Hyper-personalisation means that the products and services of companies adapt to their clients' individual needs as a result of an anticipatory customer profiling analysis. The future is different, it is about hyper-personalised interaction and experiences.

This means not only having analytics to track and anticipate customer behaviour, but also to create an environment where a new generation of analytics can adapt the products and services, in order to match them with customer behaviour in real-time, creating a personalised experience for each individual in an automated, cost-efficient way.

Hyper-personalisation is a broader name for customer-centricity.  It is not about the actual mass marketing concept of targeting the right product or content to a customer. It’s when a company builds its products, services, and processes around their customers' needs. Companies like Netflix, Spotify, and Amazon have proved that customer-centricity is the path to digital success. It is all about personalisation.


Companies are traditionally Product-Centric. The most successful companies in the digital space are Customer-Centric. In order to make it happen, we need to predict customer behaviour and also create a new generation of analytics to deal with the products. We also can predict future customer behaviour and anticipate tendencies, allowing products to adapt to clients in a much more efficient way. 


Customer-centricity can be classified in three ways:
1) Product Vision: it is when companies push their products based on self assumptions. It can be called customer-centric because Analytics are used to determine which person would be more likely to accept the offers. In this case clientes are just heard through marketing research and the experience is ignored. In this case, products and services are also static in relation to the customer needs. The feedback from this process is slow and expensive to companies;
2) 'Personas' approach: a virtual client is created to represent millions of clients with similar caracteristics. In this case, analytics are used to detect real people's characteristics to 'push' them into one of this 'personas' classifications. In this process, the real clientes are disconnected from the experience that company created to the virtual person, ignoring individual differences. Getting a feedback from the 'Personas' strategy can cost millions, as it is only possible to understand the real results by investing strongly in BI, data-science teams and data visualisation softwares.
3) Hyper-personalisation: it detect and treat each client on an individual level, following their behaviours as customers. It also adapts the products and services in real time according to these behaviourals, creating a individualised and meaningful experience. This process is self-learned and generates instantaneous feedback about client's experiences, which can improve customer interaction and reduce the costs of understanding how clients interact with the offers.
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Mass Approach Customer-Centricity
Individualised Approach


Three elements are certainly important: data preparation, customer behaviour, and product analytics. First step is to prepare data. In order to make it work, data quality is a crucial condition. It explains why we put so much emphasis into the first stage of data diagnostics. Second step - it is necessary to detect  customers' behaviour using advanced analytics. This step is fundamental, which is why we are specialised in customer profiling models having an entire team highly skilled in this field.  Third step is to build a totally new generation of adaptive product-analytics. Both, product and customer analytics, when combined, create a hyper-personalised customer experience - fourth step. And this movement will create a long-term investment effort by the largest companies globally, as they try to become more competitive in digital space.



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Hyper-personalisation is not a customer predictive profiling model. It contains customer-profiling technologies at an advanced level as part of the solution. However, traditional models ignore the product's "behaviour". 
It is neither a persona-type approach, where a virtual individual with average characteristics would - potentially 0 be representing millions of customers in average - or clusters. Some systems use this technique to try to offer the "right" product to each customer profile through CRMs supported by analytics. But it is not a hyper-personalised experience. It is still a mass experience of offering - or pushing - products. 
Hyper-personalisation models not only track customer behaviour with machine learning, and then adapt products in real-time to fit the detected behaviour, but also learn how the customer acts as a result of this experience, and takes this knowledge on a new level of learning and produce adaptation.
Our models are self-learning, they add those continuous pieces of learning to the customer behaviour profiles, improving more and more the hyper-personalisation experience with time through real-time maturation, also improving how the products should react to create more traction with customers.