Blog
Hyper-personalised customer experiences with AI
- Why Hyper-personalisation matters for a better customer experience
- How AI is helping brands achieve Hyper-Personalisation
- How we help businesses build AI systems to achieve hyper-personalisation.
To keep up with the rapidly changing customer expectation and needs, businesses need to offer highly tailored products and services to customers in order to keep with the changing consumer behaviours. According to this research from Accenture, 81% customers think that it’s important for brands to reach them in a timely and personal manner. Hyper-personalisation allows just that.
What is Hyper-personalisation?
Hyper personalization uses customer information and data to tailor content, products, and services to a customer’s needs and preferences. The data used includes profile and demographic data, location, browsing, and purchasing decisions. This data is used to build predictive models that can imitate customer behaviour and inform dynamic personalisation of content and offers.
With the use of modern AI algorithms, models and data, this helps to deliver better customer experience, relevant content regarding products and services and enhances customer engagement. The need for brands to connect with their customers on a more deeper level is now more than ever. Hyper-personalisation allows brands to learn more about their customers with predictive modeling and use the technology to offer more relevant products, services and content, engross their end-users and strengthen their bond with them through contextualised communication.
Why Hyper-personalisation matters for a better customer experience?
As customers demand to be approached in a more personalised and timely manner, it is essential for a brand to ensure that their campaigns are targeting each customer’s expectations individually. Previously, traditional marketing techniques including manual customer segmentation were used but they were not enough to offer personalised content and offerings to the customers henceforth ended up delivering erroneous information or to the wrong customers. AI-based automation techniques help to develop hyper-personalised marketing and product/service offering and allows brands to incubate a more innovative and improved marketing strategy.
Hyper-personalisation pushes the boundaries of personalisation to an individual level that helps the customers get a unique experience and ultimately results in higher engagement and conversions. It allows them to enjoy a tailored experience that is precisely created for them and helps to upgrade the overall customer’s journey. As competition grows, companies want to outdo their competitors and offer superior customer experiences, hyper-personalisation offers the perfect solution for that and allows brands to interact with new and existing customers by leveraging data and power of AI.
How AI is Helping Brands Achieve Hyper-Personalization?
AI enables brands to develop a strong customer relationship by understanding their preferences. Data and information of customers is the fuel for AI. Businesses that have been collecting data have leverage when it comes to building personalisation systems. The data is used for training the AI models which are then used for predictive modelling and generating personalised recommendations and content for users. The customers are grouped into segments on the basis of profile data, social interactions, likes, and dislikes which further helps brands to communicate with the customers and create personalised content. To accomplish this, the following things are required.
Analyzing historical and real-time data
Machine learning algorithms are trained on vast customer data sets to learn how you want personalisation to be performed. Known factors and trends are identified to help teach the algorithm which can then be used to more thoroughly correlate and analyze data. This helps you develop more robust customer profiles and to better identify successful vs unsuccessful marketing campaigns.
Integration of technologies and data sources
AI should be integrated with all of the technologies that you want to apply insights to. This means being able to ingest data from a variety of sources as well as implement automated actions. Integrated systems should include content delivery networks, customer management platforms, communications platforms, web servers, and social media accounts.
You should also consider whether new technologies should be added to facilitate AI-recommendations. For example, integrating chatbots into your website.
Monitoring and measurement
Although AI-based systems are designed to do much of the work for you, you cannot rely on these systems blindly. If you mistrain your algorithm or your system becomes unavailable without you knowing it, it can completely sabotage your efforts.
It is important to monitor your tools to ensure that everything is working as expected and to intervene if it is not. Track effectiveness metrics for your implementation and make sure to tweak your system as needed. In the beginning, you may need to make more manual adjustments but in time your system will learn and improvements will come automatically.
As world-leading brands such as AMAZON, NETFLIX AND STARBUCKS are practicing digital-first strategy and providing their customers with highly contextualized emails and personalized product recommendations.
Framework for building hyper-personalisation systems
Building a hyper-personalisation system includes many challenges and hurdles like automating decisions at scale, achieving real-time view of customers with full context across all channels and understanding customer behaviour in context. A hyper-personalisation system building strategy must have the following key functions in order to ensure the reliability, performance and scalability of the system.
Accumulating data: Creating models for personalised recommendations and content requires a lot of preliminary work such as gathering data in order to train AI models. Data from all different sources and channels are collected. The data is then prepared for training the AI models which predict customer behaviour and offer recommendations in real time.
Customer segmentation: After the data is gathered then comes the process of segmenting customers into various segments. The collected data is used to develop niche segmentation conditions for effective marketing campaigns. Hyper-personalization requires automating segmentation which uses the real-time data obtained by subscriber’s actions such as previous consumptions and purchases on online websites, surveys, previously viewed videos, and engagement on social media posts.
Targeted customer journey: There are several ways for creating a hyper-personalized experience for the clients. It is essential to select proper channels, timings, and messages for individual customers according to their interests. It is more important than ever to augment the marketing approach to target the customers with relevant offers and the most essential step is to select a suitable platform for targeting customers such as SMS, in-app messages, and notifications.
Continuous monitoring and analysis: Hyper-personalization cannot be achieved immediately. It happens gradually and requires continuous monitoring and feedback from customer interactions and experiences to improve and provide sustainable processing in the future.
AI is changing personalisation as we know it. Personalization used to be a complex, clumsy, and manual process. Analysts and marketers had to sift through huge amounts of data, make sense of this data, create micro-segmentation, then deploy personalized campaigns, and optimize with A/B testing. That is not the case with hyper personalisation.
Hyper personalisation cuts back on segmentation and optimization time, enabling you to provide each user with content that suits individual needs. In this model, users are no longer groups of micro-segments; rather, they’re individual people with unique needs. Hyper personalization AI serves your users with optimized content, thus creating a customized and positive experience.
Have a look at this case study to see how an AI recommendation system that we built for an online retailer helped the, increase user conversions and engagement.
