Hyper-personalisation at Scale: Leveraging AI for 1:1 Marketing

AI at the Core of Hyper-personalisation

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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:

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• 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.
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• 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.
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• 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.

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Benefits of Hyper-Personalisation

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Hyper-personalisation offers a range of benefits, including:

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• Increased Engagement: A study by SmarterHQ found that 72% of consumers only engage with personalised messaging.
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• 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%.
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• 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
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While the potential rewards of hyper-personalisation are immense, it is not without its challenges:
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• 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.
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• 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.
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• 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.

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Real-World Applications of Hyper-personalisation
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• 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.
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• 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.
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• 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.

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The Future of Hyper-personalisation
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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.
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For CMOs, the message is clear: hyper-personalisation is the future of marketing.
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To stay competitive, brands need to invest in AI tools and strategies that enable them to deliver personalised experiences at scale.

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