Menu
Bidding is one of the most technically complex — and commercially significant — elements of a paid advertising campaign. The wrong approach means paying too much for clicks that don't convert, or losing out on the impressions that would have. The right strategy does the opposite.
Bid strategy optimisation reviews how your budgets are being allocated and how your bids are being set, then applies the approach most likely to deliver your goals efficiently. Whether that means shifting to a target ROAS, adjusting by device, or restructuring how budget flows across campaigns — every change is grounded in data and focused on improving return.
Bid strategy optimisation is the process of reviewing and adjusting how bids are set across a paid advertising campaign to improve efficiency and return on investment. It involves selecting the right bidding approach for each campaign objective, analysing the relationship between bid levels and performance, and making targeted adjustments to ensure budget is being allocated to the placements and audiences most likely to convert.
You need this when you sell products online and want to appear directly in Google Shopping results with images and pricing visible, when your shopping campaigns exist but aren’t generating strong return on ad spend, or when competitors are consistently appearing in shopping placements that you’re not visible in. Shopping campaign management ensures your product feed, bids and creative are all optimised for maximum performance.
This service includes Google Shopping feed audit or setup, campaign structure and product group organisation, bidding strategy configuration, and ongoing optimisation of bids, negatives and product data. Includes regular reporting on ROAS, revenue and product performance. Delivered as a fully managed Shopping campaign service.
Most marketing companies focus on channels and tactics.
We focus on reaction.
Before selecting platforms, formats, or media spend, we define how your audience thinks, feels, and decides. We use behavioural psychology to understand what will capture attention, build trust, and motivate action — then choose the channels that best support that outcome.
Every channel we use has a clear purpose, a defined role, and a measurable objective. Nothing is done “because it’s popular” or “because it’s expected”.
The result is marketing that feels natural to engage with, works across multiple channels, and is designed to deliver meaningful, long-term results.
Want to see how this approach works in practice?
The process of selecting, configuring and adjusting the automated or manual bidding approach that determines how much you pay for each click or impression — and ensuring it’s working as effectively as possible toward your commercial conversion objectives.
Manual CPC, Maximise Clicks, Target Impression Share, Target CPA (cost-per-acquisition), Target ROAS (return on ad spend), Maximise Conversions and Maximise Conversion Value are the main strategies available, each suited to different campaign objectives and data maturities.
Manual bidding gives maximum control and is often preferable in the early stages of a new campaign when conversion data is insufficient for automated strategies to perform reliably. Automated strategies become more effective as conversion volume grows and the algorithm accumulates the data it needs.
Target CPA asks Google to optimise bids to achieve a specific cost-per-acquisition. It works best when the account has at least 30 to 50 conversions in the preceding 30 days. With insufficient data, the algorithm has too little signal to optimise effectively.
Target ROAS optimises bids toward a specific return on ad spend, taking into account the value of each conversion rather than just its occurrence. It works well for e-commerce where conversions have different values and maximising revenue, not just conversion volume, is the goal.
By starting with a target based on historical performance data and the commercial value of a conversion, then adjusting as the strategy accumulates data. Setting an unrealistically ambitious target initially forces the algorithm to restrict delivery excessively; a gradual approach is more effective.
Bid adjustments modify base bids by a percentage for specific devices, locations, audiences, ad schedules or demographics. They’re used to increase bids for higher-converting contexts and decrease them for lower-converting ones — improving efficiency without changing the campaign’s fundamental structure.
Partially. Some automated strategies allow device, location and audience bid adjustments. Others override manual adjustments entirely. Understanding which adjustments are compatible with your chosen strategy is important for avoiding unintended conflicts.
By comparing performance before and after the strategy change, ensuring sufficient time has elapsed for the algorithm to exit its learning phase (typically two to four weeks), and assessing results against the specific objective the strategy was set to achieve.
The period after a new automated bidding strategy is applied during which Google’s algorithm is collecting data and adjusting its model. Performance during the learning phase is typically less stable and less efficient than after it completes. Major changes reset the learning phase and should be made deliberately.