Targeting advertising with AI

Targeted advertising is a clear beneficiary of AI’s proliferation. While the discipline has evolved significantly over the past 15 years, the pace of advancement in the last five years is fundamentally changing how campaigns are planned, delivered and measured. 

 

Historically, advertisers have relied on expertise, testing and iteration to improve performance over time. AI is accelerating and, in some cases, bypassing these processes, enabling more precise and data-driven decision making from the outset. Despite this shift, many brands still lean on legacy tactics such as search engine optimisation, third-party cookies and one-size-fits-all messaging that prioritise scale over relevance. That approach is becoming less effective as consumer behaviour evolves.

 

AI is opening up a different model for advertisers, centred on three key shifts: moving from keywords to intent-led targeting, turning performance into a more predictable outcome, and delivering more precise, adaptable personalisation. Together, these changes are redefining how brands compete and where advantage is found.

 

 

From keywords to intent-led targeting

Consumer search behaviour has fundamentally shifted. People are no longer relying on short, keyword-led queries. They are asking conversational questions and expecting AI to interpret context, sentiment and intent. This change exposes a growing gap in traditional keyword targeting. Matching ads to what people type is not as effective when discovery is driven by what people mean.

 

At the same time, search itself is fragmenting. AI chatbots, social search and recommendation engines are playing a larger role in how people find information. Discovery is becoming more fluid and less tied to traditional search engine results. In this environment, intent matters more than keywords, with AI-driven searches already showing significantly higher conversion rates than traditional search. AI allows advertisers to respond to this shift.

 

Rather than building campaigns around static keyword lists, brands can now identify and group audiences based on shared intent, using signals such as behaviour, context and sentiment. This enables messaging to align more closely with what users actually want, not just what they type. And the result is a more effective and differentiated approach. 

 

 

Turning ad performance into a predictable outcome

For years, improving campaign performance has depended on testing, learning and gradual refinement. Results often became clearer over time, rather than being predictable from the outset. ML is now compressing that process. Campaigns can now be more easily adjusted as they run, prioritising the placements and formats most likely to drive engagement without waiting for multiple rounds of optimisation.

 

Recent tools from Nextdoor reflect this shift. Its AI-powered click optimisation uses machine learning to enable campaigns to adjust in real time, prioritising placements and formats that are most likely to drive engagement. In testing, advertisers saw a median increase of more than 75% in click-through rate, alongside a similar reduction in cost per click. This aligns with wider industry data, with research from Schnoco showing that AI-driven advertising is delivering higher engagement and stronger returns, and insights from The Drum highlighting how advertisers increasingly expect automation to deliver both efficiency and predictability.

 

As a result, brands are now able to rethink their strategies.  Rather than spreading spend across multiple experiments, there is growing value in prioritising platforms and tools that offer real-time optimisation and outcome-based buying. By leaning into click-optimised models and automated performance systems, stronger and more reliable performance is becoming achievable without increasing spend.

 

 

Precision and personalisation without the guesswork

Personalisation has long been a priority for advertisers, but it has often relied on broad assumptions about who audiences are and what they might respond to. AI is changing that by enabling campaigns to be shaped by real-time signals, allowing brands to respond to how people are behaving and what they are looking for in the moment, rather than relying on fixed audience profiles.

 

This creates a more precise and adaptable approach to advertising. Messaging can be refined based on context, location or recent interactions, making campaigns feel more relevant and timelier, rather than generic. At the same time, creative and targeting can evolve as campaigns run, enabling advertisers to stay aligned with shifting behaviours without needing to restart or rebuild campaigns from scratch.

 

In practice, this changes how campaigns are built and managed. Instead of locking in creative and targeting decisions upfront, advertisers can treat campaigns as responsive systems that adjust as new signals emerge, improving relevance and performance as they run.

 

 

Working from guesswork to growth

While such rapid change in the advertising landscape can appear worrying for businesses, it ultimately removes much of the uncertainty that has historically defined advertising investment and creates new opportunities. The shift towards intent-led targeting, predictable performance and precise personalisation can allow businesses to align more closely with how consumers actually behave.

 

Advertisers that embrace this shift will be better positioned to cut through increasing noise, delivering campaigns that are both more efficient and impactful.    

 

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Gareth Walton is Head of EMEA Sales at Nextdoor

 

Main image courtesy of iStockPhoto.com and Parradee Kietsirikul