App personalisation
App personalisation is all about customising the user experience within a mobile application based on individual user preferences, behaviour, and other data points. This can include tailored content, personalised recommendations, customised interfaces, and notifications that are relevant to the user's interests and behaviours.
For example, a music streaming app can use personalisation to create a unique experience for each user. Based on listening habits, the app can recommend new songs, artists, and playlists that align with the user’s musical tastes. It can also personalise the app’s interface, highlighting favourite genres or recently played tracks. Personalised notifications can also inform users about new releases from their favourite artists or suggest music for specific activities, like working out or relaxing.
An e-commerce app could analyse browsing history, purchase behaviour, and wishlist items to recommend products that match the user's preferences and shopping habits. If a user usually purchases sportswear, the app can highlight new arrivals in athletic clothing and offer exclusive discounts on related items. Personalised push notifications can also alert users to sales events or restocks of previously viewed items, driving higher engagement and sales.
AI-driven personalisation
Unlike traditional personalisation methods that rely on basic rules and manual segmentation, AI can process complex datasets to uncover patterns, preferences, and predictions, enabling businesses to offer truly personalised interactions in real-time.
For example, in the travel industry, AI-driven personalisation can analyse a customer's booking history, preferred destinations, and travel behaviours to provide tailored recommendations. If a customer frequently books beach vacations, AI can highlight new beach destinations, exclusive resort deals, and relevant travel packages.
AI can also adapt to changes in customer behaviour much faster than traditional methods. If a user starts showing interest in adventure travel, AI can quickly adjust recommendations to include activities like hiking tours, adventure sports, and off-the-beaten-path destinations. This real-time personalisation ensures that customers receive relevant travel suggestions and increases the likelihood of repeat bookings.
Content delivery network (CDN)
A content delivery network (CDN) distributes servers across geographical locations, allowing users to access content quickly and efficiently, regardless of their physical location. CDNs work by caching content on multiple servers around the world. When a user requests content, the CDN directs the request to the nearest server, minimising the distance the data has to travel. This not only speeds up content delivery but also reduces the load on the origin server. For businesses, this means faster website performance, which can lead to higher user engagement and conversion rates.
In the context of personalisation, CDNs make sure that personalised content is delivered seamlessly. Personalised content, such as user-specific recommendations, targeted ads, and customised interfaces, requires rapid and reliable delivery to be effective. CDNs help achieve this by distributing personalised content closer to the end user, ensuring minimal delays. For example, an e-commerce site can use a CDN to deliver personalised product recommendations instantly, enhancing the shopping experience and increasing the likelihood of a purchase.
Customer 360 (C360)
Customer 360 (C360) gives businesses a complete view of their customers by pulling together data from every touchpoint and channel, allowing them to deliver highly personalised and consistent experiences every time they interact with a customer.
Traditionally, companies have struggled with siloed data, where information about customer interactions is scattered across various departments and platforms. This disconnected data makes it difficult to get a unified understanding of the customer, leading to disjointed and frustrating customer experiences. Customer 360 addresses this issue by integrating data from diverse sources, such as customer relationship management (CRM) systems, social media, email interactions, and purchase history. The result is a single, cohesive customer profile that provides a complete view of the individual.
For example, a retail company can use C360 data to identify high-value customers and tailor personalised recommendations or exclusive offers to encourage repeat business. Real-time data integration means customer profiles are constantly updated, allowing businesses to respond quickly to changes in customer behaviour. Plus, customer service representatives can quickly access all relevant information, providing faster and more personalised resolutions.