Machine Learning is not new but as the processing power of modern computer has significantly increased this has completely boosted the machine-learning applications in various sectors. Algorithms that never had been implemented due to lack of computing power are flourishing in customer domain.
Though the fundamental concept remains the same, machine-learning is now widely implemented, far more sophisticated, efficient and easily deployable – and the potential it offers to revolutionize customer experience.
Machine learning revolutionize the way which businesses interact with customers. Forget product recommendations as we know them today, this takes us far beyond that, into the realms of much more hyper-personal and sophisticated experiences.
Identify, think… go!
Not only can machine learning automatically process vast quantities of data to understand customer behavior and identify where the opportunity lies, we can now action these findings as well.
Machine learning might identify that a Swiss travel company has high traffic from the UK, with people looking at ski trips near the end of the season, but lower conversion than expected. Concerns from this segment about the level of snow at the end of the season could be addressed by showing the local weather report or a snow tracker.
Alternatively, an online retailer can identify that people are browsing through party dresses on a Thursday or Friday night, but not clicking further to make a purchase.
This is important because we live in a world where delivering customers the content they are looking for in the very first couple of seconds is critical. If you don’t, the chances are they are going to get bored, distracted, and leave. Tinder’s ‘swipe-right, swipe-left’ mentality is driving change across all sectors.
As always, millennials are the ones driving demand for hyper-personal sophisticated experiences. This younger generation values experiences over commodities, and are driving a change in the way brands generally interact with consumers – also known as the experience economy.
The adoption of machine learning is no longer a “nice to have”.
Embracing programmatic techniques
To get a sense of the potential impact of machine learning on customer experience, you only need to look as far as the arrival of programmatic advertising a few years ago.
This completely revolutionized how ads are bought and targeted online, harnessing data to not only automate a lot of the “grunt” work, but also to make much smarter, more strategic decisions about where the opportunities for brands lie. The use of programmatic techniques enables campaign performance improvements of between 30 per cent and 50 per cent, according to some studies.
In the same way, harnessing machine learning for programmatic customer experience enables marketers to identify clear customer segments and target them in ways that they know will resonate.
The new marketer
Instead of having to manually identify customer groups and which offers a valuable opportunity, these can now be automatically identified and prioritised using a combination of predictive analytics and machine learning technology. This technology can then feed these opportunities back, listed according to which ones offer the largest untapped revenue opportunity.
The marketer now starts from a position of knowing who their customers are and what will excite them, empowering them to focus their efforts on meeting their needs and exceeding their expectations at every interaction.
Machine-learning fundamentally provides them with empirical empathy: the marketer can measure what their customers are doing and feeling, who they are, what they want, and – perhaps most importantly – how to tailor their online experience.
This will change the face of digital commerce in the next decade. The businesses that will win will be those that are obsessed with the customer, that have both great products and programmatic experiences at their core, and understand how to deliver the very best experience for each customer segment.