiliated accounts.
Back into the loop
Track the outcome
Collaborations tracked to outcome, so the roster improves rather than simply growing.
- Biggest lever
Influencer engine
Daily automated discovery of nano and micro medical-education creators, scored for trend and exam fit, with a ready-to-send message for each and a never-pitch roster of competitor-affiliated accounts.
- Ops
AI-first marketing operations
n8n workflows, CRM AI and LLM content pipelines; lead qualification and predictive scoring so sales time goes to the students most likely to enrol.
- Content
FMGE Instagram engine
A four-agent system — planner, manager, designer, student reviewer — with a nine-persona faculty board, producing a 120-post monthly calendar.
- Measurement
Dependable attribution
Weekly cost-per-acquisition reporting a 16 to 40 person marketing team can run without me.
03 — The Result
Growth that came from earned trust, not a bigger auction bid.
The incumbents can always outspend a challenger on search. They cannot outspend it on hundreds of relationships with the creators students already follow.
Channel
Hundreds of signed creator collaborations from a roster that did not exist before the engagement.
Operations
Lead qualification and predictive scoring inside the CRM, so sales time goes to the students most likely to enrol.
Handover
Weekly cost-per-acquisition reporting a 16 to 40 person marketing team can run without me.
- Paid enrolments multiplied between FY25 and FY26.
- Cost per acquisition fell by roughly a third in the same period.
- Hundreds of signed creator collaborations from a standing roster, with a discovery engine that adds to it daily.
- A marketing organisation that runs on AI systems rather than on any one person's bandwidth.
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