Personalisation has existed on the web for decades, but the technology behind it has changed significantly. AI can now use richer customer signals to make decisions in real time, making individualised experiences possible at a scale that earlier systems could not support. At the same time, customers increasingly expect brands to understand their context and preferences. This is why personalisation has moved from an optimisation tactic to a core capability within the MarTech stack.
When you hear the word personalisation, you may think of product recommendations, personalised emails or a website that changes based on who is visiting. These capabilities feel relatively modern because the technology supporting them has advanced so quickly. The underlying idea, however, has been around for decades.
In the mid-1990s, researchers were already exploring how websites could adapt content based on user behaviour, preferences and clickstreams. By the late 1990s and early 2000s, commercial websites began putting simpler versions of the idea into practice. A website might remember that you had visited before, greet you by name after login, or recommend products based on your previous purchases.
These early approaches relied heavily on rules and static profiles. You defined a segment, created a condition and determined what that segment would see. It worked, but only within the limits of the data available and the number of rules your teams could realistically manage.
The 2010s brought eCommerce, social media, analytics and connected digital channels that generated much richer behavioural data. Machine learning began to improve recommendation engines, email targeting and website personalisation, allowing brands to respond to patterns that were difficult to identify manually.
Then came another major change. AI made it possible to interpret much larger volumes of data and make decisions at a level of speed and granularity that traditional rules could not match.
That progression matters because it explains why personalisation to the ‘degree of one’ has become so prominent in MarTech. The concept is old. The ability to operationalise it at scale is new.
MarTech platforms increasingly treat personalisation as a core capability because it connects technology directly to commercial outcomes.
Deloitte Digital's 2024 research found that brands that excel at personalisation are 48% more likely to have exceeded their revenue goals and 71% more likely to report improved customer loyalty than brands with lower personalisation maturity. The research also found that the number of brands treating personalisation as a core experience strategy had increased by 50% since 2022, while companies expected to increase their annual personalisation budgets by 29%.
That makes marketing personalisation particularly relevant to your MarTech strategy. If your technology can understand who a customer is, interpret what they are doing and determine what experience they should receive next, the value of the platform extends beyond storing data or publishing content. It starts influencing the customer journey itself.
This is also why personalisation increasingly sits alongside capabilities such as content management, experimentation, CRM, analytics and marketing automation. These technologies each manage a part of the customer journey, while personalisation connects the information they produce to the experience an individual receives.
The commercial logic is relatively straightforward. When the experience is more relevant, customers are more likely to engage with it. Better engagement can influence conversion, retention and lifetime value. That gives marketing and technology leaders a much stronger case for investing in the underlying data and technology.
Technology has changed what brands can deliver, but customer expectations have changed what brands need to deliver.
Adobe's 2025 Personalisation at Scale study, conducted with Forrester Consulting, found that 71% of both consumers and B2B buyers expect organisations to understand when, where and how they want personalised interactions. Interestingly, 75% of consumers also said that not every interaction with a company should be personalised. These findings help explain why personalisation has moved beyond simply adding more tailored content. For many customers, relevance depends on understanding context and knowing when personalisation adds value.
Consider the difference between arriving at a website that treats you like a completely unknown visitor and one that reflects your previous interactions, interests or stage in the buying journey.
The first experience makes you do the work. You have to find the relevant content, products or information yourself. The second reduces that effort by using available context to make the journey more relevant.
This is particularly important as customers interact with brands across more channels. Someone may discover your brand through search, engage with a social post, read an article, visit your website, download a resource and return several weeks later through an email. Each interaction generates a signal.
If those signals remain isolated, your customer may experience a series of disconnected interactions. With effective Customer data integration, those signals can inform a more coherent understanding of the individual and their intent.
That is where personalisation starts becoming a business capability rather than a collection of marketing features.
Traditional personalisation generally worked through predefined rules. If a customer performed action A, they received experience B.
That approach still has value, but it becomes difficult to manage when you have thousands or millions of customers, multiple channels, and constantly changing behavioural signals.
AI-powered personalisation changes the operating model by allowing systems to identify patterns across much larger datasets and make decisions based on a broader set of signals.
Imagine a B2B visitor researching a particular service on your website. Their previous content consumption, company information, engagement history and current behaviour can all provide clues about what they need. An AI-enabled system can use those signals to determine which content, recommendation or call to action is most relevant at that moment.
The important change is the move from predefined segments towards individual-level decisioning.
AI can also incorporate signals such as browsing behaviour, product affinity, engagement timing and predicted propensity. That makes personalisation increasingly predictive. Instead of responding only to what someone has already done, the system can estimate what they are likely to need next.
Generative AI adds another layer. Content variations can be created for different audiences and contexts without requiring marketers to manually produce every version. Recommendation engines can select relevant experiences, while decisioning systems can determine which variation should appear.
Together, these capabilities make an AI-driven customer experience possible across a much broader range of touchpoints.
Your website is often where brand discovery, research, consideration and conversion come together. Yet many organisations still give every visitor essentially the same experience.
That creates a gap between what your MarTech stack may know about a customer and what your website actually does with that knowledge.
Effective website personalisation can help close that gap. A returning customer can see content relevant to their previous interests. A prospect from a particular industry can be directed towards information that reflects their needs. A visitor further along the buying journey can encounter a more conversion-focused experience than someone discovering the brand for the first time.
The objective is not to make every page look different for every person. That would create unnecessary complexity. The objective is to use meaningful customer signals where they can improve the experience or move the customer closer to an outcome.
This distinction matters because personalisation without a clear purpose can quickly become technology for technology's sake.
A good Personalised website experience should answer a simple question: What can we show this person that is more useful because we know something about their context?
The answer might involve content, navigation, recommendations, offers, calls to action or even the timing of an interaction. The sophistication should follow the customer need, not the other way around.
The growing focus on personalisation is visible in the direction of major MarTech platforms.
Adobe has connected its content, experimentation and customer data capabilities with real-time decisioning and recommendations. Its approach reflects a broader movement towards using data to determine which experience a customer should receive across digital touchpoints.
Optimizely has extended its heritage in experimentation into recommendations and audience-based personalisation. The focus is moving from testing which experience performs better to continuously adapting experiences based on what the system learns.
HubSpot approaches personalisation through its CRM, marketing automation and content capabilities. Customer information can inform how brands communicate with contacts throughout the lifecycle, connecting digital interactions with pipeline and customer data.
These platforms are responding to the same commercial reality. Customers expect relevant experiences, while businesses want technology investments that can be connected to measurable outcomes.
Personalisation sits directly at that intersection.
The next question for marketing leaders is not whether personalisation is useful. It is whether your MarTech stack can support it effectively.
That requires you to look beyond individual features and examine how information moves through your ecosystem.
Your CRM may hold customer information. Your analytics platform may capture behaviour. Your CMS manages content. Your automation platform manages communications. Your experimentation platform evaluates experiences. AI may sit across several of these systems and use their data to make predictions or generate content.
If these systems operate in isolation, personalisation remains limited. If they can exchange reliable, permissioned and timely data, your technology can respond to customer context much more effectively.
This is why personalisation has become closely linked to data architecture and MarTech integration. The quality of the experience ultimately depends on the quality of the signals feeding it.
There is also an important governance consideration. More personalisation requires more responsible use of customer data. You need clear rules around consent, data access, model decisions and the types of information that can influence an experience. Trust has to remain part of the design.
The biggest change is perhaps organisational.
Personalisation used to be something a marketing team might test on a particular campaign, landing page or email journey. Increasingly, it is becoming a capability that sits across the customer lifecycle.
That requires marketing, data, technology and customer experience teams to work from a shared view of the customer. It also requires you to decide where personalisation can create genuine value rather than attempting to personalise every interaction.
The organisations that get this right will treat marketing personalisation as part of how their MarTech ecosystem operates. They will use customer data to understand context, AI to support decisions and experimentation to learn what works.
The technology will continue to improve, but the strategic principle is simpler: make the next customer interaction more relevant because of what you already know.
That is why personalisation now sits at the heart of the MarTech stack. It connects customer data with content, technology and decision-making, turning information about the customer into an experience designed around the customer.
At Vajra Global, we bring deep expertise across MarTech, customer experience, data and AI to help organisations make personalisation part of their broader growth system. We can assess your existing MarTech ecosystem, identify where customer data and technology can work better together, and design practical personalisation use cases around your customer journeys.
Our experience across platforms, marketing operations and AI also helps you move beyond isolated personalisation experiments. We can help you build the strategy, technology integrations and AI capabilities needed to deliver relevant experiences at scale, while keeping business outcomes and responsible data use at the centre.