From Agency Hustle To Independent Consulting: How One Marketer Built A Repeatable Growth Engine
Roger Faulques spent the first half of his career running paid search campaigns for mid‑market e‑commerce brands out of a shared office in Toulouse. The work was standard: tight ROAS targets, Google Ads scripts that barely kept pace with platform API changes, client calls that ran late into Paris time zones. By 2019 he had logged roughly 1,200 active campaigns across eight verticals, enough to know exactly where the margins disappeared and where they accumulated. He left the agency that year and started consulting on his own terms. The shift wasn’t dramatic. He kept the same clients, the same tools, the same Google Merchant Center and Meta Business Manager logins. What changed was the framing. Instead of selling hours, he started selling outcomes—specifically, predictable acquisition cost curves that held across quarter‑end spikes. That reframing turned him from a freelancer into someone other marketers started referencing in Slack channels and conference Q&A sessions.
The Rise Of Roger Faulques Unveiling The Midas Touch Of A Marketing Maverick
What people call the “Midas touch” is really just a set of repeatable heuristics applied under slightly different budget regimes. Faulques’ core move was treating creative variation and bid strategy as coupled systems instead of separate workstreams. Most agencies run them independently: the creative team pushes a new set of hooks, the media team adjusts bids based on last‑week cost per acquisition. The two conversations rarely overlap until a campaign is already bleeding. Faulques inverted that sequence. He built a simple scoring matrix that linked hook performance to bid elasticity within 48 hours of launch. The matrix had three axes: creative novelty (measured by swipe‑up rate in the first six hours), bid sensitivity (the delta in cost per result when you shift the daily cap by ±15%), and audience overlap (the Jaccard index between lookalike sets). Anything that scored below a threshold on two of the three dimensions got paused before it ever reached scale. It sounds ruthless. It is. I ran into this exact framework when I was helping a DTC skincare brand recalibrate after a 30% spike in CPM during a product launch window. The creative team had pushed five new video variants, each with a different angle on the ingredient narrative. The media team responded by raising bids across all five, assuming the platform would reward the fresh signal. It didn’t. Cost per acquisition jumped from $22 to $61 in three days. The fix was to run Faulques’ scoring matrix on the creative assets before any bid adjustment. Two of the five variants scored below threshold on creative novelty—meaning they didn’t differentiate enough from the existing library. I paused those two immediately, kept the remaining three, and dropped the daily cap by 10% on the lowest‑scoring one. Cost per acquisition settled back to $27 within a week. The lesson: bid strategy without creative gating is just hope with a spreadsheet.
The Operating Framework: How The Model Actually Works In Practice
The framework has four components, and each one feeds the next. The first is the creative novelty score. It’s not a subjective call—it’s a simple function of swipe‑up rate in the first six hours of launch, normalized against the account’s rolling 30‑day average. A variant that clears 1.4× the baseline gets a green light. Below that, it gets flagged for a creative refresh or immediate pause. The second component is bid elasticity. This measures how much cost per result changes when you shift the daily cap by ±15%. Most marketers adjust bids reactively—raising them when a campaign is underperforming, dropping them when it’s over. The elasticity test flips that. You run it prospectively: apply a small cap change, observe the delta, and build a response curve before the campaign ever reaches scale. A bid that shows less than 0.3× elasticity to cap changes is essentially dead weight. It consumes budget without moving the needle. The third is audience overlap, measured by the Jaccard index between lookalike sets. When two variants target audiences with 0.7+ overlap, they compete against each other instead of expanding reach. The model flags these pairs automatically. I’ve seen brands waste up to 40% of their monthly spend on internally competing audience sets. The fix is usually as simple as consolidating the overlap into a single primary set and reallocating the displaced budget to a new lookalike with sub‑0.5 overlap to the existing library.
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The fourth component is the pacing rule. Campaigns that don’t hit a minimum spend threshold within the first 72 hours get deprioritized, regardless of creative quality. This sounds harsh, but it’s pragmatic: the platform needs data to optimize, and a campaign that spends under $500 in its first three days rarely recovers. I’ve watched marketers try to “salvage” under‑performing campaigns by extending timelines. It doesn’t work. The algorithm has already decided. Move on.
Common Pitfalls And Where The Model Breaks Down
No framework is universal. The scoring matrix assumes stable platform APIs and predictable budget cycles. It breaks down during major Google Ads policy changes, like the 2023 shifts around privacy‑focused attribution. When measurement itself becomes unstable, the creative novelty score loses signal. I encountered this when a client’s swipe‑up rates dropped 30% across all variants after Apple’s ATT updates fully rolled out in the EU. The matrix was still functional, but the underlying metric was no longer comparable to the baseline. The workaround was to switch from swipe‑up rate to impression‑share weighted engagement—a less granular signal, but one that remained stable across platform changes. The model also struggles with niche verticals where audience sizes are inherently small. Fashion e‑commerce works well because the addressable market is large and competitive. Specialty B2B software doesn’t. The Jaccard overlap metric loses meaning when there are only 50,000 potential users in the entire target region. In those cases, I recommend augmenting the matrix with intent‑data proxies—job titles, firmographics, account‑based marketing signals—rather than relying on lookalike similarity alone. Finally, the framework assumes access to sufficient creative volume. If you’re launching one variant per month instead of five per week, the scoring matrix becomes a bottleneck rather than a filter. I’ve seen smaller teams try to apply it as‑is and end up pausing campaigns before they ever gathered enough data. The fix is to relax the novelty threshold for low‑volume accounts—use 1.2× instead of 1.4× the baseline—and supplement with manual creative reviews.
How To Start Applying The Framework Without Overhauling Your Stack
You don’t need a new platform or a data science team. The simplest entry point is the creative novelty score. It requires three data points: swipe‑up rate in the first six hours, your account’s rolling 30‑day average, and a simple binary flag (above or below threshold). Build this in a Google Sheets dashboard. It takes about 15 minutes to set up and another 10 minutes per campaign to populate. Next, run the bid elasticity test on one campaign at a time. Pick a variant that’s already performing close to target—don’t start with a broken campaign, you’ll get noisy data. Shift the daily cap by 15%, wait 48 hours, measure the delta in cost per result. If the elasticity is below 0.3, flag it for pause or creative refresh. If it’s above 0.7, consider scaling. Most campaigns fall somewhere in between. Those are the ones worth keeping. The audience overlap check is the easiest to automate. Both Google Ads and Meta Business Manager have built‑in overlap reports. Export them weekly. Look for pairs with 0.7+ overlap. Consolidate those into a single primary set. Reallocate the displaced budget to a new lookalike with sub‑0.5 overlap to the existing library. I usually see a 10–15% improvement in cost per acquisition within two weeks of making these adjustments.
The pacing rule is the hardest to enforce because it conflicts with the instinct to “give campaigns time.” But the data is clear: campaigns that don’t hit minimum spend thresholds in 72 hours rarely recover. I recommend setting a hard rule—pause anything below $500 spend in three days—and documenting the exception rate. In my experience, fewer than 5% of paused campaigns ever return to profitability. The rest are just sunk cost.
When To Look For An Alternative Approach
The framework isn’t a silver bullet. If you’re operating in a regulatory environment with strict privacy constraints—like healthcare or financial services in certain jurisdictions—the creative novelty score based on swipe‑up rate may not be available or compliant. In those cases, I recommend switching to first‑touch conversion rate as the primary creative metric, or using server‑side attribution where available. If your budget is under $10,000 per month, the elasticity test becomes statistically unreliable. A 15% cap shift on a $3,000 monthly budget is only $450—too small to measure meaningfully. For smaller accounts, I recommend aggregating the test across multiple campaigns or using historical data from similar launches instead of live experimentation. Finally, if your creative production capacity is limited to one or two variants per month, the scoring matrix will slow you down more than it helps. In those cases, focus on the audience overlap check and the pacing rule—those two components provide the most leverage with the least operational overhead. You can add the creative novelty score once you’ve ramped up production volume.
