This lesson explores the use of data and AI to deliver increasingly personalised experiences—from simple targeting to “hyper-personalisation” .

5.1 The Concept of Personalisation
Personalisation is the process of tailoring products, services, and communications to individual customer characteristics, behaviours, and needs. This is a key driver of satisfaction and loyalty .

5.2 The “Hyper-Personalised” Banking Experience
Hyper-personalisation takes this a step further by using real-time data and AI to anticipate customer needs before they are even expressed. It involves providing products, services, and communications that are relevant based on the customer’s current behaviour and predicted future needs. This requires a sophisticated data infrastructure and analytics capability .

5.3 Data-Driven Personalisation Techniques
Banks use a range of techniques to achieve personalisation:

• AI and Big Data Analytics: Analysing large datasets to understand customer behaviour and predict needs.

• Recommendation Engines: Suggesting products (e.g., a specific loan product) or actions (e.g., “Next Best Action”) based on the customer’s profile and behaviour .

• Personalised Customer Journeys: Mapping and optimising the journey for different customer segments .

5.4 Data Privacy and Ethical AI
The drive for personalisation must be balanced with data privacy and the responsible use of AI. Banks must adhere to data governance and security regulations (e.g., GDPR) and ensure that AI algorithms are used ethically to avoid bias and unfair outcomes .