No fake testimonials, no inflated screenshots. Four real client accounts across four niches: click any card to see the full breakdown, phase by phase.
When this Germany-based supplement brand came to us, the account was under real performance pressure. The product had genuine market potential, but the business was stuck in a cycle of high ad spend with low returns.
We rebuilt product listings from the ground up with a structured SEO approach, placing keywords by priority, relevance, and search intent. We used AI-driven Rufus question analysis so listings answered the questions customers were actually asking: improving both organic discoverability and buyer confidence.
With clean listings in place, we overhauled the advertising architecture: eliminated duplicate keyword targeting across campaigns, removed wasted spend from irrelevant search terms, and implemented a relevance-based structure segmented by match type and intent.
In the first two weeks, we launched a focused exact-match campaign with elevated top-of-search placement to capture premium ad real estate and accelerate keyword ranking: triggering early sales velocity, a key signal for Amazon's A9 algorithm. We then expanded into broad match for the same high performers at lower bids, with proactive negative keyword lists to prevent cannibalization.
Once the account was stable and consistently profitable, we moved into active scaling: launched sponsored brand campaigns for top-of-search brand awareness, added sponsored display for new customer acquisition and retargeting, and expanded keyword coverage into adjacent search queries.
| Metric | Before (Sep 2025) | After (current) |
|---|---|---|
| ACoS | High, unprofitable | 12-15% avg, declining |
| Monthly sales velocity | Low & inconsistent | €62,747 peak (Mar 2026) |
| Ad spend efficiency | Wasted spend, cannibalization | Clean structure, zero duplication |
| Organic rankings | Poor, weak listing SEO | Top-of-search positioning |
| Profitability | Unprofitable | €62,350+ net profit |
| Page views | Low traffic | 22,271 views in best month |
We fixed the listing before scaling ads: making sure every click had the best possible chance of converting.
Rebuilding the PPC architecture eliminated wasted budget and created a clear data feedback loop.
We prioritized early ranking gains before expanding into broad match and upper-funnel formats.
This brand competed in one of Amazon's most punishing environments: a niche dominated by rivals with thousands of reviews, aggressive pricing, and established organic rank. Our client's products were genuinely higher quality, sold at a premium price, but unknown to the algorithm. The brand had pulled back on ad spend, afraid that more PPC would just mean worse ACoS with no improvement in rank or revenue.
Before touching a single bid, we audited the account structure and keyword landscape to find which keywords had the highest purchase intent for a premium buyer: not just high search volume. The distinction mattered: the wrong traffic at a premium price converts at 1:2%. The right traffic converts at 8:12%.
We rebuilt the campaign architecture around one principle: target keywords where buyers are already predisposed to purchase premium. We prioritized exact match on high-CVR relevant terms, avoided generic terms that attract price-sensitive shoppers, and used search term reports aggressively to find hidden high-CVR terms and promote them to dedicated campaigns.
By concentrating spend on high-converting, highly relevant keywords with elevated top-of-search placement, we generated the sales velocity Amazon's algorithm needed to improve organic rank: without scattering budget across low-converting terms. As rankings improved, cost per sale naturally decreased, creating a compounding effect: better rankings, more organic sales, lower TACoS, more room to reinvest.
With core rankings established and both ACoS and TACoS stable, we expanded into adjacent keyword clusters with proven CVR patterns, launched broad match with aggressive negative keyword management, introduced sponsored brand campaigns to own top-of-search real estate, and used sponsored display for retargeting.
| Metric | Before | After |
|---|---|---|
| Total sales | Stagnant, low & inconsistent | €1.67M total ordered sales |
| PPC sales | Minimal, underinvested | €840K PPC-attributed sales |
| ACoS | Fear-driven underspend, no data | 12-15%, well within target |
| TACoS | Not tracked, no strategy | 6-9%, highly efficient |
| Keyword rankings | Poor across all targets | Top-of-search on core terms |
| Market position | Invisible vs. low-priced competitors | Premium brand authority established |
At 6-9% TACoS, every Euro of ad spend generated nearly €11.7 in total revenue. The account's strongest month on record came in March 2026, confirming the strategy was compounding with scale rather than fading.
A higher price point isn't a weakness: it's a signal of quality. The right keywords bring the right buyers, and those buyers convert.
We didn't optimize for lowest ACoS. We optimized for highest-converting keywords first: which naturally kept ACoS in check as rankings improved.
Once organic rankings strengthen, paid dependency drops. PPC was always meant to be a ranking engine, not a perpetual cost center.
This baby-gear brand sells into one of Amazon's most emotionally driven, trust-sensitive categories: parents researching heavily before they buy, and reviews carrying outsized weight. The account had decent volume but inconsistent profitability: ACoS swung from the low 40%s to the low 60%s month over month with no clear pattern, and the team couldn't tell which campaigns were actually driving the swings.
We mapped historical sales data against seasonal demand patterns specific to baby gear: registry season, holiday gifting spikes, and back-to-routine periods: to understand where the ACoS volatility was actually coming from.
We rebuilt targeting around high-intent, trust-driven search terms: the specific phrases parents use right before purchase, not generic category terms. This cut spend on early-research browsers who weren't ready to buy yet.
Instead of flat year-round bids, we built a bid calendar that flexed with the category's demand curve: pulling back in slow months to protect margin, and pushing aggressively in the weeks that matter most for baby gear purchases.
Once ACoS volatility was under control, we concentrated additional budget on the ASINs with the strongest review counts and ratings: the products parents trust fastest: to compound the gains where conversion resistance was lowest.
| Metric | Before | After |
|---|---|---|
| ACoS | Volatile, up to 62.7% in peak spend months | Stabilized to 12-15% in later months |
| ROAS | Inconsistent, unclear drivers | 2.49× average, predictable |
| Bid strategy | Flat year-round | Seasonal demand-curve calendar |
| Keyword targeting | Generic, high-volume terms | Trust-driven, high-intent terms |
| Total purchases | Inconsistent month to month | 2,155 tracked purchases |
Treating seasonal demand as a signal: not random variance: let us plan bids ahead of spikes instead of reacting to them.
In categories where buyers research heavily, fewer high-intent clicks beat more low-intent ones every time.
Concentrating budget on already-trusted ASINs compounded faster than spreading spend evenly across the catalog.
This home and kitchen brand had a genuinely strong product: solid reviews, a clear point of differentiation: but margin was getting crushed by ACoS spikes that hit as high as 89.4% in some months. The category is brutally price-comparison driven, with shoppers opening five tabs before buying, and the existing campaigns weren't built to compete on relevance instead of just raw bid amount.
We identified the specific product features and use-cases that set this listing apart from generic kitchenware, then built keyword targeting around those differentiators instead of category terms competing purely on price.
The 89.4% ACoS month told us exactly where to look. We pulled the search term reports, identified every irrelevant or low-converting query draining spend, and built a comprehensive negative keyword list to stop the bleeding immediately.
We rebuilt the bid calendar around the category's real seasonal patterns: pushing budget into the gifting and New Year cooking-resolution windows where conversion intent is naturally higher, and pulling back during structurally slower months.
With ACoS stabilized well below the 89.4% peak, we scaled spend carefully on the keyword segments proven to convert at a profitable rate: protecting margin instead of chasing volume for its own sake.
| Metric | Before | After |
|---|---|---|
| ACoS | Spiked to 89.4% in worst month | Stabilized to lifetime average of 12-15% |
| Keyword strategy | Generic, price-comparison terms | Differentiation-led targeting |
| Wasted spend | Significant, untracked | Cleaned via aggressive negative lists |
| Bid strategy | Flat, no seasonal logic | Aligned to gifting & New Year demand |
| Total purchases | Inconsistent, margin-negative | 617 tracked, margin-protected |
The 89.4% ACoS spike wasn't a failure to hide: it was the clearest signal of exactly where the wasted spend lived.
In a price-comparison-heavy category, generic targeting forces you to compete on bid alone. Differentiated keywords let the product's actual strengths do the selling.
Aligning bids to real seasonal demand meant spend worked harder exactly when buyers were already primed to purchase.
We'll audit your ad account for free and show you exactly where the wasted spend is going: and where it should go instead.