Paid campaigns were driving traffic straight to a booking page, and the funnel was failing in two directions at once. The leads that did convert weren't ready to buy, burning the sales team's time on calls that went nowhere. And the leads that were actually qualified weren't converting at all: the jump from cold ad to calendar booking was too big an ask for someone making a serious decision.

Same structural flaw, two different failure modes. The targeting and creative weren't the problem; the funnel was just asking for too much, too soon.

I redesigned the acquisition flow around an 8-stage quiz architecture: ad → quiz landing → questions → email capture → results page → segmented nurture → strategy call → customer. The quiz sorted leads into three tracks (Beginner, Intermediate, Advanced), each with a different follow-up from that point forward.

Behavioural lead scoring triggered sales outreach automatically once a contact crossed 40 points. The results page got rebuilt as the trust moment it should be: highest-intent point in the funnel, and it had been treated as an afterthought.

The infrastructure ran on HubSpot: contact segmentation, behavioural lead scoring (threshold: 40 points to trigger sales outreach), and automated nurture sequences branching by quiz result track. Email sequences were written and scheduled for each of the three lead segments, with subject-line and content variation built in from the start. Every touchpoint from quiz entry to sales call was tracked, measurable, and improvable.

At a growth agency, every client has a different business model, a different audience, and a different definition of success. The job isn't just running campaigns; it's getting inside each vertical fast enough to build the right strategy, not just apply the same playbook. Three industries in parallel means three sets of KPIs, three audiences, and three completely different creative angles, without letting any of them slip.

Hospitality: booking-driven campaigns where urgency and social proof do the work. Retail: product-led creative with audience segmentation timed to inventory cycles. Legal: authority-first content where trust signals matter more than volume. Each vertical required a different creative and targeting strategy, but the same analytical foundation underneath: define the conversion event, build toward it, measure everything that matters.

Alongside the agency work: my team won the TOFU Marketing Hackathon run by SURU Labs, building a complete channel strategy for THORN under competition pressure. That's where the multi-client rigour and the strategic instinct had to show up at the same time.

On Meta Ads and Google Ads, I managed 5–10 active ad sets per client simultaneously, setting up campaigns from scratch, defining audience targeting, and running 2–4 creative variants per campaign. Performance was monitored weekly; targeting and creative decisions were adjusted based on real data, not instinct. Alongside paid: segmented email campaigns with list management, subject-line A/B testing, and open rates consistently in the 15–25% range. Content production covered social, email, blog, and website copy, all produced and scheduled across 5+ active client accounts at the same time.

Across every client, content wasn't just being produced; it was being structured to answer specific questions. Blog posts, service pages, and social copy were built around the exact phrases a potential customer would type into Google or ask an AI assistant. Answer Engine Optimisation meant treating every content piece as a candidate for a featured snippet or a direct AI-generated answer: structuring headers as questions, leading paragraphs with direct answers, and building FAQ sections that matched real search intent. For the hospitality client, this meant owning "best Airbnb in [location]"-style queries. For the law firm, it meant structuring service pages so that Google's AI Overview would surface them for legal intent searches. For the jewelry retailer, it meant product descriptions built to answer "what is the best gift for X" directly. Same rigour, three completely different answer architectures.

Beats by Dre competes in a market where purchase decisions are emotional and identity-driven. The brief: understand what's actually driving loyalty and repurchase, and where the customer journey breaks down for people who considered but didn't convert. The challenge was designing research that surfaced honest behavioural signals, not just socially acceptable answers.

I designed custom surveys in Google Forms structured around the AIDA framework, mapping Awareness, Interest, Desire, and Action signals across different buyer segments. Responses were analysed for patterns in decision triggers, loyalty drivers, and conversion drop-off points.

Insights were synthesised and delivered via two formats: a Gamma dashboard for stakeholder review, and a ManyChat chatbot that let stakeholders query findings interactively, turning a static research output into a navigable insight system.