Unleashing The Power Of Data & AI: Revolutionising Personalisation At Scale

Personalisation at scale may sound like an oxymoron – after all, how can any business create genuine personalisation without one-on-one connections? Anne Ngo, customer strategy and martech director at Akcelo, reveals it’s not as hard as many would think.

Personalisation, when you first hear about it, sounds slightly whimsical. A nice way to better connect to consumers, and perhaps something to throw on top of whatever experience or product you’re already providing.

However, any marketer worth their salt now knows that personalisation is a crucial and competitive differentiating factor. Research shows that 71 per cent of consumers expect companies to deliver personalised interactions – and 76 per cent get frustrated when this doesn’t happen.

It’s clear that personalisation is absolutely fundamental, but where businesses may struggle is in delivering authentic, personalised connections with consumers in a cost-efficient and productive way. After all, how can a multinational, with thousands of employees, truly provide that “personal” touch?

To go beyond this struggle, the conversation must turn to personalisation at scale.

This is the ability to deliver tailored and customised experiences across multiple touch points, with more speed, efficiency and accuracy than a one-on-one strategy. A McKinsey study even found that personalisation at scale has the potential to create US$1.7 trillion to US$3 trillion (AU$2.62 trillion to AU$4 trillion) in new value.

It can not only boost customer satisfaction but also drive customer loyalty, repeat purchases, and advocacy. It’s recommendations, exclusive offers, discounts and much more – all laddering up to a fully customised experience that your customer can’t get anywhere else.

Done well, personalisation at scale can be the powerhouse behind building brand experiences. Done badly, it will become an unwieldy burden shouldered only by your marketing team.

To succeed, it must be a well-oiled machine. Before embarking on your personalisation at scale journey, here are the components you need to consider.

Break down the silos

If one part of the machine breaks down, it’s harder for the others to do their job.

To efficiently pursue personalisation at scale, marketers must adopt an end-to-end approach. They must be able to not only access and capture data, but to deliver insights and transform these into actions. They must then combine this with ‘composable content’, designed to be shaped in different ways without losing its core brand essence.

Unfortunately, the nature of many businesses is to silo both their data and teams.

Marketing leaders must reckon with the shifts that are transforming the ways in which business is conducted. Where previously the IT team would be responsible for deciding the platforms marketers want to use, it is now marketers and strategists who are at the forefront of making these technology, and business, decisions.

As part of this, there needs to be a mindset change within the business itself. Teams cannot be pitted against each other in the interests of fulfilling their own KPIs. Personalisation at scale needs to bring in all teams, from IT, to legal and research, to derive true actionable insights.

It’s more responsibility, but it’s also more freedom, and opens marketing teams up for pursuing true innovation.

The marriage of data and AI

Data is important – but if you can’t bring your data to life via actionable insights, it’s not serving its ultimate purpose.

The ability to deliver these insights increasingly depends on AI.

An automated AI engine can work through the strategy and implementation needed for personalisation at scale. Just think about a customer that receives 50 different messages, across different channels, within the first 14 days of signing up to a new service. Imagine the manpower that would have to be dedicated to parsing through the resulting data.

An AI engine can look at the engagement and attributes of this customer, compare it against other customers to predict the most likely journey, and set this customer on that journey. Along the way, it can assess any change in behaviour to adapt the journey and personalise the interaction.

However, in the rush to deliver these personalised experiences, be careful to avoid data overload. Having too much data can lead to “analysis paralysis”. This is why choosing the right AI tools, and the best tech partners, can help you wade through the resources you have, taking over from the grunt work that realistically many of us don’t have time to do.

Consider using ‘composable content’

For personalisation to scale to succeed, the design and publishing of content needs to be thought of in modular forms, or component parts. Think of them as almost like Lego bricks, where you have all the necessary elements to mould the right message for that person, in the right channel.

For example, imagine a seasonal email campaign going out to 100,000 customers, all at different stages of the life cycle, with different expectations and needs. To personalise the experience for these customers, we need to think about how 10 different subject lines can combine with 100 different hero offers, shaped by 100 different images, all with the purpose of driving sales and growing revenue.

Only in this mindset will marketers be able to serve up the right message, offer, or content to customers. This strategy also means that businesses will be much more capable of meeting customers where they are, to deliver personalised experiences that meet their expectations.

Personalisation at scale may sound like a Sisyphean task. But the tools to effectively implement it are most likely within your reach right now. Dig into your data, choose the right tools, implement a design system, and break down your business’ internal silos to discover just how much further you can power your personalisation because this is how you build brand experiences.

At the end of the day, your customers will thank you for it.