Machine Learning Basics: What Marketing and eCommerce Managers Need to Know
In this digital age, “business as usual” is not sufficient; customers have come to expect their digital experience to be tuned to their needs. New techniques for meeting customer needs and expectations are required to leverage the digital channel more effectively.
Machine learning is emerging as a digital disruptor and an essential piece of technology to take advantage of the enormous amounts of customer data now available to Marketing and eCommerce managers—especially regarding their customers’ digital experiences.
Research shows that Marketing and eCommerce managers are struggling with how to identify, quantify, and prioritize customer experience issues in their digital channels. And while there is nothing simple about machine learning it’s important to understand machine learning enough to see how it applies in the world of digital customer experience.
What Is Machine Learning?
Machine learning is the science and engineering of making machines “learn.” That said, intelligent machines need to do more than just learn—they need to plan, act, understand, and reason. Collecting, organizing, cleansing, synthesizing, and even generating insights from large volumes of structured and unstructured data are now typically machine learning tasks. At its core, machine learning is predicated on interwoven algorithms that can manage massive volumes of complex data more effectively than individuals.
Using machine learning enables us to process deep volumes of data and adapt to new categories in real time. This scalable programmability is complex, but offers businesses a number of ways to improve the customer experience. For Marketing and eCommerce managers, machine learning should be viewed as a strategic tool for gleaning actionable insights out of big data.
How Machine Learning Applies to eCommerce Retailers
eCommerce companies are beginning to use machine learning to improve search results on their websites. In the same way that Google uses machine learning to suggest the most relevant results to searches, eCommerce sites are implementing this technology to improve product browsing experiences.
However, use cases are expanding beyond search experiences like these to identifying the myriad of opportunities for eliminating customer churn and optimizing opportunities to improve the customer experience more effectively, without the need for hard-to-find resources like data scientists.
While these are just a couple of different ways machine learning can impact online businesses, the potential use cases are virtually unlimited. According to McKinsey, having the right people in place to translate machine learning insights into actual business decisions is the key.
Machine Learning and Customer Experience: How Marketing and eCommerce Managers Can Make the Two Meet
In a recent study we conducted, we found that 85% of organizations have trouble understanding why customers may not be converting on their website. The promise of machine learning is that we can now identify previously undiscovered revenue opportunities and mitigate friction in the digital channel much more efficiently.
At UserReplay, we have recognized the need to help Marketing and eCommerce managers capitalize on machine learning and we have made significant inroads into using machine learning for customer experience opportunity improvement. Stay tuned for more information about how the next generation of our customer experience analytics solutions are using machine learning.