The one thing most businesses strive for is to make sure their customers are happy. In the past, the approach to this was fairly simple, engaging the customer through cheerful face-to-face interaction and seeing to all their needs personally. But as technology advances, more and more business dealings are taking place online, away from the face-to-face model that served so well for years. Many businesses are now turning to machine learning as the solution to improve customer interaction.
A Personalized Approach
If you’ve ever gone shopping in an online store such as Amazon or searched for something on Google, you’ve likely encountered tools that businesses use to engage the customer on a more personal level. Your actions on a website are often monitored, from what links you click, to what words you put in the search bar. This information is transmitted back to the business through data. Sorting through that data can be an extremely complex task, but machine learning makes that task easier.
Machine learning looks at the data and transforms the website into something geared more toward the individual customer. It’s all part of the goal that many businesses have—taking the personal face-to-face strategy and converting it into a digital format. One study from 2013 says that in the next three to five years, we could see 88% of all interactions with customers take place digitally. Another study shows that millennials want a more personalized approach from technology. A personalized website is a key to customer interaction, but there’s simply too much data for that to be managed by one worker or multiple employees, especially for a small business; machine learning is needed to make it happen.
Websites keep track of a customer’s purchase history, and through the collected data, they can make recommendations for other products to buy. Automated personalization also takes into account information about the shopper, and refines those recommendations and tailors them more specifically for the individual. It’s like having a salesperson with the customer the whole time, pointing out what products he or she thinks are right up the customer’s alley.
Personalization through machine learning can extend even further to life events. Some banks store the information customers share on their personal profiles and use that to offer services and products. One example would be if a customer updates his or her profile with information regarding a birth in the family. The bank can take that data and offer a baby savings account as well as information on mortgage rates in case the family is looking to buy a new home.
Some companies like Netflix have sophisticated machine learning programs that are used to predict what their users’ preferences are. Netflix goes one step further and more accurately narrows down the list of movies a customer might like to watch. While this level of customer interaction may not be of the traditional face-to-face variety, it is still satisfying an important element of the customer experience that many companies are just beginning to understand.
Another area where machine learning is already being used is in the rapidly growing industry of wearable devices. The amount of data collected from these devices can be massive in scope, and the predictive analytics used by businesses could be a big area of growth in the coming years. The data gathered from wearable devices could be used to engage the customer in even more personal ways while also allowing marketers to understand their actions and motivations more clearly. With enough input, machine learning can eventually develop a profile, customizing an approach for future interactions.
With businesses collecting enormous amounts of data, the information has to be readily available. Many companies are turning to flash storage systems as the means to store and analyze all that data. With flash storage, data can be analyzed in real time, providing quick and efficient information utilized by machine learning to provide an optimized customer experience. The more data that can be analyzed, the more accurate a program’s predictive abilities will be, leading to more satisfied and loyal customers.
We’ve only reached the tip of the iceberg for what machine learning can do to improve a business’s interactions with its customers. As technology continues to improve, machine learning’s uses and applications will be better understood, and online business experiences will only become more personal and fulfilling.