Due North
“It's incredible… we're looking at up to 30% reduction in wasted click spend for our customers.”
Ben Hirons, Founder & CEO, Melbourne
Find the 20-30% of your paid media budget funding search terms and creatives that will never convert.
What changes once this is in
Up to 30% reduction in wasted click spend
Validated at Due North, a Melbourne performance-marketing agency running the system across client Google Ads accounts. The wasted spend comes mainly from irrelevant search terms and negative-keyword gaps the platform never flags.
ROAS decay caught in days, not months
Campaigns drifting down in return on ad spend are flagged inside the first week of decay. The intervention happens before the quarter's budget is burnt, not after the board meeting.
Your marketing manager stops firefighting
Instead of scanning six dashboards every morning, they open one queue. The system tells them what changed, why it matters, and what to do. Analyst-grade analysis without an analyst.
Agency conversations move from invoices to outcomes
For agencies, monthly client reviews shift from defending fees to showing saved spend and recovered margin. Client churn drops. Retainer conversations get easier.
Marketing Spend Optimisation is an AI layer over your Google Ads, Meta, and LinkedIn accounts that watches every campaign daily, flags the queries, keywords, and creatives burning budget, and routes the specific intervention to your marketing manager before the monthly report lands. It is built for mid-market brands and the agencies that run them, not for enterprise ad-ops teams.
What usually breaks
You are paying for clicks that will never convert. Every week. The average paid-search account wastes 20-30% of its budget on search terms the buyer never intended, negative-keyword gaps nobody has audited, and ad groups quietly drifting on ROAS. Nobody inside the business sees it until the month-end report, by which point the money is gone.
The data to catch this already exists. It is sitting in Google Ads, Meta Ads Manager, GA4, and your Shopify or HubSpot back-end. What's missing is the thing that reads all of it together, daily, and tells your marketing manager what to do next. Agency reports describe the past. Platform recommendations optimise for platform revenue. Neither closes the loop back to profit.
That is the gap this solution fills. One system, reading everything, acting weekly.
How this lands inside your operation
Your marketing manager already opens Google Ads every morning. They are competent. They are not a data scientist, and they should not need to be. This solution runs quietly in the background across your ad platforms, CRM, and commerce stack. Each morning it surfaces a short list: the search terms to negate, the ad groups with ROAS decay, the creatives showing fatigue, and the CPA drifts worth a closer look. One brief per morning, in Slack or email, with the recommended action next to each item.
For agency owners managing $50k to $500k a month of client spend, the same system runs per client account. It turns the end-of-month conversation from invoice defence into outcome reporting.
This is for you if…
- Mid-market eCommerce and DTC brands running $50k-$500k a month of paid media with an in-house marketing manager and no data team
- Digital marketing agencies managing multiple client accounts who want to turn reporting into outcomes
- Multi-site retailers and franchise networks running paid media at head office for branches that never see the data
- Tourism, hospitality, and education groups with seasonal paid-media budgets they cannot afford to waste
- Marketing directors tired of monthly retros where the money is already spent
This probably isn't the right fit if…
- Brands spending under $20k a month on paid media. The system earns out at scale.
- Organisations looking for a pure reporting tool. This is built to drive weekly action, not weekly slides.
- Teams with a mature in-house data science function already running custom media-mix models.
What we monitor and act on
Below is the live list of signals the system watches across a paid-media account, and the action it recommends when each one fires. Every recommendation lands with your marketing manager, not the algorithm. No auto-apply.
| Signal | What the system sees | Recommended action |
|---|---|---|
| Wasted click spend by query | Search terms driving clicks but zero conversions over a statistically meaningful window | Add as negative keywords at campaign or account level. Dollar saving quantified. |
| CPA drift | Cost per acquisition trending up against the 30-day baseline for an ad group | Investigate bid strategy, audience overlap, or creative fatigue. Bid adjustment suggested. |
| Negative-keyword gaps | High-impression queries that are clearly off-intent but not yet blocked | Bulk-add negatives, grouped by theme, with impression and cost context. |
| ROAS decay by ad group | Return on ad spend falling week over week against the rolling baseline | Pause, rework creative, or reallocate budget to the ad group outperforming. |
| Creative fatigue | CTR and conversion rate decaying on a specific ad while impressions hold | Rotate creative. Brief the designer with the exact ad, the decay window, and the audience. |
| Audience saturation | Frequency rising on Meta while incremental conversions flatten | Refresh audience or shift budget to prospecting. |
| Landing-page mismatch | High click-through but weak conversion on a specific URL pairing | Flag to the CRO team. Suggest A/B test or redirect. |
| Budget pacing anomalies | Spend running hot or cold against plan by channel or campaign | Rebalance daily caps. Alert if monthly burn will overshoot before month-end. |
The engagement, step by step
- 1
We connect to your Google Ads, Meta, LinkedIn, GA4, and commerce or CRM data through the platform APIs.
- 2
The system builds a baseline of what "good" looks like per campaign, ad group, and audience in your account.
- 3
Daily, it scores every search term, keyword, and creative against that baseline for waste, drift, and fatigue.
- 4
Anomalies are surfaced to your marketing manager as a prioritised action queue, not a dashboard.
- 5
Each recommendation carries the expected dollar impact and the exact change to make in-platform.
- 6
Accepted and rejected actions feed back into the model, so next week's recommendations get sharper.
- 7
Monthly, the system produces a plain-English outcome report for the board or the client.
The capabilities behind this
AI Development
Production-ready AI agents, chatbots, and multi-agent systems, built senior-led end-to-end.
Learn moreAdvanced Analytics & ML
Predictive models, recommendation systems, forecasting, and MLOps, delivered with a ruthless ROI focus.
Learn moreInsight-to-Action Systems
Close the gap between what your data knows and what your business does. The architecture from insight through decision to action.
Learn moreWhat this looks like in the field
Due North
Digital Marketing
“It's incredible… we're looking at up to 30% reduction in wasted click spend for our customers.”
- AI monitoring of Google Ad campaigns and keywords, surfacing insights directly to ad managers
- Automatic identification of up to 30% of wasted ad spend on irrelevant search terms
- Early detection of ads trending down in profitability — intervention before the loss lands
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Let's talk about Marketing Spend Optimisation.
30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.
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