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Machine Learning Applications for Customer Support

Even though machine learning has been around for nearly half a century, successfully leveraging the technology to help businesses become more efficient has transformed customer support in the last decade. To remain competitive, leaders must recognize that a generalized solution is not enough, and understand how to select the right customized AI solution from a spectrum of choices.

Finding your own machine learning solutions

Chances are that your company is sifting through lots of data when it comes to customer support. You may have considered hiring more people to manually wade through it all. The opportunity to leverage artificial intelligence (AI) to help better serve your customers is looking more and more attractive. Not only does this solution reduce the staff it requires to serve customers, intelligent automation can also help to align brand messaging and voice, guaranteeing a consistent experience for your customers. In addition, smart machines can assist with the most tedious and repetitive work and allow your people to be creative, empathetic, and perform the high-level work they were paid to do. It’s an assistive tool for our minds and our workflows. Leveraged appropriately, it helps deliver better service to your customers, and frees up your people to do what they do best.

So, which type of AI solution is best for your customer-focused business? There is no one-size-fits-all answer. Instead, it’s essential that you choose the right technology for your specific application. You must know when to apply a chatbot versus machine learning systems, or when to rely on broad data sets versus data unique your own business.

Let’s dive deeper into ML and AI solutions so you can make the best choice for your customer support needs.


The “chatbot” or “chatterbot” phenomenon—perhaps one of the more recognizable forms of AI—has received a lot of attention recently. Chatbots are computer programs which conduct conversations via textual or auditory dialog. They are being recognized in both the film and entertainment industry—think movies like Her and Ex Machina—and also in the real world. Chatbots are sometimes used in phone or internet-based dialog systems for information acquisition or customer service scenarios.

Chatbots are powerful assistive tools. Yet, without proper monitoring, chatbots can do real harm to a company’s communications or brand image. Consider when Microsoft’s Tay recently went haywire after being turned loose on Twitter.

The limitations of such universal solutions are evident in contrast to the success of customized machine learning applications. Even with the best technology, a chatbot should not be trusted as the answer to all customer questions and every incoming problem. A company must be in control of the information the bot can access, and monitor what happens when it does.  In short, despite the hype, even the most sophisticated effectively require extensive human generated rules to operate safely and effectively. See, for example, the commentary around Facebook’s M intelligent assistant.  This requires a machine learning application that uses a consistent discovery process to develop new content along with leveraging the existing library -- all within the context of your unique brand voice and messaging.

Public data-fueled solutions

One key challenge of trying to apply a generalized AI solution from companies like Google, Facebook, or Apple is the basic assumption that such a system has access to the vast amount of data available on the web. Some of these are intelligent personal assistants like Google Now and Apple’s Siri, which both perform tasks or offer services based on user input or awareness of online information (like geographical locations, weather, stock numbers, or retail prices).

These types of ML applications have access to a wealth of data, and in this context they have the potential to be incredibly useful.  However, as noted above, there still exists a set of rules under the hood that gives the intelligence a structure. People—not machines—decide which questions are valid to be asked. Remove that structure, and anything can happen—like it did with Tay.

More importantly, Siri and Google Now work because they are always learning from millions of people asking valid questions that can be answered with data that is widely available. This type of solution works for very generalized purposes using large pools of public data. However, one should proceed with caution when trying to apply these tools to the smaller scale, unique, and proprietary questions facing an individual business.

Intelligence powered by proprietary data

It’s safe to assume that the questions customers are asking about your products or services are specific to your business and not the same as the questions they would ask about someone else’s product. LikeAnswerIQ, the information needed to answer those questions about your product or service is different than the information another company would use, because it’s specific to your company. In that regard, trying to apply a generalized solution to a specific problem using someone else’s data—even if there is an infinite amount of it—is destined to fail.

In contrast, machine learning applications focused on proprietary data offer a customizable, focused assistive tool. When machines are allowed to interact with and duplicate decisions your people are already making based on your company’s past data, the resulting feedback is more focused and structured.

Request a AnswerIQ Support demo.

It’s true that ML can harness enormous amounts of data and make important progress in the world of customer support. Yet, the algorithms and technology involved only work to the extent of the data that powers them. Google-scale data works for Google Now. But, in a more narrow, specific business environment like customer support, generic data sets are more likely to fail. In order to create valuable processes and responses, machines must learn from the right information from the start. Then, your customers are free to jump in and build on those processes with their own creative, high-level thinking.

After all, your company has its own voice, its own data, and its own business goals. Keep all those things in mind in order to get the most value out of your chosen solution.

The AnswerIQ advantage

The advantage of AnswerIQ intelligence over a chatbot or large-scale solution is that the applications are designed to offer proprietary responses based on a company’s personal data. You don’t need Google-scale data in order for ML to work for you. If you have good, clean, segmented data based on your own company’s historic responses, a customized machine can learn from it and move you toward greater customer satisfaction and retention.

In addition, if your organization doesn’t have the team or capacity to build a content library for smart automation purposes, AnswerIQ Support can build that system by leveraging what you currently have, while discovering new pieces to fill in gaps, working towards a comprehensive content library aligned with the unique brand image and voice of your company.

For more information about how machine learning can impact your bottom line, download our ThredUp Case Study.

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Topics: Machine Learning, Customer Success, Customer Support

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