What it is and why it matters

Personalisation uses data and analytics to tailor customer experiences to the individual. Using shopping history, demographic data and pattern recognition data, an individual’s experience can be modified to fit their unique preferences. Offering options that best fit their needs helps the customer to find what they want quickly, giving them more personalised experiences, ideally leading to happier customers and more sales.

History of Personalisation

While personalisation may be thoroughly modern and you might think it’s driven solely by technology, that’s far from true. Personalisation has been around since the first shop owners.

For example, in the 1800s a man might walk into a cobbler and ask for a pair of shoes. The cobbler could look at his customer card to see what size the man wears, how much he usually spends, how much time he spends on his feet – and then make a new pair of shoes based on his previous customer data. Especially in smaller towns and villages, early retailers were likely to recognise their customer and be able to create just what the customer needed. This level of personalisation became scarce after the Industrial Revolution when mass production largely replaced handmade items. As the early stages of the internet eventually came around, personalisation was still not much of a concern and most marketing teams would cater to each customer the same way.

However, the customer experience shifted when web-based companies, such as Amazon, appeared. Web personalisation first took off with Amazon due to its “customers who bought this item also purchased …” feature. And the age of the recommendation engine was born. This enabled grouping of customer segments based on merchandise preferences to be shown to other buyers in the same segment. Today, segmentation is not considered the purest form of personalisation, but it allowed for early personalised experiences and began the growth of the concept tremendously.

Personalisation is key in telecommunications. And analytics is the natural solution.

Learn how telecommunications company Telenor Norway uses real-time data and machine learning to personalise customer offers, enhance business decisions, improve customer service and continuously adapt to customers’ needs.

Personalisation in Today’s World

Find out how personalisation is used today.

Modernising Marketing

Health care consumers expect the ease and personalisation of their health care experiences to match their interactions with their favourite brands – like Amazon and Apple. Delivering on these expectations during critical moments in the complexity of the health system can change member actions and improve health. In this webinar, our experts will peek into retail’s personalisation playbook for digital transformation, bringing a 360-degree orchestration of customer engagement and behaviour.

AI and Personalisation

Learn how AI improves personalisation by taking the guesswork out of marketing. This article explores the transformation of marketing that machine learning has started and how it ultimately encourages a more unique, exciting experience for the customer. It discusses the benefits of automating large-scale, repetitive tasks to give marketers more time to focus on creating and planning. AI also allows for more detailed insights, which leads to a more memorable impression on the customer.

Balancing Personalisation and Privacy

As technology advances and starts to collect more customer data, people have become increasingly wary of how it affects their privacy. Security breaches, government use of personal information and marketing communications that are a little too personal are making people more on edge about their personal information being shared. This makes it difficult for marketers to know the balance between how much personalisation is going to make someone’s privacy feel violated. As technology expands, it becomes vital for companies to be able to show two things: that they understand the customer and can protect their personal information.

Who’s using personalisation?

Personalisation is commonly talked about in retail; however, it’s useful across practically every industry. The reason for this is because people are unique. There is no single mould for everyone and that is what makes personalisation offerings so successful. Through machine learning, marketing teams consider people’s differences and present them with what they want or need individually by using customer data.


Personalisation is huge in retail. Now that online shopping has flourished, customer service is not required right from the start. Using customer data, companies must be able to show a site visitor the right item, in the right place, at the right time to make a sale. Personalisation also encourages repeat customers, which is highly valuable in any business.


Personalisation in the financial industry allows employees to give the right financial advice because it provides them with the customer’s goals and priorities. It also ensures that there is consistent advertising across marketing communication channels, which in turn strengthens the customer experience.

Health Care

The health care industry uses personalisation options in many ways. For example, the COPD Foundation uses customer feedback to shift its focus onto topics and problems that are specific to customer needs. It ensures that clients aren’t being ignored and are able to maintain their health as much as possible.


Honda and Kia both use personalisation throughout their customer surveys. They use real-time analytics to quickly evaluate the customer data received and then have it processed in a way that alerts them of emerging trends and problem areas. This allows the companies to easily digest the feedback and change customer experience, catering it to what's needed to provide excellent customer service.

When I look at hype and innovation, I think of companies that are able to stand out by putting a smile on people's faces and really by providing what I call a frictionless customer journey. These are companies that are actually combining convenience, personalisation and timing to really deliver and excel at bringing customer satisfaction. Steven Hofmans Customer Experience, Analytics and Marketing Adviser SAS

CNM Helps Nonprofits

Nonprofits were one of the many organisations significantly affected by COVID-19. However, personalisation offerings allowed them to keep up with demand and further their efforts during the crisis. Their use of analytics allowed them to build back safer and more healthy communities. Watch this video to take a closer look into how nonprofits fought back during COVID.

How Personalisation Works

Personalisation is mainly accomplished today by using algorithms and machine learning. Algorithmic complexity varies from basic to advanced, but they all offer a degree of differentiation. Basic algorithms might present new products or bestsellers to a buyer. More advanced algorithms for personalisation will be able to identify specific things about a customer and recommend similar items. For example, Netflix uses an algorithm that looks at the shows you watch in real time, and then recommends shows to you based on your viewing data. Decision trees are created to direct you to different paths to find more products related to your known interests.

In many ways, personalisation is the modern equivalent of excellent customer service. Customers expect it and may even become annoyed when the sites they visit do not include personalisation features. When looking at car insurance for a new driver, for example, an insurer who knows the ages of your children and what types of cars you drive can personalise an offer more quickly.

There are multiple approaches to personalisation. Here are four ways personalisation may be used in companies either separately or in conjunction with each other:

This form of personalisation focuses on using factors (such as location or education level) to know more about a person’s perspective, and therefore, the context. This allows for content that not only the viewer would like to see, but also relates to their situation. For example, by knowing someone’s location, a company suggests the nearest store that has the shirt in stock that a viewer wants to buy. Contextualisation allows for an easier way for customers to navigate through the valleys of information on the internet.

Hyper personalisation
Hyper personalisation is exactly what it sounds like. Machine learning is used to consider more customer data than personalisation to achieve more helpful personalisation offerings. It allows your website to act in real time and adapt its content as the customer moves through a web page.

Real-time interactions for the customer journey
Personalisation can also extend beyond marketing and be an asset to customer-facing functions like sales, service and support. Sophisticated analytical decision engines can orchestrate two-way, interactive engagements between consumers and brands, allowing the customer needs to be met immediately. These next-best experiences in real time require customer data platforms, advanced analytics, machine learning, automated processes and integrations with operational systems to create an engaging customer experience.

Customer recognition programs
Customer recognition programs are rather popular nowadays. Brands have found that analysing consumer behaviour is one of the top five drivers for customer loyalty. As a result, many businesses implement features such as reward systems and loyalty programs. This serves as a benefit to both the customers and companies as the recognition programs make the customer feel excited and motivated to keep coming back and claim their rewards.

Next Steps

Check out the Reimagine Marketing podcast and learn more about how customer experience is advancing technologically.

Personalisation Solutions

Customer experience is a key part of any business. With SAS® for customer journey activation, you can propel your business to the top by using the latest technology, machine learning and tactics to get to know your customer better. This allows for a high amount of personalisation options for individuals and can better predict what a customer may want or need.