Artificial intelligence has been around for quite a while—research on it started in the 1950s, in fact. But it is only now that it is really moving beyond the pages of science fiction, and into the realms of not just reality, but usefulness. In part, this is because of recent improvements in analytics, computing capacity, and algorithm development. These together make machine learning and cognitive computing, the two main components of artificial intelligence, much more accessible and useful.
There has been a lot of excitement in particular around how artificial intelligence is likely to improve customer experience. Cognitive computing, for example, uses analytics to answer questions. It is already being used to manage fast responses to customer queries to service centres. Computers create an initial response and direct the query appropriately. It’s fast and effective. Machine learning is enabling improved planning of marketing campaigns, based on customer segmentation and preferences.
Perhaps most excitingly, the emergence of deep learning, the third component of artificial intelligence, is starting to benefit marketing, and in particular, more complex tasks. It is early days yet, but if a computer can be ‘taught’ to drive a car, there is little reason why it can’t learn to carry out marketing tasks like mapping customer journeys.
The idea of artificial intelligence being used in marketing, and especially to improve customer experience, is very exciting. But in reality, artificial intelligence is totally dependent on data. If we’re honest, we know that for most marketing teams, including here in the Nordics, the journey towards data-driven marketing is only just beginning.
Marketing today is both easier and more difficult than it has ever been before. We have access to huge amounts of data, but how should it be used most effectively? Marketing is not, perhaps, seen as a natural partner for mathematical modelling and analytics. But to succeed in a world that is becoming increasingly data-driven whether we like it or not, marketers need to understand and use data to answer questions about their customers’ preferences and problems. Fortunately, analytics tools are becoming increasingly user-friendly. What is required now is more a willingness to try things out, to see what value can be created, rather than a degree in mathematics or computing.
More complexity, increased reward
The Internet of Things (IoT) is one of the key drivers of the huge increase in data volume in recent years. With estimates suggesting that the number of connected items could increase exponentially over the next few years, the data volume is likely to get ever-larger, and much of it requiring real-time analysis.
But if the IoT will add complexity and data volume, it also has potential to increase the rewards. It is already, for example, being used to improve customer retail experience, through the use of improved personalisation and tailored interactions and offers. The question, as we have noted before, is not so much can value be generated, but how many of those who could benefit are even aware of the potential. Apparently, somebody is. Read the e-book Internet of Things: Visualise the Impact to have a deeper insight on that.
Benchmarking to assess progress
With so many aspects of marketing being influenced by digital customer experiences, how does a marketing team assess progress and seek guidance on aligning priorities? SAS believes benchmarking can help. If you're ready to take stock of your digital marketing approach, we are ready to help you develop a game plan to strengthen your marketing confidence. Take the assessment and get your score.