From delayed attribution to profitable scale: How bottom-line bidding unlocked growth for NKON
A Google Ads structure capable of scaling profitably in a complex B2B buying environment.
About the brand
NKON is a European B2B retailer specializing in battery cells, energy storage components, and related products. With a broad product assortment and customers across multiple European markets, the business operates in a highly specialized market where purchase decisions often involve longer consideration periods and multiple stakeholders.
The challenge
NKON’s Google Ads account was heavily reliant on Performance Max, making it difficult to separate different types of demand and gain full control over where budget was being allocated.
But the bigger challenge was the B2B buying cycle. With customers often taking three to four weeks to complete a purchase, Google Ads attribution significantly lagged behind actual business performance. Revenue generated today could still be attributed to advertising clicks from several weeks earlier. On top of that, purchases could involve multiple devices, colleagues, or other touchpoints that Google Ads could not always connect to the original click.
This created a fundamental problem: optimizing against Google Ads-reported revenue meant making decisions based on an incomplete and delayed view of performance. The account needed a way to measure performance closer to the actual bottom line - and a structure that gave us more control over the different types of demand captured by Performance Max.
Our strategy
1. Bottom-line bidding
Instead of relying solely on the conversion data reported inside Google Ads, we shifted the focus towards actual business performance. By steering based on real-time bottom-line revenue, we could compare the revenue the business was actually generating against advertising spend much more directly.
This gave us a significantly more accurate and timely performance signal, allowing us to make budget and bidding decisions based on what was happening in the business now, rather than what Google Ads attributed several weeks ago. The result was a much tighter feedback loop between advertising spend and actual commercial performance.
2. Separating branded demand from Performance Max
Performance Max remained an important part of the account, but its broad inventory meant branded traffic could account for a significant share of spend. To create more control and transparency, we gradually started separating demand from PMax.
We introduced dedicated Branded Search campaigns, Branded Shopping campaigns, a non-branded Shopping fallback campaign and a non-branded Dynamic Search Ads campaign. This allowed us to isolate branded demand, capture non-branded traffic outside of PMax, and create clearer segmentation between different stages of the customer journey.
3. Combining control with profitable scale
The real impact came from combining the new campaign structure with bottom-line bidding. Rather than optimizing individual campaigns in isolation, we could look at the account from a business perspective: where is the next euro of advertising spend creating the most value?
The combination of better segmentation and a more accurate performance signal allowed us to gradually increase investment while maintaining control over profitability.
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Geert Adams
Founder, Ecubate
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