Trends come and go. And sometimes come round again. Who would have thought puff-ball sleeves would be back in fashion, or there would be a revival in fringed jackets and vinyl raincoats?
This is also true in the advertising world, whether it be interactive video, messenger apps or the arrival of artificial intelligence and virtual reality. Brands will try every innovation at their disposal to capture consumer attention, and the rapid growth of digital technology gives them a whole new market to explore. But one thing that will never go out of fashion – just like the timeless little black dress – is the need to provide engaging, relevant ads that encourage consumers down the path to purchase.
Hyper-personalization uses data combined with machine learning (ML) technologies, to address this need. The idea of using data to personalize marketing messages is nothing new, but hyper-personalization allows brands to harness the power of data in a more advanced and effective way.
The ultimate personal shopper
Consumers might still head to a favorite store when they want to update their wardrobe or make over their home. But they also have a multitude of alternatives and are just as likely to browse online – the digital version of window shopping – often purchasing through their cell phone or computer. An estimated 1.66 billion people worldwide bought goods online last year, with global sales of $2.3 trillion. Brands can help their customers navigate this brave new shopping world by delivering highly personalized messages throughout each unique customer journey, aligned with the immediate and ever-changing needs of the individual.
Delivering the right message
Digital advertising often receives negative press, with ad blocking use in the US predicted to pass 30 percent this year. And this is no surprise if, thanks to poor marketing practices, consumers are constantly receiving irrelevant, unappealing or even irritating messaging. Brands need to take a more holistic approach to the consumer experience and journey, pinpointing when and where ads should be delivered for greatest effect and maximum value. This tactic requires a deeper understanding of the user that goes beyond basic demographics, previous purchases, and abandoned shopping carts.
Delivering the right message means understanding why a customer made a decision at a certain point in their journey and how each interaction can be adjusted to align with individual needs. Perhaps the customer is checking prices on a competitor site or has been put off by shipping charges? Offering a discount or free delivery at the optimum moment could encourage them to return. Brands can also use advanced techniques such as dynamic creative optimization (DCO), where behavioral, demographic, and contextual data is harnessed to select the ad creative that will have the most impact, and maximize in-the-moment relevance.
The most successful ML applications still put the personal into personalization. Despite the benefits of automation, the human element remains necessary to develop and adjust the algorithms on which ML operates and to determine the most appropriate data streams to feed it.
The next stage in machine learning
The use of ML is now commonplace as an efficient way of crunching data within the programmatic advertising ecosystem, allowing brands to make the leap from basic personalization to hyper-personalization. The next stage in this technological evolution is reinforced learning (RL), which provides even smarter analysis by considering all the variables the human brain would encounter when making digital purchase choices, including value and the positive or negative effects of previous decisions.
These advanced algorithms process information faster, more accurately, and at a much greater scale than a human could. And, unlike standard machine learning, they are not restricted by finite scenarios but can adapt on a case-by-case basis. So they turn complex data into actionable insights that enhance the customer experience and advance the purchase journey.
Hyper-personalization is more than just a fad for the current season and will improve brand reputation and drive sales in the long-term. By harnessing consumer data, brands can use ML and ultimately RL to deliver ads to the right person, at the right time, anticipating individual needs and improving the overall customer experience.