Personalisation has long been the holy grail of marketing, but with the advent of artificial intelligence, it has evolved into something far more powerful: hyper-personalisation.
This takes personalised marketing from simply addressing a customer by their first name in emails to offering highly relevant, real-time experiences at scale. AI allows brands to tailor their messaging, products, and services based on an individual’s behaviour, preferences, and context all in real time.
Hyper-personalisation is more than just a trend; it is a necessity in today’s consumer-driven market. According to Accenture, 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations.
CMO’s who adopt hyper-personalisation strategies can drive deeper engagement, improve customer loyalty, and significantly boost revenue.
What is Hyper-personalisation?
Hyper-personalisation goes beyond traditional personalisation by using real-time data, AI, and machine learning to deliver experiences tailored to each individual customer. It draws from various data sources such as purchase history, browsing patterns, location and social media activity to provide a one-to-one marketing experience at every touchpoint.
Unlike basic personalisation, which might involve segmenting customers into broad categories, hyper-personalisation creates unique experiences for every individual.
This approach enables brands to offer not just the right message but also the right product at the right time through the right channel.
AI enables one-to-one marketing with real-time insights.
AI at the Core of Hyper-personalisation
AI is the driving force behind hyper-personalisation because it can analyse vast amounts of data and draw actionable insights far beyond human capacity. Here is how AI enables hyper-personalisation:
- Real-time Data Processing: AI processes real-time data, helping brands deliver up-to-the-second recommendations and content. For instance, Netflix’s AI algorithms analyse viewing habits to recommend shows tailored to each user’s tastes. This approach has resulted in 80% of its viewed content being driven by AI recommendations.
- Predictive Analytics: AI models can predict customer behaviour, anticipating future purchases or interactions based on past behaviour. For example, Amazon’s recommendation engine, responsible for 35% of the company’s total sales, uses AI to suggest products based on previous purchases and browsing behaviour.
- Dynamic Content Delivery: AI can dynamically adjust content in real time, ensuring that each user receives the most relevant message. This is a game-changer for email marketing, where subject lines, images, and offers can all be tailored for individual recipients.
Benefits of Hyper-Personalisation
Hyper-personalisation offers a range of benefits, including:
- Increased Engagement: A study by SmarterHQ found that 72% of consumers only engage with personalised messaging.
- Improved Customer Retention: Personalisation keeps customers coming back. McKinsey reports that personalisation can reduce acquisition costs by as much as 50%, lift revenues by 5% to 15%, and increase the efficiency of marketing spend by 10% to 30%.
- Higher Conversion Rates: Personalised experiences increase the likelihood of conversion. Research by Epsilon shows that 80% of consumers are more likely to make a purchase when brands offer personalised experiences.
Challenges of Hyper-personalisation
While the potential rewards of hyper-personalisation are immense, it is not without its challenges:
- Data Privacy: Hyper-personalisation relies on collecting and processing personal data, which raises privacy concerns. Brands need to be transparent about how they use data and ensure compliance with regulations such as GDPR.
- Data Quality: AI models are only as good as the data they are trained on. Inaccurate or incomplete data can lead to irrelevant or even harmful recommendations.
- Technical Expertise: Implementing hyper-personalisation requires a robust AI infrastructure, which can be costly and complex. Brands will need to invest in the right tools and talent to effectively leverage AI for hyper-personalisation.
Real-World Applications of Hyper-personalisation
- Retail: Brands like ASOS and Sephora use AI to offer hyper-personalised shopping experiences. By analysing customer behaviour and preferences, they provide personalised product recommendations, dynamic pricing, and tailored marketing messages in real time.
- Travel and Hospitality: Companies such as Marriott use AI to deliver hyper-personalised travel recommendations, from suggesting destinations to recommending hotel amenities based on a guest’s past stays.
- Financial Services: AI-powered chatbots in banking offer tailored financial advice, helping customers manage their accounts, provide investment insights, and even deliver personalised loan recommendations.
The Future of Hyper-personalisation
As AI technology continues to advance, hyper-personalisation will only become more sophisticated. Gartner predicts that by 2025, 80% of marketers who have invested in personalisation will abandon efforts that do not involve AI. This shift will usher in an era of true one-to-one marketing, where every interaction is tailored to the individual in real time.
For CMOs, the message is clear: hyper-personalisation is the future of marketing.
To stay competitive, brands need to invest in AI tools and strategies that enable them to deliver personalised experiences at scale.
