Hyper-personalization is here -- but are organizations ready?

The rising demand for relevant, convenient and personalized customer experiences across all sectors has put modern organizations under pressure to adapt. McKinsey reports that almost three quarters of buyers now expect personalized interactions. The choice is clear: either embrace personalization, with individual offers or tailored updates based on previous habits, or risk falling behind competitors with a standardized approach. 

Many organizations already do this well. From personalized Netflix movie recommendations to tailored Google adverts built on previous searches, organizations are able to delight customers with tailored services.

But as personalization becomes the norm across brands, organizations must now go to even greater lengths to remain competitive. To truly meet customer expectations in 2025 and beyond, they must aim for hyper-personalization.

More targeted than ever

Hyper-personalization involves creating products, services or content that is customer-specific and highly granular. To achieve this, enterprises typically use a combination of machine learning, AI, analytics, real-time data and predictive analysis.

Instead of only looking at basic information like purchase history or demographics, hyper-personalization considers real-time behavior, location, changing circumstances and even emotional cues to decide which strategies to use. 

Global brands are already using hyper-personalization in everyday customer interactions. Starbucks now utilizes AI and real-time data on its app to send users unique offers for food and drinks they have purchased in the past. Amazon’s algorithm is now sophisticated enough to offer you themed store products based on films you have watched on Prime Video.

Over the next year, these capabilities will only become more sophisticated thanks to the increased prominence of agentic AI. With this transformative new approach to GenAI, many organizations will further enhance their personalized offerings by harnessing AI agents that go beyond chatbots to deliver automated personalized content directly into consumers’ hands. With the ability to understand context, execute tasks and reason independently, these agents will be a potent tool for companies looking to take their targeting to the next level. 

As this technology becomes ubiquitous, organizations that have not embraced it will start to fall behind. But currently many enterprises fall short on even the most fundamental requirements for hyper-personalization. Namely, they lack the data foundations needed to provide these services and technologies with the context they need.

Data architecture for hyper-personalization

Hyper-personalization services need to handle both structured and unstructured data at low latency -- all in real-time -- to make meaningful, context-aware decisions on the fly. This requires seamless integrations across unstructured data processing, vector databases and transactional systems for efficient storage and retrieval of diverse data types.

But too often, organizations looking to harness hyper-personalization store their data in disconnected silos in different systems. This means data can be too complex to access, taking time and extra resources. Not only that, but it’s hard to know whether the data being accessed is the right information and whether it is stored in the relevant format or language. 

Another issue is that many organizations suffer from database sprawl. This is when companies have a multitude of applications, all with their own databases, which may be operational, transactional or analytical in nature. The challenge comes when applications want to access this data. It’s difficult to move information from one database to another due to the different languages, management methods and processes that each one uses. Essentially, database sprawl makes it too complex to access the information required to carry out immediate analytics.

An additional need for hyper-personalized services is the ability to process data close to where it’s collected – at the edge. This is crucial for the speed at which the models must make intelligent decisions. Enterprises therefore need a data architecture that can process data both at the edge and centrally with the utmost speed.

A hyper-personalized future

There’s plenty to consider when implementing hyper-personalization, but if organizations can combine agentic AI with sophisticated database architecture, we could start to see some extremely interesting use-cases over the next couple of years. 

Keeping the customer in mind at all times will be vital, in terms of both what they do and don’t want to see from personalized apps. There remains a risk of ‘over-personalization’: if personalization goes too far, it can come across as unsettling and make people uncomfortable, causing them to lose trust in the brand. 

But done correctly, hyper-personalization could lead to transformative customer experiences that change how we interact with applications and services forever.

Image credit: Tashatuvango/Dreamstime.com

Rahul Pradhan is Vice President, Product and Strategy at Couchbase.