3. Spread your spend
Once target parameters have been set, advertisers can make their dollars go further by carefully spreading ad spend out across different platforms and formats. Once again, it’s important not to fall into the trap of being too generic and spend distribution should never come at the expense of robust targeting. You still need to be clear about who you want to reach and where you want to reach them. But since customers rarely stick to just one platform, cross-platform campaigns are a must.
The best way to approach spend allocation is to identify the platforms where you have the most organic reach then divert some spending towards repurposing top-performing content into different formats. That could be paid ads, social media ads, video ads, audio ads, or even in-game advertising. Consider new and emerging formats: if you know your audience spends time streaming content, shopping online, or consuming media across multiple devices, expanding into Connected TV (CTV), retail media, and programmatic campaigns can significantly extend your reach.
CTV allows brands to reach highly engaged audiences in premium, full-screen environments, while retail media enables advertisers to influence consumers closer to the point of purchase using first-party data from retailers. Programmatic ties it all together, allowing for real-time optimization and consistent targeting across channels at scale. When used as part of a unified strategy, these formats help brands more accurately follow their audiences and reduce wasted ad impressions as a result.
“2026 will be the year CTV becomes a full-funnel medium,” commented Max Deyerl, Manager and Product Innovator at Virtual Minds to IAB Europe. “As retail media networks integrate with CTV inventory, as shoppable formats mature, and as AI improves household-level attribution, CTV will prove its ability to drive incremental conversions, not just incremental reach.”
4. Automate
Research shows that automation can decrease cost per lead by nearly 20% and cost per click by 9%. Automated advertising platforms like Demand Side Platforms (DSPs), ad attribution products, and anomaly detection services are equipped with features to help advertisers optimize ad spend, track ad value metrics, and flag up anomalies in campaign data. Traditionally, these automation tools have focused on rule-based optimizations such as pausing underperforming ads, reallocating budget based on predefined thresholds, or scheduling campaigns based on historical performance.
Similarly, dedicated ad management tools measure campaign success and use measurement and attribution to identify the most lucrative growth opportunities. This enables teams to scale what works and eliminate inefficiencies faster than manual processes.
Today, modern AI-driven optimization is taking performance optimization a step further by continuously adapting. The Trade Desk, for example, uses advanced AI and machine learning to score impressions, predict outcome likelihood, and optimize bids in real time. Instead of relying on static rules, AI models analyze vast amounts of historical and live data to make predictive adjustments, automatically increasing or decreasing bids based on the likelihood of conversion or customer value. This helps prevent wasting spend on impressions or clicks that are unlikely to perform.
Similarly, AI-powered anomaly detection goes beyond simple threshold alerts by identifying subtle performance shifts before they materially impact spend. And at the creative level, AI is capable of dynamically testing and optimizing messaging, formats, and visuals to identify winning combinations faster than manual A/B testing or generic automation workflows.
Combined, these capabilities reduce wasted ad spend more effectively than traditional automation tools by continuously optimizing outcomes. In fact, research suggests that AI driven audience targeting increases ad performance by 30% while reducing costs by 25%.
5. Privacy-first tactics
Privacy-first advertising strategies reduce wasted ad spend by improving the quality of the data used to make buying decisions. As third-party cookies and cross-site tracking disappear, identity-based targeting increasingly relies on incomplete or degraded signals, leading to duplicated reach and misattribution. Privacy-first approaches shift optimization toward first-party, consented data, which is inherently more accurate and stable. This allows advertisers to allocate budget with greater confidence and avoid spending on impressions that can’t be properly measured against outcomes.
In addition, privacy-first optimization encourages a move away from user-level surveillance and toward aggregate performance signals and outcome-based learning, which AI systems can use to optimize more effectively at scale. For example, an ad platform can optimize bids toward high-performing cohorts based on conversion patterns without needing individual user histories.
Instead of chasing individuals, AI models built around privacy-first targeting analyze patterns relating to conversion likelihood, engagement, and contextual performance to predict where spend will be most efficient. This not only protects user privacy but also cuts wasted spend caused by broken targeting pools. The result is advertising that performs better over time and directs budget toward impressions that are more likely to drive results.
Testament to the success of privacy-first targeting, major brands and publications are embracing these methods. In September 2025, it was reported that The Guardian would be increasing its online advertising services through a privacy-first model in partnership with identity provider, ID5.
“This partnership reflects the evolution of digital identity toward a sustainable, privacy-first model that empowers publishers to take control of their audience relationships and future-proof their monetisation strategies,” said Morwenna Beales, Vice President of publisher Development at ID5.
6. Optimize infrastructure spend
In their search to improve return on ad spend, adtech companies should also consider where cost savings can be made around one of their biggest expenses - infrastructure. Many are led to believe that the most cost-effective infrastructure solutions lie with hyperscale cloud providers. And it makes sense. Hyperscale cloud comes with minimal barriers to entry, pay-per-usage plans, and quick provisioning. But often these aren’t the solutions offering the best balance between network quality and affordability.
The reality of pay-per-use models like these is that, more often than not, you end up paying a premium for what you need. And as your resource demand increases, it’s easy to start haemorrhaging money. Instead of achieving flexibility, companies find themselves locked-in to services that are financially unsustainable.
Nobody wants to change infrastructure provider. But by shopping around to see what else is out there, including options for reducing spend on baseline compute needs with dedicated hosting, you can save serious money. Infrastructure-as-a-service from bespoke hosting providers comes with predictable costs, increased transparency, and far better levels of technical support. Through a full or partial migration to bespoke adtech server hosting, companies can find a better balance between spend and performance.
Waste not want not
When the economy shows signs of instability, ad budgets are usually the first thing to go. But slashing budgets isn’t necessarily the solve-all option it seems and often businesses find that the decision causes more problems than it solves long-term. From making use of automation and contextualized advertising, to investigating the new world of AI-driven automation, there are tools at your disposal to help you spend more efficiently and ensure return on ad spend.
