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Can machine learning improve customer service?

How To Apply Deep Learning To Improve Customer Service

How much can we expect a machine to learn from a human?

Is it realistic to believe a machine is capable of adopting human communication, actions, and responses to the point of replicating human behavior?

It’s not crazy to think entire businesses may eventually be automated and able to adapt to market changes based on how well machines can learn from their human instructors.

How To Apply Deep LearningThink about the last time you called your cell phone carrier or Internet service provider to talk about your service. You probably had to use an automated voice answering system before you eventually spoke to a human. Many automated answering systems aren’t very intuitive. In fact, you probably have more than a few memories of yelling into the phone or speaking like a robot before you either finally reached the person you wanted to or hung up in frustration.

A recent Forrester Research study shows almost 80 percent of U.S. adults choose “valuing a customer’s time” as the most important sign of good customer service. It’s hard to feel like your time is being valued if you’re becoming more frustrated with every attempt. For every misroute or repeated voice command the customer experiences, each instance can erode a caller’s trust in your company’s service.

What is Machine Learning, or Deep Learning?

So, what exactly is machine learning? Global management and consulting firm McKinsey & Company has an excellent definition of machine learning:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

Instead of being programmed to do a certain task, computers can be fed a series of information. The systems will detect whatever data is entered and will match the new data with the data set already in its memory. For instance, rather than teaching a computer how to draw a dog, programmers can load millions of pictures of dogs into the computer’s database so it will recognize any dog or sketch that resembles a dog.

So, why is this important?

Machine learning builds mathematical and logical algorithms inside a computer’s processing system. This allows the computer to learn, adapt, and respond based on the information it receives.

You may wonder, “Is machine learning really that important?” It is if you can use machines to enhance your client’s care and provide admin support for more mundane tasks, like resetting passwords, collecting contact information, such as email addresses and phone numbers, and even provide sales support. Having a reliable machine-learning system in place frees up live customer service agents to handle more complex conversations with customers or revenue-generating activities, like closing a sale. With 75 percent of people preferring to solve a tech problem on their own, the value of having an automated system that learns what people truly need in the moment is priceless.

This is only made possible if the machine-learning experience is efficient, nimble, and most importantly, hyper-accurate. A Harvard Business Review study article states that businesses incorporating machine-learning processes and systems into their everyday operations are experiencing both top line and bottom line performance increases. Study author H. James Wilson wrote, “Powerful machine-learning algorithms that adapt through experience and evolve in intelligence with exposure to data are driving changes in businesses that would have been impossible to imagine just five years ago.”

Machine Learning Helps Businesses Handle High Volumes of Clients

Machine learning allows businesses to engage with a high volume of clients on a daily basis. Each interaction creates its own set of data specific to both the client and the customer service process itself. Machine-learning systems can curate the data collected from each interaction and adjust the algorithms for even better accuracy. Now, does continual training of a computer lead to ongoing costs? Yes, but think of how much time is already being invested in a customer service personnel base that experiences high turnover, low expectations, and a stigma of being one of the least fulfilling jobs in almost every industry.

Imagine if a customer service machine-learning process can also use predictive analytics to suggest a repeated issue that the caller may be calling about again. Imagine if a customer starts a phone call with Tech Support and receives a personalized response based on what the system already knows about their previous interactions. How do you think the customer will feel about the company? It’s a faster, more accurate, and friendlier process that no longer feels cold and uninformed. Integrating a Natural Language Processing (NLP) system may even make the customer service experience one of the easiest ways for a customer to feel appreciated and heard by your company.

As with many tech sectors, machine-learning startups are the ones with the freedom to explore some of the newer frontiers. Google spends billions of dollars acquiring fledgling startups, some of which turn into headline-snatching mistakes, like acquiring Nest’s smart home technology valued at $3.2 billion but riddled with bugs. Of course, many of Google’s acquisitions are massive successes. Is acquiring scores of wild-card startups expensive and unpredictable? Yes, but in the case of Google, money is never the issue and when they hit paydirt, the windfall may be historic. One of the biggest winning acquisitions made by Google is their $500 million purchase of DeepMind, a London-based artificial intelligence startup whose acquisition signaled the beginning of AI as a major tech sector.

DeepMind has already helped Google with energy efficiency. Google operates data centers around the world and put DeepMind’s data and machine learning capabilities to use improving power usage efficiency, or PUE as it’s also called, in all its data centers. DeepMind adjusted the computer usage patterns and reduced Google’s power usage by several percentage points. That may not sound like a lot, but think about this: Google used 4,402,836 MWh of electricity in 2014, roughly the same amount of energy used by 366,903 U.S. family homes in one year. Reducing power usage by one percent every year will translate to hundreds of millions of dollars saved in energy costs… and all because a machine learned how to use data to make changes. Using DeepMind to save energy costs will pay for the acquisition of DeepMind, but that’s just the tip of the iceberg.

In fact, Google had probably some of the most efficient structures in place for its data centers, and DeepMind still improved on their design. In spite of excellent over-all efficiency, Google had inefficient cooling systems in place before integrating DeepMind’s AI learning. Imagine how much energy, time, and most importantly, money could be recouped or saved by implementing machine learning for everyday processes. Retail giant Amazon is also experimenting with machine learning for fraud detection, website marketplace optimization, and even packaging and shipping processes. Simply restructuring a conveyer belt by a matter of feet could save Amazon tens of thousands of dollars every year in wasted energy.

Microsoft is rumored to be building a machine learning tool suite called Open Mind Studio. The intent is to integrate Microsoft’s machine-learning technology across its product spectrum, including Outlook, Skype, HoloLens, and search engine Bing. At the core of Microsoft’s machine-learning efforts is its Cortana Intelligence Suite combined with its Azure Machine Learning Studio that can be used by businesses and developers for creating, testing, and integrating predictive analytics with user data. The more tech leaders expand the reach of machine learning, the greater the potential for businesses to enjoy the possibilities.

Over a dozen European banks have replaced their outdated statistical-modeling systems with machine learning. The results are eye-opening in some instances:

  • 10 percent increase in new product sales.
  • 20 percent saving in capital expenditures.
  • 20 percent increase in cash collections.
  • And, 20 percent decline in churn.

Part of the machine learning systems initiated by the banks are microtargeted models that can accurately predict which patrons will default on bank loans or cancel service with a built-in intervention process. Machines are now helping save humans from their own spending habits by predicting failure and intervening before it becomes a reality.

This article is an excerpt from NEO founder Jesse Morris’ new book Data and the World of Today: The Reality of Today that will Impact your Business Tomorrow. Purchase your copy via Amazon.com.

Machine Learning and Google: Match Made In Heaven

How Will Machine Learning Affect Business?

What were you doing in November 2016? If I were to make a guess, you were probably getting ready for the holidays. It was around that same time Google announced one of the most under-the-radar announcements regarding machine learning.

Google Translation & Machine Learning

On November 22nd, 2016 Google published an articled entitled Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System. Just two months earlier, Google announced Google Translate’s transition from phrase-based translation to a new system called Google Neural Machine Translation (GNMT).

How Will Machine Learning Affect BusinessYou might wonder what’s wrong with phrase-based translation. It’s the same approach you used in freshman Beginner Spanish memorizing phrases in Spanish to hold broken conversations with your classmates. You also probably used phrase-based translation during your spring break trip to the Alps while you awkwardly fumbled through your new English-French dictionary to hit on that one girl at the Louvre.

There’s one problem: phrase-based translation is effective, but clumsy and stilted to use. It doesn’t provide the nuance of language any of us incorporate throughout our everyday conversations. Phrase-based translation doesn’t capture the natural flow of communication used in every language around the world today.

Massive Upgrades In Online Translation!

The GNMT platform serves as a complete end-to-end learning framework. The challenge facing Google is that Google Translate currently translates over 140 billion words every day in 103 languages. With its previous phrase-based translation platform, Google Translate required a suite of network systems to accurately translate between any two languages. This necessity cost Google a significant amount of computation and financial resources. By connecting Google Translate to Google Neural Machine Translation (GNMT), it effectively received massive upgrade in translation horsepower.

Google believed the GNMT system would allow Google Translate to improve translation quality. In initial testing it was obvious the GNMT system’s ability to provide more accurate translation results was significant. This raised another dilemma: how do you scale a neural network as dynamic as GNMT to accommodate 103 unique languages worldwide?

Google elected to extend its previous GNMT system into a single system for language translation. There was no specific change in the existing GNMT system with one exception. Google included an extra “token” connected with the input sentence that allowed the system to select the desired output language for translation. Google also enabled “Zero-Shot Translation,” which occurs when language pairs with no previous inter-translation history in the GNMT system were triggered for translation between the two languages.

As you can see in the graph below, a multilingual GNMT system expands on the previous single GNMT system. In this example, the different language pairs share the same system for translation. This allows the system to transfer “translation knowledge” between language pairs to leverage the full potential of the multilingual model.

Photo Creds: https://1.bp.blogspot.com/-jwgtcgkgG2o/WDSBrwu9jeI/AAAAAAAABbM/2Eobq-N9_nYeAdeH-sB_NZGbhyoSWgReACLcB/s1600/image01.gif

Google was hopeful this zero-shot translation opportunity would be realized. Would the GNMT system be able to translate two languages never before encountered by the system in a translation pair? Google was amazed to learn the GNMT system created reasonable translation results between language pairs in a zero-shot translation scenario.

Google Learning On Its Own

How did the GNMT system know how to translate between languages with no previous experience in its system? It learned. In essence, the GNMT system appeared to created a new language to help translate more effectively… without human influence. The artificial intelligence capabilities inside the neural network of the GNMT system developed an interlingua of sorts to aid in translation accuracy.

Translation, pun intended, the GNMT system seemed to invent an internal language to increase translation efficacy without anyone telling it to in just a few short weeks.

GNMT Google Translate team member Mike Schuster (Google Brain Team), Melvin Johnson (Google Translate), and Nikhil Thorat (Google Brain Team) shared their perspective:

“Within a single group, we see a sentence with the same meaning but from three different languages. This means the network must be encoding something about the semantics of the sentence rather than simply memorizing phrase-to-phrase translations. We interpret this as a sign of existence of an interlingua in the network.” (Google Research Blog)

As of Google’s article published date, the multilingual GNMT system is enabled for all Google Translate users for ten of the 16 launched language pairs. This new approach is leading to better translation results with a simplified framework for helping people around the world understand each other better.

What the success of Google Neural Machine Translation (GNMT) means for neural networks

Does the GNMT system success signal a shift in neural networks? Technology experts have long believed in the power of neural networks to realize the potential of artificial intelligence. Neural networks are built in similar infrastructure of organic, biological brains. Their capacity to reason, deduct, implement, and even react is what drives technology leaders to explore new challenges to solve.

Neural networks, also known as artificial neural networks (ANN), thrive on recognizing patterns in data. IBM supercomputer Watson is already recognizing patterns in medical data, weather forecasts, legal matters, and education systems. I believe neural networks have the power to change every industry through artificial intelligence. The ability to think and respond in a human-like manner at an exceptionally high level of computation is opening new frontiers and solving countless problems.

What Does This Mean For Everyday Life?

In our immediate context, what does the Google Neural Machine Translation system’s success mean for your everyday life? It means our ever-diversifying world will better accommodate the melting pot of languages in every major city. I see a future where language groups can communicate more easily than ever. AI headsets connected wirelessly with Google Translate will allow us to automatically hear and understand any foreign language in our native language. Businesses will span language barriers and conduct business more efficiently than ever because neural networks will make translation easier than any other time.

The way the world communicates is changing because of Google Translate. Will we need to learn new languages? Maybe not. The potential to overcome millennia of miscommunication and communication barriers cannot be overstated. With the right technology, Google is committed to making sure this opportunity isn’t lost in translation.

 

Learn more from NEO about how machine learning, natural language processing, and other emerging technologies impact business on our main website.