By Claire Cundill, CBO, Leighton
When it comes to personalisation, it’s no longer enough to rely on simple recommendations or targeted emails.
Customers expect experiences tailored to their individual needs, and businesses must deliver if they want to keep up. With the advent of advanced technology creating deeper, more effective customer engagement, we’re seeing the boom of ‘personalisation 2.0’.
What is personalisation 2.0?
Also known as ‘hyper-personalisation’, personalisation 2.0 goes beyond basic tactics like “customers who bought this also bought that.” It taps into data from multiple sources like browsing history, purchase behaviour or real-time interactions to provide unique, highly targeted experiences. AI, machine learning, and data analytics are at the core, allowing companies to personalise at a much deeper level.
Instead of generic recommendations, personalisation 2.0 predicts what each customer wants before they ask. It’s proactive. For example, an e-commerce site might automatically adjust its homepage to highlight the exact products a returning visitor is most likely to buy, based on past behaviour. This level of personalisation feels natural and intuitive to the user.
Moving beyond basic tactics
Personalisation 2.0 takes customer engagement to new levels by moving beyond surface-level techniques. You’re no longer limited to predefined segments. With real-time data and predictive models, you can dynamically tailor content for each customer.
Imagine a user browsing a travel website. Instead of seeing the same deals as everyone else, they’re shown packages based on their previous searches, preferred destinations, and even real-time travel trends.
This approach is about context. Each interaction matters, and businesses can tailor experiences based on a customer’s intent, whether they’re shopping, researching, or simply exploring.
The role of AI and machine learning
Artificial intelligence (AI) and machine learning (ML) are key drivers behind personalisation 2.0. These technologies enable businesses to predict what customers want based on patterns in their behaviour. AI-powered tools analyse customer data to suggest products, services, or content at exactly the right time.
For example, a streaming service might use machine learning to recommend shows based on a user’s viewing history and preferences. But it doesn’t stop there. The system continues to learn and refine its suggestions as the user watches more content. This creates a feedback loop where the service becomes more attuned to the user’s tastes over time.
AI helps personalise experiences across channels, whether that’s an app, website, or even an in-store interaction. The result is a seamless experience that feels custom-made for each person.
Challenges and considerations
Personalisation 2.0 offers clear benefits, but it’s not without challenges. Implementing these advanced techniques requires a solid data infrastructure. Without reliable, wellorganised data, personalisation efforts will fall flat. Companies also need to manage data responsibly, ensuring privacy and compliance with regulations like GDPR.
Transparency is critical. Customers need to know how their data is being used and should always have control over what they share. Trust is key. If users feel uncomfortable with the way their data is handled, they’ll disengage.
Personalisation 2.0 is already reshaping how businesses engage with customers. By using AI and real-time data, companies can provide more relevant, meaningful interactions. The result? Higher customer satisfaction and better business outcomes.
leighton.com