AI-driven personalised customer experience at scale

Delivering a highly personalised customer experience at scale is a challenge that manual efforts alone cannot meet. In this article, we will explore the obstacles businesses face in achieving personalisation and demonstrate how automation, proprietary data, self-developed algorithms, and AI can work together to create a seamless, tailored customer experience.

While there are third-party tools available to help implement personalisation, they come with certain challenges:

• The customer experience is shaped by an unknown “black box” algorithm.
• Business needs and these black-box algorithms do not always align.
• The data generated by the process lacks transparency, making it difficult to develop customer insights.

How personalised customer experiences are created

A personalised customer experience is built on multiple calculations, each playing an independent role in shaping a specific aspect of the overall experience. When these calculations are combined, they create a dynamic, tailored experience driven by proprietary data.

By allowing data to flow seamlessly between calculations, businesses can generate personalised content that evolves with each customer’s behaviour.

Automated customer profiling

Automated profiling involves analysing a customer’s browsing history, purchase data, and behavioural events to calculate a score reflecting their interest across different dimensions. These dimensions determine how customers are segmented.

For example, dimensions may include product attributes or categories. A higher score in a particular dimension indicates greater interest in that area.

Unlike traditional methods, segmentation rules are not manually defined within the customer data platform (CDP). Instead, calculations run automatically, refreshing continuously within the database and CDP system. This ensures that customer profiles are always up to date and ready to be used for personalisation.

Through this automated process, each customer develops a multidimensional, self-updating profile that serves as the foundation for personalised experiences.

Generating personalised content for customer profiles

Personalised content is generated by matching products, articles, or banners to each customer based on their segmentation or profiling scores. This ensures that the most relevant content is displayed to them.

These calculations run continuously in the background, storing results as customer ID and content ID pairs. Different types of recommendations can be pre-calculated for various use cases.

Importantly, the process is data-driven. The algorithms use numerical data – such as sales figures, clicks, impressions, and timestamps – to determine ranking and relevance. Business-defined weighting factors further refine the output, ensuring that the recommendations align with business objectives.

For optimal targeting, each recommended content item must be linked to a relevant dimension in the customer’s profile. Without this connection, personalisation loses its effectiveness.

Delivering recommendations across channels

Once the system has generated the recommended customer ID and content ID pairs, they are enriched with descriptive data. This includes product catalogues, banner metadata, and content highlights containing key details such as title, description, image, and price.
At this point, the personalisation system can retrieve and display the most relevant content for each identified customer.

The final step is rendering the content in HTML, ensuring it appears seamlessly within the selected channel – whether that’s an e-commerce site, a mobile app, or an email newsletter. Typically, personalisation is implemented in owned channels, where customer identification is possible, and content can be freely managed.

From raw data to personalised recommendations: a complex journey

As this process illustrates, raw data undergoes multiple transformations before it reaches the customer as a personalised recommendation. These transformations involve numerous calculations, with data flowing through different stages to deliver meaningful and relevant content.

Since many calculations take place in the background, not everything can happen in real time. Some processes are best scheduled as batch operations to ensure efficiency and accuracy.

The role of AI in enhancing personalisation

On paper, the calculations may appear straightforward – especially when all segments, content, and variables are neatly linked through unique identifiers. In reality, however, businesses often face data quality issues that complicate personalisation:
Manual CRM entries are often unstructured, making them difficult to process despite containing valuable insights.

Data duplication leads to inconsistencies, with the same information stored multiple times in different formats.

A direct key linking customer profiles to the relevant content may not always exist.

AI-driven data processing can help overcome these challenges. For instance, session data from tools like Google Analytics can be analysed to identify direct correlations between customer behaviour and content preferences. AI can dynamically generate linking keys based on the highest correlation principle.

Additionally, generative AI can classify unstructured data into predefined business categories, making it easier to use for personalisation. AI can also automate the detection and resolution of duplicate records, ensuring data quality remains high over time.
By leveraging these AI-driven techniques, businesses can establish stronger connections between customer engagement data, CRM records, and personalised content, enabling scalable and automated personalisation.

Is the effort worth it?

The question remains – does personalisation at this scale justify the effort?
At first glance, it may seem like an overwhelming task. However, once the system is in place, businesses gain the ability to control and refine their personalisation strategy with transparency.

For example, adjusting algorithm weights can instantly influence recommendations across all customer profiles, allowing businesses to fine-tune the personalisation experience to align with strategic goals.

Beyond the immediate benefits, personalised customer experiences also generate valuable, fully transparent data. By analysing historical profile data, businesses can develop predictive customer journeys, anticipating future behaviours based on past interactions.

This is the true power of a personalised customer experience – it scales automatically, adapts across all segments and channels, and remains fully controllable using business data and decision-making.

The larger the dataset and customer volume, the greater the benefits – all without requiring additional effort.