In a given day, 4.5 billion Facebook likes are given, 4 billion YouTube videos are watched, and 3.5 billion Google searches are conducted. For marketers, these are all signals that a consumer is giving out. A marketer’s job is, at its core, to listen to the customer and change what you do in response to the lessons learned from listening, so these signals are vital. However, the sheer size of these numbers means that it is simply impossible to make sense of them all.
However, machine learning is allowing marketers to cut through the noise. Everything a consumer does online is a measurable event that can be linked together into a decision path. When every event is identified and aggregated, patterns start to emerge that reveal what consumers like, what they don’t like, what they will react to, and so forth. If we think of it in terms of marketers being in a room full of people all shouting what they want at the same time, machine learning essentially picks out each individual voice, divides the room full of shouting consumers into groups of people saying the same things, and sends them off to different rooms. A marketer is then able to walk between the rooms and better understand its inhabitant’s wants and needs. Machine learning essentially renders simplicity where there once was complexity. This means increased personalization.
It is marketer’s jobs to react to these findings, but machine learning also does much of this too. It determines the offers and kind of content that are likely to resonate with particular audience members, and when and how often these should be deployed. Automated marketing technology can then deliver relevant messaging to the consumers when and at a frequency that is appropriate, without human involvement, and run large-scale A/B testing to determine whether or not it works. This allows organizations to reach a far larger audience with a much wider range of experiences. With machine learning, marketing personalization and optimization easily scales to many millions of customers.
Many organizations are already realizing this potential. According to Marketo & Ascend’s ‘Marketing Automation Strategies for Sustaining Success’, on average 51% of companies are currently using Marketing automation, with more than half of B2B companies (58%) plan to adopt the technology. SoftwareAdvice’s ‘Marketing Automation Software BuyerView’, meanwhile, found that 91% of the most successful users agree that marketing automation is ‘very important’ to the overall success of their marketing across channels.
There are a number of challenges that still need to be overcome before we see widespread adoption of machine learning and automation in marketing in which people are not needed to oversee the process. However, understanding customers and responding to these insights is increasingly being automated and will undoubtedly be even more so in the future. The question is where does this leave marketers?
It is naive in the extreme to think that marketing technology equipped with the latest AI can never replace marketers’ experience and instinct, or that companies will keep marketers on when there are cheaper and more effective machines that can do the job. There are, however, two vital parts of the process that will likely never be automated: creativity and content. Marketers are hitting more people with messaging, and they are doing so with a wider variety of tailored content. While machines are proving adept at understanding habits and automatically sending messages, they cannot create content. The biggest challenge facing marketers is creating and finding enough relevant content to fully exploit all of these new technologies. This is a challenge for the many marketers for whom copy is a secondary concern, and in the coming years it will become a far more important skill than it currently is.
AI is also still not able to come up with radically new ideas. For the foreseeable future at least, machine learning will elevate the role of marketer and relieve them of the day-to-day drudgery of repetitive menial tasks. To say that their role will become more strategic is not telling the full story, as what and when of an actual strategy will be largely determined by machines. It will, however, become far more creative. As Noam Chomsky once said, ‘Thinking is a human feature. Will AI someday really think? That’s like asking if submarines swim. If you call it swimming then robots will think, yes.’