PPC Agency in Auckland

Budget allocation across platforms should follow a demand waterfall. First, fully capture existing search demand on Google — these are your highest-intent prospects, and leaving search impression share on the table is the most expensive mistake in PPC. Once search is fully captured, expand to Meta and YouTube to create demand and fill your upper funnel with future buyers. For B2B companies, LinkedIn Ads are typically more expensive per click but generate higher-quality leads that convert into larger deal sizes, often justifying a significant budget allocation despite lower click volumes.

Audience synchronisation across platforms multiplies your PPC effectiveness. We upload your customer lists and website visitor audiences to every platform, creating consistent retargeting coverage across the web. A prospect who clicks your Google Search ad, visits your website, and then sees a tailored Meta retargeting ad featuring a case study relevant to their industry, then a YouTube pre-roll with a client testimonial, and finally a LinkedIn Sponsored Message with a free consultation offer — experiences a coordinated sequence that converts at dramatically higher rates than any single-channel approach.

Our approach matches bid strategy to account maturity and conversion volume. New campaigns and low-volume accounts start with Maximise Clicks (for data gathering) or manual CPC with enhanced bidding, transitioning to Smart Bidding only after accumulating 60-90 days of quality conversion data. Established accounts with strong data foundations typically run Target ROAS for e-commerce or Target CPA for lead generation, with regular strategy reviews to ensure algorithms are optimising toward the right goals. We also use portfolio bidding strategies that pool conversion data across related campaigns to accelerate machine learning where individual campaigns have insufficient volume.

Bid adjustments remain an important complement to Smart Bidding for controlling spend allocation by device, location, audience, and schedule. We analyse conversion rate data and implement bid modifiers that override algorithmic defaults where human pattern recognition identifies clear performance trends the algorithm has not yet captured. For example, a home renovation service that sees 60% of its weekend conversions come from Saturday morning searches would benefit from an aggressive weekend morning bid adjustment regardless of what the algorithm recommends.