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Case Study ยท Meta Top-of-Funnel

A leading fitness industry brand

Lifted a fitness industry brand's Top-of-Funnel Meta ROAS +53% in six months, while monthly Bookings climbed 24% to a record $886K.

+53%

Meta ROAS, transition floor to exit

+24%

Monthly Bookings vs agency baseline

−30%

CPL trajectory during the engagement

The Result

Meta ROAS, monthly

December 2024 through June 2025. Engagement period shaded.

Fitness Industry Meta ROAS, December 2024 to June 2025 ENGAGEMENT 1.13 0.93 1.41 0.80 1.00 1.20 1.40 1.60 Dec '24 Jan '25 Feb Mar Apr May Jun ROAS
Pre-engagement Engagement Engagement period

What was broken

The brand inherited a Top-of-Funnel Meta acquisition account from an incumbent agency that was mis-managing spend quite drastically. Meta is their primary TOF acquisition channel. I started transitioning the account in January. January 2025 should have been peak season, the year's strongest New Year demand window for their category. Instead it was the ROAS floor of the prior twelve months, one of the worst months on record despite the calendar saying otherwise. Spend hit $825K and bookings did not keep pace.

The diagnosis was structural and creative: too many campaigns with low performance eating significant budget. Creative had stopped grabbing attention, and Meta's algorithm did not respond well to the engagement slump that followed.

What I did

01 Budget reallocation
02 A fresh video assembly line
03 Cross-platform priming

Step 01Budget reallocation.

The account had dozens of campaigns competing for spend, with CPLs ranging from one campaign at $30 to others at $90 and up. I consolidated aggressively, cut the campaigns burning budget at 2 to 3 times the account average, and concentrated spend on what was already working. The CPL effect was immediate, with the account-wide trendline bending down within weeks of full operating control.

Step 02A fresh video assembly line.

Meta rewards fresh inventory. The brand's creative had stalled, so I built a system to keep new video coming in on a real schedule. The first attempt was UGC creators. Quality was sub-par. So I pivoted, taking a page from Nike's playbook: I started reaching out to trainers, not creators, to tell their stories. Day in the life. Biggest fears. What they loved about helping clients. What advice they would give someone starting out. Real, specific, human. Nothing directly about the product.

The mechanics were an assembly line. Small budget for outreach. Content flowing in continuously. Editors briefed on top performers and why I thought those were winning. Iteration on the winners running alongside net-new concepts, on a fixed cadence so the algorithm always had options to compare against.

The Iteration Pattern

Meta creative launches, monthly (evergreen)

September 2024 through June 2025. Engagement period shaded. Promotional BFCM ad launches excluded for apples-to-apples comparison.

Creative launches per month, excluding BFCM, September 2024 to June 2025 ENGAGEMENT PRE-ENGAGEMENT AVG: 24/mo ENGAGEMENT AVG: 76/mo 11 14 25 47 76 112 44 58 40 127 0 35 70 105 140 Sep '24 Oct Nov Dec Jan '25 Feb Mar Apr May Jun NEW ADS
Pre-engagement Engagement Engagement period

Excluding BFCM promotional creative, monthly evergreen launches went from a ~24/mo agency-period average to ~76/mo across the engagement, a 3.14x increase. A few things the data taught us:

The Attention Rate

Meta hook rate, monthly (video, spend-weighted)

January through June 2025. Spend-weighted across video creative running during the engagement.

Meta hook rate, January through June 2025 ENGAGEMENT 15.5% 28.1% 23.2% 10% 15% 20% 25% 30% Jan '25 Feb Mar Apr May Jun HOOK RATE
Engagement (Jan through Jun 2025)

The proof of this work wasn't only in CPL. The bigger movement was in hook rate, monthly revenue, and backend ROAS.

Step 03Cross-platform priming.

I rendered the top-converting Google search terms as headline banners directly into the Meta videos: the exact high-intent phrases users were typing in. When users who had seen those videos later searched those phrases on Google, they were measurably more likely to engage. The two channels stopped being separate buckets and started reinforcing each other.

What happened

Hook rate climbed throughout the engagement and peaked at 28.1% in April. CPL came down from a $53 January floor to $37 at exit, a 30% improvement on the engagement trajectory.

Monthly Meta-driven Bookings climbed from a $716K agency-period average to $886K in June. That is a 24% lift at the exit month and a $170K-per-month increase against the agency baseline. AOV moved from $696 to $777, a 12% lift. Fewer leads converted on a raw percentage basis, but the ones that did spent meaningfully more. That is what quality traffic looks like in revenue terms: lower lead cost, higher value per customer.

The Revenue Climb

Meta Bookings, monthly

July 2024 through June 2025. Engagement period shaded. Vertical dashed line marks the start of full operating control in March 2025. Source: client backend reporting.

Meta Bookings, monthly, July 2024 to June 2025 ENGAGEMENT FULL OPERATING CONTROL → AGENCY AVG: $715K/mo FULL-OP AVG: $845K/mo $730K $697K $860K $886K $600K $700K $800K $900K Jul '24 Sep Nov Jan '25 Mar May Jun BOOKINGS
Agency-managed Transition (Jan through Feb) Full operating (Mar through Jun)

ROAS climbed with all of it. From the 0.93 January floor to 1.41 at exit, a 53% lift. $4.2M in Meta spend stewarded across the six months. Total account ROAS (Meta plus Google plus TikTok) rose from 2.01 in December 2024 to 2.57 by June 2025, which confirms the Meta gain was real lift, not credit shifting between channels.

A note on the data. CPL above comes from Meta's reporting. Every other metric on this page (Bookings, ROAS, AOV, conversion rates) comes from the brand's backend reporting, not Meta's claimed attribution. Same approach on the Google case study. When platform attribution drifts (and it does), backend numbers are the only ground truth I trust.

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