The marketing ecosystem is on the brink of a seismic shift. We’re swimming in a sea of data, yet access is becoming increasingly restricted.
The old ways of reaching audiences – cookies, third-party identifiers – are crumbling before our eyes. We can’t avoid the inconvenient truth that due to global and local macro forces impacting media and digital, almost all of the open internet will soon become untrackable.
We need to change the way we look at data and tech to thrive in the next era of media, and the answer lies in embracing the transformative power of predictive technology.
I think predictive technology will have a major impact in three key areas: predictive planning – where it will help brands move beyond the limitations of 1:1 targeting; predictive optimisation – where AI will unlock campaign success and predictive content – where dynamic, personalised experiences will transform connection with audiences.
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Predictive planning: Moving beyond the limitations of 1:1 targeting
For years, marketers have chased the holy grail of 1:1 precision targeting. But with growing privacy concerns and walled gardens casting long shadows, this approach is proving unsustainable.
Instead of fixating on individual data points, we need to widen our data aperture and move past siloed audience planning to unified cross-channel, cross-platform planning.
The only way we achieve unified audience planning is by creating a new common currency for identity, embracing the potential of hyper-local data for enhanced targeting. Hyper-local data hits the sweet spot of scale and accuracy.
Think of it as a tapestry woven from location data, demographics, psychographics, and even purchase behaviour. By analysing these threads together, we gain a nuanced understanding of consumer behaviour within specific geographic areas.
This allows us to reach entire communities primed to receive the message. Imagine, for instance, a campaign for healthy burgers that targets neighbourhoods with a high concentration of fast-food restaurants and health food stores. Now, what if we can further enrich these areas with the highest transacting customer cohorts for health equipment purchases, gym membership subscriptions and purchases at fast food restaurants? That’s the power of hyper-local data in action.
Predictive optimisation: Unleashing the power of AI for campaign success
The sheer volume of data available today is overwhelming, even for the most seasoned marketer. We also know that within the current environment, brands need to do more with less. That’s where AI comes in, acting as a tireless assistant capable of uncovering insights and optimising at pace.
AI in media isn’t new – but it is accelerating. Already around 69% of all advertising is touched by AI in some way, andGroupM has forecast that it will reach 94% globally by 2029.
AI-powered predictive optimisation can help deliver the performance optimisation brands are looking for by analysing vast datasets to identify patterns and trends, allowing marketers to fine-tune campaigns in real time. Programmatic enables access to a wealth of data points and endless optimisation possibilities.
We can enable mid-flight optimisation, and automatically adjust bidding strategies based on real-time signals (weather patterns, pollen count, sporting results), on-site behaviour, and product availability – imagine an airline only optimising to available flight routes based on real-time seat availability.
Providing algorithms tailored to custom objectives with an automated model prioritisation approach to optimise towards high-margin products can far exceed a standard CPA (cost per action) model, which is a blunt instrument for brands.
Where AI will truly add value in the future is by helping us optimise campaign spending cross-channel with consistent audience approaches across DOOH, addressable TV, digital, and audio – ultimately delivering enhanced targeting efficiency and return on ad spend (ROAS). This level of granular control empowers marketers to maximise ROI and ensure every dollar spent delivers tangible results.
The balance to achieve is empowering our teams with the tools to ensure we get the benefits of human ingenuity powered by machine learning to deliver maximum performance optimisation so media investment can go further. Predictive optimisation can also uncover granular insights that can inform a predictive content strategy.
Ryan Menezes
Predictive content: From static ads to dynamic, personalised experiences
In a world saturated with content, capturing and holding the audience’s attention is more challenging than ever. The key lies in delivering personalised experiences that resonate on an individual level – at scale. That’s where predictive content comes into play.
By leveraging AI and machine learning, we can analyse consumer data to predict the type of content most likely to engage specific audiences. This could involve tailoring ad copy to reflect local slang or dynamically generating video ads featuring products relevant to a user’s browsing history or purchase propensity. This shift from static ads to dynamic, personalised stories allows brands to forge deeper connections and deliver truly memorable experiences.
These three pillars of predictive technology will be transformational for brands in the near future. By embracing hyper-local data, AI-driven optimisation, and dynamic content creation, we can navigate the evolving media landscape and forge meaningful connections with audiences.
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