Digital marketers are dealing with a constantly changing customer and huge amounts of data. Machine learning can help us learn from this data.
The challenges confronting today’s marketers have evolved. They are now faced with a constantly changing customer, a customer whose habits, tastes and expectations have evolved along with technology in recent years, and can be complex to manage. With the modern customer also sharing huge amounts of data about their activity, in the coming years, this data is set to be pored over by machines capable of recognizing patterns and learning from them. In fact, machine learning has the potential to alter digital marketing and data management as we know it.
It is important to note that machine learning is not the same as artificial intelligence (AI). When we talk about machine learning, we mean a machine that accepts data and then continually learns from it based on the flow of data that it receives over time. AI is different: it accepts data and can draw conclusions independently without human direction. AI would be able to give you world peace if you ask it to find ways to get it. In Digital Marketing we want to do less complex jobs with huge junks of data but still be able to learn from it without further human interaction.
The modern customer is always online. This is a big opportunity: marketers can now get their message across to the customer via a variety of media and across channels, and that message can now reach customers globally in seconds. However, the enormous scale of data whizzing around online via millions of swipes on smartphone screens means that humans are starting to need help processing it all. Some estimates suggest that the amount of data globally doubles every two years. This is where machines apply: machine learning algorithms can make sense of huge quantities of data and can pick up on patterns that humans might miss.
We see examples of machine learning everyday. If you incorrectly type a search into Google and are asked if you meant another word, this is based on Google's machine learning algorithms. When you purchase an item from Amazon, algorithms enable learning based on your purchasing data, and you'll receive recommendations or offers the next time you log-on. Amazon can be considered the benchmark in terms of the sheer volume of purchase data they process every day.
This is one of the key benefits of machine learning: the ability to personalise consumer experiences. In recent years, customers have come to expect a degree of personalisation in their shopping experiences and can be deterred from a brand if the offers they receive are too generic. The same applies to digital ads. If a machine can continually learn about the preferences of a customer over time, the offers a customer is presented with can be made more relevant to their needs and behaviours. This will be crucial for digital marketers in the future and will also be a central piece of budget allocation and distribution for digital ad spend.
Machine learning should improve over time: the more data the machine receives about a customer, the clearer the picture of that person's habits and interests becomes. Machine learning can also offer greater efficiency. It can help companies raise profit, lower costs and increase ROI. It could help chatbots to replace human customer service personnel. HR can be transformed via machine learning, with recruiters using public data on social networks and identifying high-performers. Then there's predictive pricing - Uber already uses machine learning in order to predict how much a client is willing to pay for a particular ride.
One of the challenges of this transformation in digital marketing will lie in deciding when to utilise machines, and when we’ll need a human touch. Machines are fast, they learn over time, they can make decisions and take on responsibility. And, in theory, they shouldn't make mistakes. Machines can deal with high frequencies and volumes of data that a human simply cannot - but no worries: we are not going to be wiped out by machines, as there is something essentially missing in any algorithm: feelings.
Where could we use machine learning in digital marketing? There are a number of opportunities: it could help manage and monitor social media, create highly personalised content, lead management, data transparency and campaign management, to name a few.
Applying machine learning to digital marketing will open possibilities for making in-the-moment content decisions for specific customers as well as for real-time predictions like Uber’s aforementioned pricing approach. Digital marketers could also use machine learning for real-time data ingestion by incorporating performance data as it streams in. This performance data could then be used for real-time model training. In addition, as machine learning improves over time, continuous algorithm evaluation and selection allows us to be able to automatically adapt to data points, the brand, the content and the unique patterns that exist among them. Essentially, machine learning will allow us to listen to our customers continuously, eliminating the need for A/B testing.
Given these benefits, recent studies have indicated that machines are set to take over human jobs in the next 15-20 years. But digital marketers need not be fearful for their jobs just yet.
At its heart, marketing is still about feelings and making a connection with the customer. It's also about daring to do things differently, taking a risk and thinking creatively. Machines aren’t yet capable of taking risks, demonstrating a remaining need for human qualities. In addition, machines are likely to take on a lot of the heavy lifting around organising data, searching for patterns and concentrating on more boring, repetitive tasks, allowing humans to concentrate on creative problem-solving and human interactions. According to the World Economic Forum’s Future of Jobs report, in the list of top ten skills, creativity is set to jump from 10th position to 3rd position from 2015 to 2020.
In coming years, delivering personalised, optimised experiences for customers will become increasingly important. Customers leave clues and patterns wherever they shop or interact online, and it's up to digital marketers to take advantage of that. Machine Learning provides us with an opportunity to leverage valuable data to truly understand our customers and to continue to develop that understanding over time.