AI and personalisation in digital marketing: what does that look like in 2025 and beyond. This question is at the very top of the marketing agenda.

It is a question we will explore in our next webinar on March 26, sharing our knowledge on this omnipresent topic with insights from our expert panellists.

It’s indisputable that personalisation is now pervasive in marketing, as outlined in the Customer Engagement Future Trends 2025 report from the Data and Marketing Association (DMA). Marketing leaders face questions on how to deliver personalisation at scale with sustainably, where to use it in your marketing and the impending challenge Gen AI presents. 

There is an absolute need for brands to handle data sensitively in personalisation strategies today to avoid putting-off consumers and coming across creepy. Brands need to act with transparency as audiences become more aware of the use of AI and their data in marketing. Personalisation fundamentally falls into six uses in marketing, we’re here to look at two areas and the brands who are getting the balance right.

Making people feel seen, not stalked

Isn’t it great when you feel seen, like someone has truly been paying attention and just gets you? Personalisation can be a pleasant exchange. Utilising first-party data, brands can leverage insights from real-time habits and preferences to create a tailored user experience. Done right, it feels like a helpful suggestion. Done wrong, intrusive or incessant.

Netflix gets it right. Using their AI algorithms to analyse user viewing history like what is watched, for how long, when, how often it’s paused or rewound. This data is used to predict content preferences and even show personalised imagery to entice users to click on specific programs. This AI-personalised strategy has reportedly saved the company approximately $1 billion annually.

Across ecommerce, customer data is being fed into AI tools to create personalised comms across a multitude of brand owned platforms. According to Shopify “checkout will supersede social media as the premier surface for gathering data and building your picture of a customer”. Based on conversion not intent, these insights can make for very compelling upsell and cross- sell opportunities.

Robust data is everything for ASDA rewards. Using predictive AI models, they analyse purchase history, browsing behaviour, demographics and collect customer feedback to paint a picture of future shopping behaviour. By anticipating the need, ASAD can be proactive with offering a personalized product solution and build loyalty by creating relevance.

Sephora kicks it up a notch when it comes to building customer profiles. With their industry insight, they use AI-powered intermittent quizzing to understand customers' intent. Like a good friend over coffee Seophora will politely prod what your skin concerns are, how you feel about it and what your priorities are. Then the brand responds with helpful ways to tackle your specific problem. Solutions, such as a highly personalised 5-step routine, made up of products they can sell you, are tailored to individual concerns. Through this approach Sephora gains valuable insights on consumers who are open to engaging with their interactive format. 

Ads that slay

These examples underline that a great user experience starts with putting people first. Human-centric design meets AI to enhance, not define your personalisation strategy.

In other words, these brands are targeting the right audience, with meaningful creative, at a relevant moment at just the right frequency to not be annoying. Frequency is less sexy than talking about the impact of striking images and emotive messages on your audience, but it’s vital when we are talking about personalised ad campaigns.

It’s the worst when you feel like a brand is following you around, on every platform. We marketers are highly aware of the role frequency plays in creating campaigns that boost sales while preventing ad fatigue. DMA customer engagement research showed that 88% of marketers feel personalisation is often or sometimes too intrusive.

So how do we get it right? AI tools have hugely driven the effectiveness of advertising. Particularly when it comes to deciding which consumers to target and scheduling ads at the optimal frequency. According to research from Nielsen consumers need to see an advert 5-7 times, before they consider intent. But there is still the matter of what they are seeing, the content itself. This is why creative diversity is crucial and dynamic creative optimisation is taking centre stage. Imagine seeing the same ad seven times, the risk is by the fourth your brain has already switched off interest. It feels like a lot if you are plugging the same product.

Loop Earplugs has nailed it. I’m not saying this just because I was personally targeted and can now sleep soundly despite my youthful neighbours making a racket on a Tuesday. They have a compelling brand story; a functional and aesthetic product and have effectively used AI to target and understand their audience.

What many believe to be most significant in their success, is their commitment to the test and learn approach. They invested heavily (€1.5 million monthly) running ads across a breadth of social media platforms and used A/B testing at scale to refine ads and landing pages for different audience segments. These ads all feel different. Tailoring creative to focus on a variety of messages, to reflect the product use and resonate with the target demographic. It's a master class on using data-driven insights to make ads feel personal and see huge ROI.