21 August 2026
Enables Hyper-Personalisation
Personalisation is now a standard part of modern marketing. Customers expect brands to recognise who they are, understand their preferences and make communications more relevant.
However, there’s a significant difference between basic personalisation and genuinely responding to an individual customer.
Using someone's name, placing them into a segment or recommending products based on a previous purchase can all improve relevance. Hyper-personalisation goes further though. It uses a broader range of customer data, behavioural signals and contextual information to determine what somebody is most likely to need or respond to at a particular moment.
While the opportunities for marketers are huge, the challenge is that hyper-personalisation depends heavily on having the right customer data connected and ready to use.
What makes hyper-personalisation different?
Traditional personalisation often relies on a limited number of customer attributes.
For example, a retailer might tailor communications around previous purchases. A charity might adapt messaging according to donation history. A travel business could personalise offers based on a customer's usual destination.
Hyper-personalisation, however, builds a more complete picture. Instead of looking only at what a customer bought last time (effectively one dimension), an organisation might also consider what they have recently viewed online, how often they purchase, whether they have responded to previous communications, their predicted future value and how their behaviour compares with similar customers.
This can influence the offer someone receives, the content they see, the channel used to contact them, or whether they should be contacted at all.
The aim isn't necessarily to create a completely unique interaction for every individual. It’s to make better decisions using a fuller and more current understanding of the customer. And that all depends on data.
Why connected customer data matters
Most organisations already hold large amounts of customer information.
This could be in the form of transactions that sit in an ecommerce platform, email engagement that’s recorded elsewhere or website behaviour that lives within analytics tools. Additionally, CRM, service systems, mobile apps, loyalty platforms and offline databases hold additional pieces of the picture.
Individually, each source can be valuable. The problem is that personalisation based on only one or two systems is inevitably limited in scope.
A customer might appear to be a strong candidate for a product promotion because they have repeatedly browsed a particular category online. If another system shows they bought the product in store yesterday, that same message suddenly becomes far less relevant.
Therefore, hyper-personalisation relies on connecting these different signals and making them available to the people and systems responsible for deciding what happens next. Four capabilities are particularly important:
A unified customer view
Different platforms frequently hold different versions of the same customer. An email address might identify someone in one system, a loyalty number in another and a device ID somewhere else. Unless those records can be reliably connected, personalisation will always be based on an incomplete picture.
A unified customer view brings those records together and creates a more consistent understanding of the relationship.
Timely data
Customer behaviour changes quickly. Someone researching a product today may be receptive to relevant information now, but much less so three weeks later. A previously active customer may suddenly disengage. A prospect may convert and no longer need acquisition messaging.
Not every use case requires true real-time data, but the information needs to be current enough for the decision being made.
Intelligent decision-making
Connected data creates possibilities, but it doesn’t decide what should happen next. Rules, segmentation, predictive models and next-best-action approaches can all help organisations interpret customer behaviour and identify the most appropriate response.
AI is certainly increasing the sophistication of those decisions, but the underlying principle has not changed. Better outputs still depend on complete, accurate and relevant customer information.
Activation across channel
Insight has limited value if it remains trapped inside a database or analytics environment.
Once an organisation identifies the right action, that decision needs to reach the email platform, website, mobile app, advertising network, contact centre or other channel responsible for customer engagement.
That connection between data, decisioning and activation is what allows personalisation to become consistent across the wider customer journey.
Hyper-personalisation in practice
The impact becomes clearer when we look at how organisations are already using connected customer data:
Domino's: improving customer targeting
Domino's Mexican operation was managing customer interactions across dozens of digital channels and applications, with information distributed between different systems.
By consolidating customer data through Twilio Segment, its parent company Alsea created a more complete customer view that marketing teams could use for targeting.
Domino's was able to build audiences based on factors such as recency, frequency and monetary value, helping the business understand how likely customers were to reorder and adapt advertising accordingly.
According to Twilio's case study, the programme delivered a 65% reduction in cost per acquisition and a 700% increase in return on advertising spend through Google campaigns. The interesting point is that personalisation was not limited to owned channels such as email or the website. Connected customer data also improved decisions around paid media, helping the business become more selective about who it targeted and crucially, when.
Dunelm: creating more relevant customer journeys
Dunelm faced a similar challenge. Valuable first-party customer data was spread across multiple platforms, limiting the retailer's ability to use it effectively.
By connecting those sources through Tealium's Customer Data Platform, Dunelm was able to create more targeted, behaviour-led marketing.
One example was its new-customer welcome journey. Rather than treating every customer in the same way, Dunelm used individual behaviour to determine someone's preferred product category and adapt the content they received.
Tealium reports that this activity contributed to a 7% increase in click-through rates and a 23% uplift in conversion rates.
Dunelm also improved its ability to identify customers who’d abandoned baskets, resulting in a reported 15% conversion uplift.
The example demonstrates an important point though: hyper-personalisation doesn’t have to begin with a huge programme covering every customer interaction. It can start with a defined moment in the journey where better data enables a better decision.
Six Flags: creating the foundation to scale
Six Flags provides a slightly different example. Following its merger with Cedar Fair, the combined organisation needed to bring together customer data and marketing technology from two previously separate businesses.
Its ambitions included creating more personalised guest experiences, improving loyalty and increasing customer value. But before any of that could happen at scale, the underlying data and technology environment needed to be simplified.
Working with Merkle and Treasure Data, Six Flags began consolidating its customer data and marketing technology around a new customer-data architecture.
According to Treasure Data, this consolidation generated more than $1 million in technology cost savings, while creating a stronger foundation for future customer engagement.
The significance here is less about one individual personalised campaign and more about creating the infrastructure required to support personalisation across the organisation.
It’s a useful reminder that better customer experiences often begin behind the scenes.
What can marketers take from these examples?
Domino's, Dunelm and Six Flags operate in very different markets, but the pattern is similar.
Each started with valuable customer information spread across multiple systems. Bringing that data together expanded what the organisation could do with it.
For Domino's, that meant smarter advertising and audience targeting. For Dunelm, it created more relevant customer journeys. For Six Flags, it established the foundation needed to develop personalisation at greater scale.
There are three broad lessons.
First, start with the customer data rather than the channel. Personalising one touchpoint while the rest of the customer relationship remains disconnected places an immediate limit on relevance.
Second, focus on useful personalisation. There’s nothing to be gained from hoarding data, and there’s no value in demonstrating how much data you can (or do) hold about somebody. The focus should be to make interactions more appropriate, useful or timely.
And third, start with a defined problem. Identify an outcome you want to improve, understand what customer information would allow you to make a better decision, and then determine how that decision can be activated.
Building towards better personalisation
The technology available to marketers continues to become more sophisticated.
Predictive models can anticipate behaviour. Decisioning tools can recommend next-best actions. AI can analyse increasingly complex customer information and support more dynamic interactions. But these capabilities still depend on the same foundation.
Customer data needs to be connected, trusted, current and accessible to the marketing, insight and AI tools responsible for using it.
Euler Mechanica creates that foundation by bringing customer data together into a unified view and making it available to the systems that need it.
By providing one door to all your data, Mechanica gives organisations the customer understanding they need to move beyond isolated personalisation and create more connected, relevant experiences.