Portfolio: education businesses we’ve helped grow

Before and after

The problem

For about five months, paid ads had stopped producing admissions. Enquiries still came in, but the founder described many of them as junk or low intent, and the setup was spending money without moving enough students to enrolment.

The course offer was strong. What held it back was weak conversion, and too much of the marketing sitting with the founder: website work and fixes, the Google Business Profile, coordinating SEO, and the daily running of campaigns, performance and lead quality.

So the job was to rebuild the growth system around the ads, and take the daily marketing load off the founder.

What we did

1. Took marketing off the founder’s desk. We took over the daily execution: website support, the Google Business Profile, SEO and local optimisation, social proof, and campaign testing, retargeting and reporting.
2. Tested before scaling. We launched several campaigns and ad copy variations rather than betting on one message, so the bigger budget decisions had evidence behind them.
3. Backed the winners. We compared lead cost, lead volume and response quality, moved budget towards the ads and audiences that performed better, and cut back the weaker ones.
4. Brought warm prospects back. Remarketing brought students who had already shown interest back into the decision journey, with repeated proof and follow-up.
5. Connected ads to admissions. We built a follow-up path behind the campaigns, with social proof and conversion-focused messages, so an enquiry had a route to admission.

The results

return on ad spend

12×

spent on ads, for a 7× return on investment

₹60K

marketing jobs taken off the founder’s desk

4

• Campaign testing cut the cost per lead by 41.5%. The optimised cycle brought 232 enquiries against the first cycle’s 172, on 21.1% less spend.
• Remarketing produced 231 demo-link actions from students who had already shown interest.
• With ₹55K+ spent on ads: 404 enquiries, 14 confirmed admissions and 7 more students in the pipeline.
• On ₹60K of ad spend: a 12× return on ad spend and a 7× return on investment.
• The founder now has more time for course delivery, student experience, operations, partnerships and the rest of the business, while we handle the day-to-day growth work.

Before and after

The problem

Last financial year, the platform enrolled 106 students across all 12 months.

Students rarely enrol after seeing one advertisement. They need to trust the platform, compare it with the alternatives and see proof of other students’ results, and the decision can take a long time. That is why the work here covers four channels.

What we did

1. Paid campaigns. We plan and run the platform’s ads for students looking for entrance exam preparation.
2. SEO. A lot of the website was overcomplicated, so we made simple fixes. We fixed the site architecture and the internal links, and removed the dead pages, the ones that drew no impressions or did not answer what students were searching for. We also made sure the pages meant to rank match what students mean when they search for a course.
3. Social media management. We manage the platform’s social media channels.
4. Reputation management. We look after what students find when they check the platform’s name, because proof from other students is part of how they decide.

The results

enrolments in 8 months of this financial year

143

return on investment

31×

in all 12 months of last financial year

106

• 143 enrolments in 8 months of this financial year, against 106 in all 12 months of the last.
• About 18 enrolments a month, against about 9 a month last year: twice the pace.
• Already 35% above last year’s full total, and the year is not over.
• A 31× return on investment.
• About a month into the SEO work, clicks from Google search had nearly tripled, from 240 in one 28-day period to 667 in the next.

Before and after

The problem

Students came to the website, used the free study material and left. The platform was running no campaigns of any kind, remarketing included, so no ad followed them once they had gone.

The decision is slow, too. Students usually buy six to seven months after they start using the free material, so a student found through cold ads costs money now and pays, if they pay, more than half a year later.

What we did

1. Tracked the students who were already interested. We added tracking pixels to the website, so the students who use the free material can be reached again.
2. Remarketed to them. Remarketing campaigns bring those students back to a platform they already know. The campaigns return 5× their ad spend.
3. Added a micro offer for the long wait. We introduced micro offers that push students to keep using the content in the months before they buy. Students pay a small fee for them, so the wait now earns money.
4. Built the funnel from scratch. There was no funnel to inherit. Pixel tracking, remarketing and the micro offer were set up together as one funnel.

The results

return on ad spend from remarketing

5×

of cold campaign ad spend paid back by a micro offer

50%

• Remarketing to students who had used the free material returns 5× its ad spend.
• The micro offer’s fee now pays back 50% of the ad spend on cold campaigns, so half the cost of finding new students comes back while they are still deciding.

Before and after

The problem

The college was running paid campaigns for its programs, and they were producing qualified leads. But it only followed up with people who submitted their details straight away.

Anyone who clicked an ad, visited the landing page, watched the content or showed interest without enquiring was not tracked or followed up. That was a large pool of warm prospects. They already knew the college, but needed more trust, more information or a reminder before taking the next step.

What we did

1. Qualified leads from new audiences. We refined the targeting, wrote program-specific messages, used qualification-focused creatives and lead capture, and optimised the campaigns for lead relevance as well as volume. Qualified enquiries from cold audiences were tracked.
2. Tracked the people who showed interest. The Meta pixel recorded who interacted with the college’s campaigns and pages, and we built remarketing audiences from that engagement.
3. Separate messages for warm prospects. Follow-up ads dealt with common concerns and decision barriers, and reintroduced the program to people who had shown interest but not enquired.
4. Brought them back into the admission journey. Remarketing campaigns kept the college in touch with interested prospects who had left without enquiring.

The results

lower cost per lead from remarketing

65%

as many leads for the same budget, from remarketing

2.9×

• The college began recovering prospects who would previously have been lost after their first interaction.
• Remarketing leads cost ₹70.87 each, against ₹202.52 for a new qualified lead: about 65% less.
• At those costs, the price of one new qualified lead buys nearly 2.9 remarketing leads.
• The college gained a second source of enquiries from its own traffic and audience data, without relying entirely on new audiences.
• The admission funnel now covers both first-time discovery and the follow-up that brings interested students back.

Before and after

The problem

The consultancy was generating more than 300 leads through basic Meta lead form campaigns. The volume looked strong, but most enquiries were irrelevant, unqualified, or outside the services the consultancy could provide.

Out of those 300 leads, only one or two were positive. The counselling team spent most of its time calling and following up with the rest.

What it needed was a way to find the serious, serviceable students before a counsellor picked up the phone.

What we did

1. Clearer targeting and messaging. We rebuilt the audience targeting, campaign messaging and creatives around qualification, aiming for relevance and enrolment potential rather than the lowest possible cost per lead.
2. Qualifying questions at the start. A structured lead capture asked about each student’s eligibility, requirements and intent.
3. Filtering and sorting. Prospects the consultancy could not support were filtered out, and the rest were segmented by suitability and readiness.
4. The strongest leads first. Counsellors got the stronger prospects first, and tracking continued past the first lead submission.

The results

positive-intent prospects, against 1 or 2 before

12

less manual screening for counsellors

80%

counselling hours saved per 300 leads (our estimate)

20-40

• Lead volume went from 300+ to 60 qualified leads, and 40 of the 60 (66.7%) were students the consultancy could genuinely support.
• 12 of those 40 showed positive intent and the potential to move forward, against one or two out of 300 before.
• 240+ irrelevant leads came off the counsellors’ list, about 80% less manual screening.
• Counsellors could concentrate on 40 serviceable students instead of searching through 300+ enquiries: about 87% fewer leads needing attention.
• The consultancy now judges its marketing by serviceability, intent and enrolment potential, not lead volume.

The hours saved are an estimate: 240 fewer leads to screen, at 5 to 10 minutes each for a first call, qualification and CRM update, comes to about 20 to 40 hours, or 2.5 to 5 working days, for every 300 leads.

Before and after

The problem

The business teaches children memory methods. Before this campaign it had been unable to get enrolments.

What we did

1. Started from scratch. We built a new campaign for Africa and launched it in August.
2. A webinar, then a follow-up. Most of the campaign’s enrolments came this way. The client’s tracker has a column for each step: whether the student attended the webinar, and how the follow-up ended.
3. A direct route as well. One of the 21 enrolled after a direct consultation instead of the webinar.

The results

students enrolled in the course

21

from launch to 21 enrolments

1 month

of the 21 attended the webinar first

19

• 21 students enrolled in the course in the campaign’s first month, August.
• 19 of the 21 attended the webinar first, and one enrolled after a direct consultation.

Before and after

The problem

The coach was running paid campaigns but getting a high volume of low-quality, irrelevant enquiries. The team was spending time on prospects who were not serious, did not understand the offer, or were not ready to invest.

The campaigns produced leads but not consistent sales opportunities, and the fix had to work without a large monthly advertising budget.

What we did

1. Rebuilt around the ideal client. We refined the ideal client profile from the coach’s best-fit customers and repositioned the offer around a specific problem and outcome.
2. Ads that filter. Pain-specific ads pre-framed the service and discouraged casual and irrelevant enquiries before anyone filled in a form.
3. A qualification funnel. Questions on each prospect’s situation, needs and readiness sorted leads into qualified and highly interested.
4. Immediate WhatsApp follow-up. An immediate WhatsApp journey reinforced the offer and encouraged the next step, and the highly interested leads got faster personal follow-up.
5. Refined continuously. The coach’s feedback on lead conversations went straight back into the campaigns.

The results

of enquiries qualified

80%

highly interested prospects in one month

22

of qualified leads were highly interested

30%

• Leads cost ₹162.01 each on average, counted in Meta Ads Manager when a prospect reached the appointment page.
• 94 leads in the month, and 80% of them qualified: about 75 people worth a conversation.
• Only about 20% were low quality or unsuitable, 19 of the 94.
• 30% of the qualified leads were highly interested: about 22 strong opportunities, nearly 1 in every 4 leads.
• The coach could spend sales time on those 22 instead of treating every lead the same.