54% More Paid Revenue on Less Ad Spend for a B2B Industrial Hardware Retailer
Augmentiq PPC management added $135K in paid-attributed e-commerce revenue at record-high ROAS — while cutting total ad spend.
The Client
A specialty e-commerce retailer in the industrial RFID and Auto-ID hardware space. The catalog spans thousands of SKUs ranging from $5 accessory tags to multi-thousand-dollar enterprise reader systems, printers, and antennas — sold across a complex buyer mix that includes systems integrators, IT departments, healthcare facilities, race-timing operators, and OEM partners.
Sales cycles vary dramatically by product category. Accessories convert quickly with one-click purchase behavior, while readers, printers, and full-system orders frequently involve multiple touches, account-manager engagement, and quote-based purchasing. The catalog also covers a wide manufacturer ecosystem — including Zebra, Honeywell, Brother, Impinj, and Confidex — each with its own buyer profile and search behavior.
Industry
- B2B
- E-Commerce
- Industrial Hardware
Services
- PPC Management
- Measurement & Analytics
%
More Paid Revenue
Year-over-year, on 6% less ad spend
%
Higher Blended ROAS
Efficiency lifted from 1.49x to 2.44x
%
Lower Cost per Acquisition
Blended CPA fell from $606 to $393
The Challenge
The account had grown comfortable but inefficient. Performance Max campaigns were running with Final URL Expansion turned on, and Google’s machine learning was doing what algorithms do: chasing the path of least resistance. Budget was flowing toward fast-converting, low-AOV accessory traffic because that is where the conversion signal accumulates fastest — while the categories that actually move the business (readers, printers, full systems) were starving for budget.
On the Microsoft Ads side, spend was directed at maximizing clicks. Visibility was high, value was not. And Brand Search — historically the highest-ROAS layer of any e-commerce account — was being run with legacy structures that had not been refreshed since before AI Max bidding became broadly available.
The Strategy
Augmentiq rebuilt the account around two ideas: take back manual control where the algorithm was making the wrong product-mix call, and modernize the bid strategies where the algorithm could be redirected to optimize for real revenue.
- Overriding the Algorithm on Product Mix. Final URL Expansion was disabled across Performance Max, and page feeds were adopted to lock budget into specific product categories. This stopped Google’s machine learning from defaulting to quick-win accessory purchases — high transaction probability, acceptable ROAS, low absolute dollar contribution — and forced the algorithm to spend on longer-cycle, higher-AOV categories where signal accumulates slowly but where a single sale eclipses a dozen accessory orders. A human-driven insight the platform could not surface on its own.
- High-AOV Campaign Segmentation. The Readers category was split into dedicated High-AOV and Low-AOV Performance Max campaigns, structurally isolating higher-margin SKUs from commodity traffic. A parallel build-out of manufacturer-specific campaigns (Honeywell, Zebra, Brother, Impinj, and others) added a second layer of segmentation, enabling budget allocation by manufacturer ecosystem rather than product type alone.
- Modernizing Brand Search with AI Max. Two new Brand Search campaigns were launched on AI Max with target-ROAS bidding, replacing legacy keyword structures. These two campaigns alone delivered ROAS well above 50x while consuming a fraction of total spend, freeing significant budget for prospecting investment elsewhere in the account.
- Rebuilding Microsoft Ads Around Value. The Microsoft Ads strategy shifted from Max Clicks to target-ROAS bidding. Visibility was deliberately reduced — impressions fell 63% by design — in service of value. A funneling structure was built around manufacturer-brand queries (Zebra, BarTender, Impinj) to scale the highest-intent traffic, and overall Bing budget was pulled back to redirect spend toward Google where the per-dollar return was demonstrably higher.
The Results
All revenue and transaction figures below are sourced from GA4 last-non-direct-click attribution rather than from platform self-reporting, which carries year-over-year configuration noise. ROAS is calculated using platform-reported cost divided by GA4-attributed revenue — the strictest defensible measurement available.
- 54% More Paid-Attributed Revenue on 6% Less Spend. Combined Google and Microsoft Ads spend was reduced from $167,328 to $157,706 (−5.8%), while GA4-attributed revenue from paid sources grew from $248,833 to $384,261 (+54.4%). Paid media’s share of total site revenue jumped from 26.3% to 36.9% — the channel now drives more than a third of all e-commerce revenue.
- Blended ROAS Lifted from 1.49x to 2.44x. A 64% improvement in dollars earned per dollar spent. Blended cost per acquisition dropped 35% — from $606 to $393 — and 401 paid-attributed transactions were recorded against the prior year’s 276, a 45% increase. Average order value rose to $958, up from $902.
- Microsoft Ads Cut Spend by a Third, Transactions Up 42%. The shift from visibility to value bidding produced the cleanest single-platform story of the quarter. Microsoft spend fell from $20,698 to $14,010 (−32%), and the platform delivered 51 transactions versus the prior year’s 36 (+42%). Microsoft cost per acquisition fell from $575 to $275 — a 52% reduction — and Microsoft ROAS climbed from 1.41x to 2.16x.
Key Performance Indicators – Q1 2026 vs. Q1 2025
| Metric | Q1 2025 | Q1 2026 | Change |
|---|---|---|---|
| Paid media spend | $167,328 | $157,706 | ▼ −5.8% |
| Paid-attributed revenue (GA4) | $248,833 | $384,261 | ▲ +54.4% |
| Paid transactions (GA4) | 276 | 401 | ▲ +45.3% |
| Blended ROAS | 1.49x | 2.44x | ▲ +63.8% |
| Blended cost per acquisition | $606 | $393 | ▼ −35.1% |
| Average order value | $902 | $958 | ▲ +6.3% |
| Paid share of site revenue | 26.3% | 36.9% | ▲ +10.6 pts |
Methodology
Spend figures are sourced from Google Ads and Microsoft Ads platform reports. Revenue and transaction figures are sourced from GA4 (Traffic Acquisition by session source/medium). ROAS is calculated as GA4-attributed revenue divided by platform cost. Platform-reported conversion counts are intentionally excluded to eliminate noise from year-over-year changes in conversion-action configuration. Comparison window: January 1 – March 31, 2026 versus January 1 – March 31, 2025.