4 Ways To Use Machine Learning To Improve the Customer Experience

Written By Ivan Serrano

machine learning customer experienceThese days, programmers find themselves working on projects that intersect with machine learning (ML) and artificial intelligence (AI) technology all the time.

Software developers deploy it as part of their quality assurance processes. Web developers build web apps that connect with ML services via the many cloud provider APIs now available. Machine Learning is even working its way into mobile app development, one new feature at a time.

However, some programmers tend to have a myopic vision of how ML can help them in their work. Although they see ML technology as a driver of new functionality or an extra hand in getting things done, what they don’t see is how ML can help them to build better products by improving the customer experience.

That lack of utilization is a pretty big deal. For proof, look no further than this recent survey of business professionals, which reveals that nearly half now call improving their business’s customer experience their No. 1 priority.

That means programmers who put in the work to learn about the various ways they can apply ML to improve the customer experience will be in an excellent position to capitalize on that business trend.

Without a doubt, current-generation ML can be programmers’ greatest customer experience asset. All they have to do is learn how to use it in the most effective ways. To help, here is a guide to four ways programmers can use machine learning to improve the customer experience for the products they create.

Provide 24-Hour Self-Service Support

One of the most common uses of ML in the world today is in the realm of customer service. In fact, it’s so common that most of us encounter it in one way or another every day. It powers modern iterations of integrated voice response (IVR) systems, on-site chatbots, and a plethora of self-help support systems.

Indeed, there are very good reasons that so many companies are using ML technology in this way. It’s what customers want. According to the Harvard Business Review, 81% of customers would rather solve a problem themselves than have to talk to a live support agent. And as any experienced software developer would agree, when that many customers are asking for something, it’s a good idea to give it to them.

There are benefits for programmers as well in using ML to provide customer support. ML-enabled customer support systems provide massive troves of data that can help identify product weaknesses that may not be apparent at first glance. It’s an excellent way to inform your development cycle and stay ahead of customer demands—even if customers aren’t being precise about what it is they want from you.

Analyze Customer Feedback

One of the key mechanisms that programmers use to gauge customer satisfaction is direct user feedback. Nonetheless, even a moderately successful product might generate such voluminous feedback that it’s all but impossible to distill it into useful information.

That’s where ML comes into play.

By training an ML model to look for specific kinds of data within large quantities of user feedback, it’s possible to mine it for real insights. One of the key ML approaches to this is to use sentiment analysis to look for less-than-obvious pain points that users might be struggling with while using a product.

From a software development perspective, this is particularly useful because rank-and-file users frequently lack the technical knowledge and vocabulary to identify problems they’re having. However, if you can correlate negative sentiment directed with a particular part of your product, you at least have somewhere to start.

Nowadays, it’s even possible to pair sentiment analysis up with natural language processing technology to create an analysis workflow for customer support phone calls. That means you can use the technique without even asking users to complete a survey or offer specific feedback.

That’s important, considering that users experiencing difficulty with a product won’t always take the time to offer formal feedback—they’ll simply ask for help and stop using your product if they don’t get it.

Enabling Product Personalization

machine learning customer experienceIn the age of always-on, always-connected technology, programmers are frequently locked into a continuous development cycle to keep their products relevant. Proper use of ML can somewhat alleviate that. By building ML-supported personalization into their products, programmers can keep them relevant and responsive to users without the need for a fast-paced refresh cycle.

In a way, this approach is a combination of sleight of hand and prudent design. It’s how massive companies like Netflix and Amazon provide a customer experience that seems to be ever-changing, even though they’re updating their platforms at a regular, measured pace. They harness vast troves of user data and use ML to analyze what users want in real time—and then give it to them.

In that way, both parties win: Users get a relevant experience at all times, while developers get the breathing room to make useful changes to their products rather than working just to keep up appearances.

Improving User Retention

Developing a piece of software (or a website or app) doesn’t happen overnight. It takes time, effort, and resources. And that means a key metric of success for any development project is how well it satisfies its audience and keeps them around after launch. That makes post-launch customer retention a mission-critical task for programmers and the businesses they serve.

This is another area where ML can make a major impact. By creating predictive models to measure and analyze user churn, it becomes possible to address retention problems before they harm a product’s ROI. From a developer’s perspective, this is yet another way to identify weaknesses in a product post-launch and act to resolve them.

This is especially important for developers working on software as a service (SaaS) products. Because SaaS has no replacement cycle like traditional software would, success is measured almost exclusively by user retention. In an environment where continuous development and feature evolution are a given, there are too many moving parts to leave to chance.

Fortunately, ML modeling offers just the right type of assistance developers need to spot issues on the horizon and deal with them before they cause a user exodus.

ML Technology Can Be the Key to Improved Customer Experience

At this point, it would be stating the obvious to point out that ML technology isn’t going anywhere. As it continues to improve, it’s going to give programmers tools and capabilities they can’t even imagine now. But that’s all the more reason to explore the many uses it already has—and to prepare for what’s to come.

Because there are already so many well-developed use cases for ML to improve the customer experience, that’s the perfect place for today’s programmers to start. In that way, they can make every product they create more useful, functional, and valuable. And doing that will help keep their customers and the users of their products satisfied.

That, in turn, makes them more marketable to their clients in the current business environment. With the customer experience taking center stage for so many businesses today, a programmer who can cater to a client’s needs will get more work, more often. And in the end, that’s just the type of experience that every programmer dreams about.