Personalised marketing plays a key role, enhancing a campaign’s ability to reach the right audiences and resonate with them. Tailored content that reflects audience preferences, behaviours, and contexts captures attention more effectively, drives engagement, and amplifies campaign impact.  

Today, in a crowded attention economy, consumers encounter countless touchpoints, including platforms, brands, creators, and communities, which makes relevance a key differentiator. Traditional personalisation methods often rely on extensive data and resources, and while they can create meaningful experiences, they require more time to keep up in today’s fast-moving environment. Therefore, modern personalisation requires a faster and more adaptive approach that responds to real-time behaviours and evolving expectations. 

In this era of AI, we have an opportunity to rethink how personalised marketing works. AI allows marketers to uncover deeper insights into audiences’ sentiment, behaviours, and intents. Rather than guessing, AI boosts personalised marketing by predicting what audiences are doing in real time. 

So, how does AI help with hyper-personalised marketing? 

We have always personalised through data, audience insights, and ongoing content testing. The introduction of AI does not replace these fundamentals; rather, it enhances the operational side of the work.  

For example, in media buying, AI-driven tools streamline processes by analysing behavioural patterns at speed, organising audiences more precisely, and reducing the time spent on routine optimisation tasks. This allows us to shift our focus from mechanical adjustments to higher-level decisions such as refining objectives, shaping strategy, and aligning campaigns with broader business goals. 

When it comes to messaging, generative tools make it easier to produce variations that suit different contexts or audience groups. The direction, tone, and storytelling still come from us marketers; AI tools simply speed up execution, letting us test more versions without increasing production timelines. 

For product development and customer journey planning, AI-driven research tools help surface customer sentiment, recurring issues or shifting behaviours by analysing large volumes of feedback. This gives us faster clarity on what customers are responding to, so adjustments or improvements can be prioritised earlier in the cycle. 

Hyper-Personalisation in Social Media 

As these AI capabilities evolve, the most immediate and visible impact happens on social platforms, where personalisation already plays a central role. 

AI-powered creative variation: Meta’s Advantage+ Creative uses generative AI to automatically produce and optimise ad variations across image, video, text, placement, and layout, based on predicted audience response. This lets brands scale personalised messaging without manually creating dozens of assets. 

Dynamic optimisation and automated targeting: TikTok’s Smart+ system can automate audience discovery, creative variations, and campaign optimisation once we set the objective and budget. Its AI continuously learns from user behaviour to refine delivery and improve relevance over time. 

Feed-level personalisation: Platforms like TikTok, Instagram Reels and YouTube Shorts use feed‑ranking engines that monitor watch time, engagement, and interaction history to curate content per user — which means personalised ad delivery becomes a possible (and common) follow‑up, since ads are often served through the same ranking/feed engines. 

AI-supported personalised engagement: Tools like Sprinklr AI and Zendesk AI assist with comment moderation, intent detection and suggested replies, making one-to-one engagement scalable while keeping communication relevant and human. 

How do we use AI while respecting our audiences’ privacy? 

While AI tools enable powerful hyper-personalisation across social platforms, the speed and scale of automation also introduce new ethical responsibilities. As AI systems gain autonomy, the line between “helpful” and “intrusive” can blur. Relying solely on AI to automate tasks without attention to privacy, data governance, or user consent can quickly erode trust. 

Instead, we must prioritise transparency, user control, and ethical oversight. This means:

• Clearly communicating how data is collected and used. 
• Providing users with choice and control over their settings. 
• Ensuring data security, anonymisation where possible, and compliance with privacy regulations. 
• Regularly auditing AI outputs to prevent bias, ensure fairness, and maintain brand values. 
• Using AI to enhance work, not replace human judgment, letting humans oversee sensitive decisions and creative direction. 

When done right, responsible AI-driven personalisation can respect user privacy while delivering relevance, value, and trust. 

In conclusion, 

AI technology is just picking up speed, and as tools evolve, there are more areas of marketing where it can add value, from campaign planning and content creation to customer experience and operational optimisation. 

Hyper-personalised marketing in the age of AI is not about letting technology take over but about amplifying human creativity. On the front end, AI helps make customer experiences more relevant and engaging. On the back end, it streamlines tasks such as optimisation, testing, and analysis, freeing teams to focus on strategy, insights, and creative decision-making. 

Ultimately, AI can predict preferences and deliver content efficiently, but only humans can shape the emotional impact and meaningful experience. Hyper-personalised marketing succeeds when AI and marketers’ creativity work in tandem, combining analytical insight with intuition, empathy, and storytelling. 

To turn these opportunities into action, organisations can benefit from practical guidance: AI marketing consultancy can provide usage guides covering ethics and strategy, marketing consultancy can define target audience segments and map a brand experience roadmap, and support in setting up AI tools ensures teams can implement these solutions effectively. 

References 

• Meta (2023). Advantage+ Creative – Meta for Business.  
• TechCrunch (2023). Meta debuts generative AI features for advertisers.  
• TechCrunch (2024). TikTok turns to generative AI to boost its ads business.  
• Salesforce (2023). Responsible AI in Marketing: Embedding Ethics by Design