Over the last few years, marketers have become acutely aware of how the scope and responsibilities of their job have evolved. No longer simply responsible for serving ads to consumers across a handful of different touchpoints, they must now reach out using a whole multitude of channels, the number of which seems to increase daily.

More than this, brands today need to be able to take the unique hobbies, habits, behaviours and interests of consumers into account in order to target them with highly personalised communications, and this requires a major adjustment in marketing approach.

Of course, achieving personalisation at scale simply wasn’t possible when newspaper ads and direct mailers were among the only means of communication, and the same message was sent en masse to a huge audience. Now, however, our eyes are being opened to how technology can be used to deliver the level of personalisation today’s consumer expects.

The benefits of powerful personalisation are obvious: consumers are far more likely to engage with something if it’s written in a way they are familiar with, or mentions something they have an active interest in. If they feel they’re talking to a friend, rather than a brand, it will more often than not lead to a more meaningful, longer-lasting relationship.

The challenge, however, lies in being able to deliver this level of personalisation across all channels, whether that’s e-mail, social media, smartphone push notifications or something else entirely. Getting this right involves taking huge amounts of customer data and orchestrating it in intelligent ways, and this can be a real challenge, not least because each channel has its own set of unique rules and requirements.

In a bid to deliver effective, hyper-personalised communications across all channels, while simultaneously trying to simplify this process, marketers are increasingly turning to AI (Artificial Intelligence). Based on sophisticated algorithms and machine learning methods, AI is already a part of our everyday lives — it powers everything from the voice assistants on our smartphones to Facebook’s facial recognition feature — and this is now expanding rapidly into the marketing sector.

Because of the variables involved, AI is able to perform a much better job at converting vital customer data held by companies into actionable customer insights. In fact, machine learning algorithms work better with more data: the more information a company holds, the more complex techniques (i.e. deep learning) they can use to influence their customer journeys, which results in better outcomes. The algorithms can also be used to pick up on complex trends and patterns that might have otherwise gone unnoticed by the human eye, which can lead to an extremely valuable additional layer of personalisation that can further drive engagement and customer loyalty.

Many companies are now using AI-enabled data management solutions — whether they are DMPs (Data Management Platforms), CDPs (Customer Data Platforms) or capabilities of both.

However, AI still has limitations that need to be worked around. It still relies on human input to structure and standardise the data it uses, for example. Marketers cannot simply plug raw data into an AI-generated algorithm and expect it to do all the hard work — they need to consider how the data should be structured and how the algorithm can be made fast enough to predict things on a large scale, in real-time.

Ideally, AI needs to be implemented in the right way to deliver true personalisation. Recommendations are essential, but deep learning through industry models that are applied to the data and logic for specific organisations is what will enable marketers to activate relevant insights in a scalable manner.

Looking to the future of personalisation, AI will undoubtedly still have a huge role to play, but it will be utilised in more sophisticated and innovative ways. Take chatbots, for example. Powered by AI and designed to simulate conversation with human users, these computer programs could take a consumer’s personal interests and behaviours into account, within industry algorithms before relaying the most relevant results for a specific question.

The potential is easy to imagine. Let’s say a family is preparing to go on holiday, and one of them asks their Amazon Alexa AI assistant to buy an extra pair of swimming shorts. Alexa could then take this information, look at it alongside all the other information it holds, and then use it to make any further relevant purchase suggestions. Perhaps it then asks the family whether it also needs to buy any more sunscreen, or if they’d like to buy a travel guide for their holiday destination.

As consumers demands have changed in terms of how they want companies to communicate with them, so too do the requirements on online marketers. It could be argued that success is now closely linked to the level of personalisation that can be achieved, and so every effort must be made to make all communications as tailored and relevant as possible. AI technology will no doubt prove to be a major advantage in this quest, and its potential will surely evolve as time goes on.


By Tomas Salfischberger, CEO and co-founder at Relay42

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