Elevating experiences:
A new spin on personalisation

Successful personalisation strategy begins with a strong data foundation that allows you to deeply understand the preferences of your customers. Purchase history, browsing behaviour, and customer feedback are just a few of the useful types of information that brands can analyse to better understand what drives their customers.

Traditionally, personalisation meant creating audience segments and sending out marketing campaigns via social media, third-party cookies, or emails. But as consumer expectations evolve, companies are shifting towards micro-segmentation. Instead of just looking at demographics, they're considering psychographic patterns and individual preferences. “The big challenge now is how to scale this hyper-personalisation effectively,” says Ankit Jain, Data, Analytics & AI Sales Leader at Google Cloud. “That’s where generative AI steps in, helping companies manage the complexity of delivering personalised experiences at scale.”

By integrating structured data (such as purchase history) and unstructured data (such as social media interactions), it becomes possible to develop a holistic view of your customer and unlock ‌actionable insights from that data to create natural and intuitive customer experiences.

"The tech can now just eat that unstructured data alive and give you really useful data points and signals that you can use to drive much better personalisation," 

says Karl Hampson, Chief Technology Officer, Data & AI, at Kin + Carta.

When the tech is that powerful, the limiting factor in developing your personalisation programme becomes focused on business questions: What value are you trying to create through personalisation?

“When you start personalisation by thinking about the tech, rather than thinking about the value, you're just going to tie yourself in knots,” says Heather Ryan, Lead Data Consultant, at Kin + Carta. “You're better focusing on what's going to give you the biggest reward, identify the data you need to execute that, and then do that one use case, start to finish.”

That means your personalisation team shouldn’t be limited to technologists. Make sure you’re pulling in people across specialties, including design, product, and customer service. This inclusive approach helps teams check for strategic alignment, avoid biases, spot potential issues quickly, and optimise the customer experience, all while setting the stage for implementing universally beneficial designs.

"What’s really important is making sure that you’ve got a multidisciplinary team who are involved in the design of the system,” says Ewan Nicolson, Director of Data & AI at Kin + Carta. “You need human intervention throughout. You don’t need somebody babysitting the thing because you’ve designed it in such a way that equity is baked in."

And that level of care is going to become even more important as we move away from personalisation solely based on records of past behaviour and start using zero-party and first-party data to develop fine-grain personalisation models that can adapt in real-time to changing preferences.

Consider how Chipotle uses first-party data to customise its loyalty programme offerings. By 2022, 60% of promotional offers sent to loyalty members were personalised. In 2023, Chipotle expanded on this through the “Freepotle” programme, which generated 10 personalised rewards throughout the year. The company gets additional data with every interaction to further tailor and personalise reward offerings and product selections during the ordering process.

Personalisation can magnify the power of customer relationships, but it can also amplify the downsides of betrayed customer trust and lost data. Plus, bad personalisation feels invasive. "Just because you can do personalisation doesn’t mean that it’s actually a better experience,” Nicolson says. “A lot of the time it can come across as a bit creepy."

To overcome that creep factor, focus on relevance and utility. Every interaction should feel to the customer like it’s a natural part of their journey. For example, every online retailer wants to deliver relevant product recommendations to regular customers. But those marketing messages need to feel like nudges along a path they’re choosing—not one they’re being forced down. If someone buys a set of dumbbells, personalised content about resistance bands or exercise mats would likely be received positively.

However, if you send that same customer personalised emails about weight-loss supplements, they may feel insulted, question how their data is being used, and become disengaged.

Instead of making assumptions, design personalisation programmes that show why you can be trusted. Consistently tailoring offerings to individual preferences helps customers feel valued and understood—and they expect no less.

“At the end of the day, deep personalisation means building a relationship with that individual consumer, understanding them at an intimate level, and then delivering value through their lens,”

says Ryan Estis, who advises Fortune 1000 financial services and retail brands on customer-centred growth.

That level of empathetic personalisation goes beyond merely addressing the needs of specific customer segments. It involves understanding and anticipating the diverse needs of all customers, ensuring that your personalisation efforts are inclusive and beneficial to the widest possible audience.

Those two requirements might seem like a contradiction, but we can often reconcile them by borrowing from a concept in urban planning known as “the curb-cut effect,” in which accessibility-friendly changes for one group can benefit much larger audiences. (The name comes from the way that making pavement curbs accessible for wheelchairs also improved access for parents with pushchairs, delivery workers, cyclists, and many others.)

For example, a retail brand may design its app to include the option to turn on audio messages primarily to serve the visually impaired, but then find that other customers choose to activate that feature for their own reasons.