Hyper-personalisation, the delivery of uniquely tailored experiences that address the individual needs and preferences of each customer, is no longer a futuristic concept. It’s rapidly becoming a reality, driven by the power of artificial intelligence (AI). As organisations move beyond cautious exploration and fully embrace AI, hyper-personalisation will become a key objective.
AI and hyper-personalisation are poised to revolutionise how businesses operate. Dr Peter Stanski, Chief Technology Officer at V2 Digital, comments, “Historically, Customer Service and Developers have been responsible for designing Customer Experience (CX) systems. Today AI gives enterprises the power to redefine the entire spectrum of business operations, improving CX and Employee Experiences (EX), which previously have not always been considered in tandem at the cost of either one or the other”.
“For example, imagine receiving a bank notification about a fraudulent transaction on your business credit card that you need to address immediately. If the bank adopts effective hyper-personalisation, the system will be aware of your corporate credit card spending policy, contextualise your past spending patterns, understand who the merchant is behind some obscure merchant name, present it to you based on your personalised preferences before intuitively suggesting a likely reason and prompting a next step”.
By leveraging data analytics, machine learning, and AI algorithms, businesses can gain deep insights into individual customer behaviours, preferences and needs. This includes analysing both structured data (e.g. purchase history, demographics) and unstructured data (e.g. social media interactions, customer service transcripts).
From these deep insights organisations can create products, services and content for individual customers. Agnes So, Head of Customer Experience at Hotdoc, explains, “In the context of customer experience, particularly in healthcare which is what we service at HotDoc, “hyper-personalisation” means delivering tailored, contextually relevant experiences that address the unique needs and circumstances of each persona—be they practices, patients, or practitioners”.
“For medical practices, it could mean our CX programs enable them to streamline operations and ensure compliance with minimal administrative burden. For patients, it involves ensuring timely, relevant communication, such as appointment reminders are received with utmost trust in our systems. For practitioners, it’s about how we support simplifying workflows to reduce burnout and enable a focus on patient care”.
Hyper-personalisation is already being implemented across various industries, such as retail, travel, and finance. For example, e-commerce platforms like Amazon use AI to recommend products based on individual browsing history, purchase history, and even social media activity. Travel companies leverage AI to personalise travel itineraries, suggest destinations based on individual preferences, and provide real-time travel alerts.
Fishing for insights – The role of AI in enabling hyper-personalisation
AI-powered tools can scour massive dat asets to uncover hidden insights. It can act as a “smart net,” surfacing the most relevant data and enabling more accurate, earlier predictions to inform product development and service delivery. So says, “AI is capable of running through large data sets and bringing insights that we miss. Our current process is very much a fishing game, we have a hypothesis, and we go ‘fish’ for data to either prove or disprove and it’s not very efficient. AI could be the right partner to surface the fish that we need to catch and provide a greater net to catch it. This allows us to predict the future earlier and more accurately, informing what we build and how we service it”.
So also highlights how AI excels at automating repetitive interactions and reducing the workload of customer service and support staff, “AI in the CX space specifically has proven to be capable of automating repeatable interactions at scale or deflecting agent contact. In our industry, I see how people are exploring opportunities for AI to be more personalised to a patient in a healthcare context, providing coaching advice so patients can get healthier outcomes and reach their own individual goals”.
“Perhaps this is a way forward but it depends how comfortable we can get with that healthcare information being fed into an algorithm and how we keep within an ethical approach. Based upon what is the customer sentiment from our support team, we know that there’s still a good segment of patients who are not ok with that kind of data being read by anyone else other than them or their GP”.
According to Dr. Stanski, the growing demand for real-time, 360-degree customer insights is driving the evolution beyond smarter systems to next-generation intelligent platforms. “These intelligent platforms will be made up of multiple large language models (LLMs) and multiple predictive models (ML), pushing data back and forth in secure ways to surface the next generation of customer interactions at an exponential scale”.
“AI aligns the human customer’s interactions with the desired organisational brand experiences. For example, what I call the Amazon-isation of your systems is the evolution into more intelligent platforms from the customer order on the website to the warehouse robotic flows that move your ordered items that ultimately arrive in your mailbox”.
Casting the net – analysing structured and unstructured data
To drive hyper-personalisation, AI must effectively leverage both structured and unstructured data. Stanski comments, “Customer data, as does its application and integrations, plays a critical role in driving effective hyper-personalisation. I call this the era of “Intelligent Platforms in the Enterprise”. A term I use to describe Data platforms, Artificial Intelligent Systems and Corporate Systems that are open and interconnected, tied back to the user’s identity.
“All of these are looking at and inferring meaning from huge volumes of structured and unstructured customer data, so for them to operate optimally, it is paramount that the customer data can be trusted, securely encrypted and only accessed as needed by a combination of trusted organisations”.
Structured data is organised and formatted in a predefined way, making it easily understandable and processable by both computers and humans. This often involves tabular formats with clearly defined rows and columns, such as in a spreadsheet or a database.
For example, customer data with fields like “Name,” “Address,” and “Phone Number” is considered structured. This structured format allows computers to efficiently analyse the data to uncover insights, such as the total number of customers or the most common customer locations.
So explains how structured data is utilised by Hotdoc, “For us, structured data really comes from the behavioural data points we see within our own platform. This could include demographic details, support tickets or adoption and utilisation patterns. This data, while considered ‘structured’, is technically scattered across multiple systems and isn’t always easy to access, so our challenge is how we bring the data points together in a meaningful way”.
“We might do this by starting with a hypothesis based upon an objective that we want to achieve such as “How might we enable GPs to provide more care options when patients need it most?” and then bring in the structured data points we need from there. From that data, we might discover that Monday mornings are when patients need care the most. This is the “Quant” part and I see it as bringing in the breadth of information we need to get to an answer”.
Unstructured data, on the other hand, lacks a predefined format and is more challenging for computers to process. Examples include social media posts, emails, and customer reviews, which often contain a mix of text, images, and other media. “Unstructured is important feedback to round out the “Quant”. For us it comes from places such as surveys, socials or customer conversations. For example, we have a group of trusted board advisors within the industry who we talk to regularly for their opinions on pain points. While their feedback is highly unstructured and only one data point, the depth they give in comparison to structured data is incomparable” says so.
Ethical considerations
While hyper-personalisation offers significant benefits, it also raises important ethical considerations, such as data privacy, bias, and manipulation. It is crucial to ensure that hyper-personalisation is used responsibly and ethically.
So says, “I think for a while we’ve been very much ‘wait and see’ because AI is tricky in a healthcare context. We take our commitment to privacy and being a trusted advisor to our customers seriously so we’d rather take a risk averse approach here than get it wrong. However, I’m also fully aware that the AI industry is moving at light speed so we have to be ready to adapt when we see the right business cases for us”.
The future of hyper-personalisation
The future of hyper-personalisation will be shaped by emerging technologies such as generative AI, edge computing, and the metaverse. As AI continues to evolve, hyper-personalisation will become increasingly sophisticated and integrated into every aspect of the customer experience. By embracing ethical practices and leveraging AI responsibly, businesses can create truly unique and meaningful customer experiences that build lasting relationships and drive business growth.
Stanski advises, “AI projects are a team sport that define a brand’s “moments that matter” when it comes to customer hyper-personalised experiences. Integrating customer personas, customer engagement journeys, tangible workflows, and information flows into and out of machine learning and generative modules is paramount”.
“With the increasing complexity of this, CX assistants (such as Agentic AI) can help to traverse millions of interactions in your digital vaults. These assets are the customer interactions that businesses should be keeping an eye on. Whether that be with systems and employees, AI can highlight pitfalls and opportunities to refine personalisation into the new reimagined hyper-personalised brand customer experience moments”.