What is Machine Learning? Exploring Its Impact on Customer Experience

How Machine Learning Works

Machine learning algorithms have stemmed from predictive analytics and use data to improve behaviors and identify patterns, without being explicitly programmed to do so. It's a common feature in artificial intelligence (AI) solutions that need to analyze large data sets and accurately predict outcomes based on that data.

Though AI is the broad science that mimics human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Machine learning models today can independently adapt to new data as they're exposed to it and, over time, learn from previous computations to produce decisions and insights that are reliable and repeatable.

In regard to customer service, machine learning has played an integral role in business to consumer interactions. It's the driving reason why the page you see when you log on to Amazon.com is vastly different from the one your mom, neighbor or co-worker sees. Thanks to machine learning, advertising, product recommendations, and sales promotions are all tailored to a specific consumer's unique profile, which is based on their historical browsing and buying behaviors.

As digital touchpoints increase and consumer interactions increasingly move to digital platforms, business organizations will become rapid adopters of machine learning. As O'Reilly Media reports, 49% of enterprise organizations are looking into deploying machine learning into their organization, 36% claim to be early adopters, and 15% define themselves as "sophisticated users."

By harnessing machine learning, businesses can transform the ways in which consumers engage with their store or use their service. Here are three ways that machine learning improves the customer experience.

Personalized Experiences

Personalizing the customer experience is no longer just a tactical decision for marketing teams; it is actually expected and preferred by today's consumers. Recent research from marketing company Epsilon found that 80% of consumers are more likely to do business with a company if it offers personalized experiences and 90% find personalization appealing.

Fortunately, with machine learning capabilities, businesses can utilize customer data to deliver a unique experience to each customer. By gathering information like engagement, click-through rates, search patterns, purchase history, past queries, or help tickets, machine learning models can predict customers' wants and needs, sometimes before they even know themselves.

Personalization can be utilized in email campaigns, loyalty programs, a customized home page, and even in brick-and-mortar sales. Organizations that have embraced personalization in their marketing strategies have found that their efforts have paid off. In a survey of over 200 digital marketers, 96 % praised the ability of personalization to advance customer relationships and 61 % said that personalization has helped them achieve a superior customer service.

Convenient Service

It's not a surprise for businesses to learn that customers expect a convenient and seamless experience wherever they choose to do business. For many consumers, the addition of voice assistants, like Amazon Echo or Google Home, as a retail touchpoint has completely changed the ways in which they participate in the purchasing funnel.

By the end of 2018, it was predicted that 50% of all consumers owned a voice assistant, and of those who did, 30% use their device for shopping and ordering items online. Machine learning is widely used across voice assistants, predominantly for learning the ways in which a given user speaks, understanding the conversation without error, and anticipating the user's needs.

Specifically, machine learning techniques have become a vital component of Amazon's Alexa, especially in reducing its error rate, which was minimized by 25% in the last year. With the use of machine learning, a user can talk to Alexa in a far more natural way than they could a year ago, with a reasonable expectation that it will understand what they're saying.

The progress made in virtual assistant technology is very much reflected upon your brand. As they become easier to use and better equipped for business transactions, your customers will have a much better overall experience.

Accurate Inventory Levels

Not having adequate inventory in stores or warehouses, is more than a supply chain issue, it's a customer experience issue. When you don't have your goods well stocked when your customers want, they can quickly become aggravated and choose to do their business elsewhere.

When faced with a "stockout," the term used when an item is completely sold out, 58% of consumers will turn to a different retailer to purchase a similar good. Machine learning, however can help to avoid unsatisfactory stock levels. Machine learning's ability to consume and analyze vast amounts of data means it can accurately and quickly assess customer buying behavior, even using information such as weather forecasts and news feeds to predict necessary stock levels.

Aside from just ensuring that you have appropriate inventory levels, utilizing predictable insights into customer buying behaviors also helps to maintain accurate delivery times and avoid items on backorder. This way, when you promise your customers that their items will be delivered in 5-7 business days, those items are in stock and set to be delivered within that time frame.

Machine learning is an exciting prospect for businesses given its potential for improving customer satisfaction, streamlining processes, and driving business growth. If you haven't found a way to use machine learning in your current business operations, now is the time. Your customers will thank you for it.