Hyper-Personalisation with Salesforce Data 360: Potential and Foundations

What Is Hyper-Personalisation Really?

At this point, most professionals working within marketing and customer experience have heard the advice: “Know your customers and deliver experiences tailored to them. Do not focus on segments or personas, focus on the individual with the right experience at the right moment.” Statements like these have been common since the early days of marketing automation, and for many organisations, this used to be more of an ambition than a reality. However, recently it has become much more attainable with the combination of unified customer data, AI and composable content platforms.

Hyper-personalisation is essentially understanding every customer as an individual rather than as part of a segment or a persona classification, and showing this through meaningful, tailored interactions. A traditional approach to personalisation is to ask “Which group does this customer belong to?”, whereas hyper-personalisation aims at tackling “What does this specific individual want at this given moment?”

Hyper-personalisation is no longer just a promise. Today organisations can achieve this by combining the right data with the right content tools to deliver tailored experiences at scale. The hyper-personalisation benefits your organisation can measure when tailoring your customer experience to an individual level include higher conversion rates through more relevant communication and offerings. Customers who feel understood should also be less likely to unsubscribe, ensuring your ability to continuously communicate with your hard-earned subscriber base.

Segments Are No Longer the Obvious Answer to Your Marketing Campaigns

For years, segments have been the backbone when planning marketing campaigns. If you have worked with email marketing, you will know the workflow of mapping offers to larger pools of individuals and have probably had the thought: “Does the interest of everyone in my segment really match the product or service we are sending?”

Segments have structural weaknesses:

• Even segments used in automated campaigns are snapshots and rely on refresh frequency. If a segment refresh schedule is set to weekly, the intent a customer showed yesterday will be stale if the communication is triggered multiple days later.
• Segments focus on demographics and explicit data such as preferences, and are not optimised for timely implicit intent capture. For example, a segment built on the following criteria: “Men, 20–25, interested in running shoes” assumes everyone in the segment is equally likely to purchase running shoes, without considering that they likely have different budgets, buying moments and even granular needs depending on what type of running they do, what surface they run on and even weather conditions based on their location and season.
• Segments scale linearly and require the marketer to guess in advance. Every additional variation of your segment equals another version of your content and manual work, and this is often why marketing teams set a threshold on variations. Hyper-personalisation, by contrast, scales exponentially by acting on customer interactions.

How a Hyper-Personalised Campaign Could Look

Continuing with the same example of an online retailer selling sports equipment: the hyper-personalised setup ensures an individually unique experience for each of your customers. For example, a person in your previous segment has viewed the same set of trail running shoes multiple times in the last week and today added them to his cart in size 45.

By utilising your first-party data from across systems, it is possible to see a pattern in his buying cycle. He buys roughly every six months, his last purchase was three months ago and he is a few points away from the next loyalty tier. Additionally, you see the shoes in his size are low in stock.

The hyper-personalised campaign triggers a direct communication to him featuring exactly the shoes in his size, alongside affinity products, and generates a sense of urgency by highlighting that the shoes are low in stock and that unlocking the next loyalty tier would give him additional benefits. When he returns to the online shop or the app, the homepage shows specific trail running content and loyalty incentives to nudge the customer to take action, instead of showing generic campaigns.

In our current agentic AI era, this could advance further: for example, a personal shopping agent is launched next time he returns to the online shop or app. For more technical purchasing decisions in a different business context, the hyper-personalised campaign can also include sales and service teams who are provided with next-best actions from the hyper-personalised campaign setup.

To sum it up, hyper-personalised campaigns unify the customer experience across channels.

What Data Enables Hyper-Personalisation?

Personalisation across organisations is often focused on building campaigns based on explicit data. Explicit data is what the customer tells you through preference centres and surveys, but it limits the personalisation possibilities.

By collecting implicit first-party behavioural data with clear customer consent, you unlock an almost infinite potential for personalisation. Product engagement data helps to determine what to feature, while contextual data such as marketing engagement, device, channel, time and location supports the “where and when” to take action. Historical data such as sign-ups, registrations, account creation, purchases, returns, service cases and loyalty status supports providing customer context and adapting the offering itself. It can also be used to adapt the tone of communication.

In some cases, explicit and implicit signals can be conflicting. For example, a customer may have specifically selected a preference for offers related to swimwear but has only ever purchased running shoes. In general, explicit data is not rich enough to deliver hyper-personalisation but can be used to gather data points that cannot be deduced from implicit behaviour, for example via customer surveys.

Meanwhile, the implicit signals are frequent and real-time and are better suited for immediate personalisation. However, the real value is generated when you reconcile your data.

How Salesforce Data 360 Supports Hyper-Personalisation

Organisations generate vast amounts of data which are scattered across systems. Salesforce Data 360 helps break down data silos with its hundreds of native connectors for ingesting and transforming data into a harmonised data model. With deterministic and probabilistic reconciliation of customer profiles and explicit and implicit data signals, it turns these into a single, actionable, trusted customer identity that can be activated and orchestrated across channels and shared with your teams:

• Identity Resolution:
  Real-time and near real-time ingestion and unification of customer profiles. Matching rules determine which customer profiles from the same or different systems belong together and consolidate these while retaining data lineage and related historical data, and implicit and explicit data signals from every channel, enabling a single, trusted customer view.
• Calculated Insights and Predictive AI Models:
  Converts raw behavioural data and historical data points into actionable static and predictive attributes like churn risk, propensity scores, clustered CLV tiers, customer product affinities, etc.
• Real-Time Activation:
  Pushes insights to your teams and into activation platforms like Salesforce Marketing Cloud, Personalization and Agentforce in real time to deliver the right experience at the right moment.

The Next Step: Content at Scale

You have all the signals at this point. You know that the customer from earlier wants the trail running shoes in size 45, but you also have thousands of other customers with equally specific purchase intentions. The signals are only valuable if you can deliver individually tailored messages and content to each customer, in every language and every channel. This is where most organisations will hit a wall.

Some organisations are achieving this in one, two or, in the very best case, three channels with Marketing Cloud Engagement, utilising rigid data models and AMPscript to pull in personalised content and images for channels such as emails, push notifications and maybe SMS. But rigid data models, image feeds and scripting for personalisation do not facilitate agentic hyper-personalised content decision-making across every customer touchpoint.

To bridge this gap, Salesforce completed its acquisition of Contentful on 1 September 2026. Contentful is a composable content platform used by more than 4,800 brands. A composable content platform is a headless content management system (CMS) which stores content as structured, reusable building blocks such as a “product promotion” consisting of a headline, description, an image, a call to action and context about the intended audience, rather than a complete email. An orchestration system can retrieve these components via an API and provide them to any channel as part of a content framework, composing the final message delivered with reusable content blocks.

This is why content management systems like Contentful can provide the missing part of the puzzle: a native content layer working alongside Salesforce Data 360 and Agentforce for context and orchestration.

Where to Start

Start with a readiness assessment. Before pursuing hyper-personalisation and agentic capabilities, you need to address the foundation of both, which is your data model, identity spine, data consistency and data governance.

1. Establish the foundation: Start by defining your identity strategy. Set business and data governance rules for how you identify a person across ERP, CRM, data lakes, website, mobile apps, etc. prior to any personalisation initiatives.
2. Build trusted data: Test and evaluate Data 360 Identity Resolution matching rule sets. Reconcile implicit and explicit customer data to achieve a trusted, unified customer profile. Without a coherent customer profile, personalisation efforts are as good as guesswork.
3. Get value now: Explore and plan how you can use your current personalisation and dynamic content tools. Experiment with hyper-personalisation in one channel and measure the results.
4. Plan for scale today: Build a strategy for breaking content into reusable, tagged components prior to shifting towards a composable content platform.

Looking to explore how hyper-personalisation can effectively support your marketing? Our experts can help you assess your readiness and identify the right use cases for your business. Get in touch!