As machine learning algorithms are increasingly embedded in today's analytics applications, this offspring of artificial intelligence is grabbing headlines.
In reality, however, the technology isn’t as new as you might think. Its roots in academia date back to the 1950s. Machine learning algorithms have risen out of predictive analytics and use data to learn and improve behaviors and recognize patterns — without being explicitly programmed to do so. While the technology may not be new, its business application are.
To better understand the impact of machine learning, let’s look how it intersects with customer experience and where this combination may be headed.
How Machine Learning Works
Machine Learning relies on automation and analytical algorithms to detect patterns and derive insight from data to achieve specific goals.
Until recently, machine learning was used only by organizations that had large analytical teams. Now machine learning is becoming democratized and, as a result, is not only impacting every day analytical programs (e.g., analytical segmentation, modeling and optimization,) it is also being used to achieve specific analytical goals.
These goals, particularly when they support improving customer experience, translate into personalizing (or even individualizing) an offer for a product or service. Personalization involves increasing the speed, accuracy and context of interaction. For example, imagine a father who receives a stellar offer for a pair of noise-cancelling headphones on his mobile device shortly after purchasing an electric guitar and amplifier. That’s an offer that has the right speed (right after purchase), accuracy (mobile as his preferred channel) and context (knowing he may need the headphones to silence the noise coming from his teenage son’s room). This type of offer increases customer satisfaction as it is pertinent at the time of need. The end-result is a happier customer (this company understands me) and a happier brand (increased revenues.)
So, what are some practical applications we are seeing today? Below are a few examples of how machine learning is improving customer experience through next-best-offer personalization, customer behavior analytics with new data sources and analytical optimization. The examples are mainly from financial services organizations (banks and insurers), but similar use cases and results are being seen across other industries as well. View More
