Last year researcher Janelle Shane trained a neural network to generate and name new paint colors by inputting 7,700 examples from Sherwin Williams. It came up with some interesting names. I don’t think anybody will be painting their living room Stummy Beige, Stanky Bean, or Turdly anytime soon.
That’s not what this blog is about, but it probably took you about eight seconds to read that paragraph. In today’s busy, digital world, eight seconds is the average attention span of your customer. For the record, that’s less than a goldfish. You have eight seconds to help them, eight seconds to suggest a product, or eight seconds to provide quality customer service. Humans can’t do that, but artificial intelligence (AI) can. That’s what this blog is about.
NLP for Better Customer Service
While artificial intelligence may have failed at naming paint colors, it is poised to change customer experience as we know it, and in fact already has. AI breakthroughs such as Natural Language Processing (NLP) are allowing systems to understand consumers in their own language on their chosen channel. It may seem counterintuitive that shifting interactions away from human agents is making people feel heard and personally catered to, but it’s AI’s capabilities that are making quick, personalized services possible.
One of the most common uses of NLP is the chatbot. Chatbots provide huge benefits to both businesses and consumers. Individuals get immediate, efficient service in a human-like engagement while the company saves time and money. Unlike actual human agents, AI programs are able to constantly gather and process information, so a chatbot can immediately suggest products to a customer based on that person’s past activity and preferences.
Sifting Big Data for Customer Insights
People can listen to an individual customer’s concerns or complaints. They can even segment them into different categories. What they can’t do, but AI can, is decipher this meaning at scale and across many different formats. Call center calls may be manually categorized, but who is categorizing the tweets, Facebook comments, and chat messages? AI can do that.
Companies are using AI to leverage their unstructured data. If a company has already decided on categories for analysis, they are likely to miss emerging trends and opportunities. AI is simply better at processing huge amounts of data from different sources, and it can often find patterns where humans can’t.
AI’s ability to find trends allows businesses to anticipate a customer’s needs or actions before they happen. Catherine Havasi, natural language processing expert and founder of Luminoso, shared some great examples of how AI has solved a lot of the customer- and employee-issues that humans have gotten wrong in her recent interview with TechEmergence.
One example was of a telecommunications company that was trying to lower their employee turnover. In reviewing employee feedback, they found a lot of complaints about the company’s food quality. They made changes based on this feedback and then found that these changes made no difference whatsoever. Had they used AI, they would have quickly been able to see that while many people were complaining about the food, these were not the people who were leaving the company. AI could have saved them from spending time and money on a problem that didn’t need fixing.
Havasi also shares a story about an auto maker trying to improve its customer service. After deploying AI, it found a group of customers complaining about a dog smell in their cars. It also found another group of customers complaining of having dew in their cars in the morning. The AI system quickly figured out that the dog smell complaints and the dew problem were in the same specific car model, and by putting the two sets of data together the company was able to quickly fix the issue.
Companies have a lot to gain from the customer experience improvements they can make using AI, and customers are ready for the personalization that AI has to offer. In Oracle’s recent Retail in 4D report, 50 percent of respondents said that they would be attracted to personalized offers that were based on their past purchases or real-time browsing data.
While only 32% of financial service executives report using AI technologies, banks do understand the power of AI. In Accenture’s Banking Technology Vision 2017 report, it found that 79% of those in the banking industry agree that AI is going to revolutionize the way they get information from and interact with their customers. Banks seeking to improve the customer experience have AI in their future. Just don’t let it pick any paint colors.
I enjoy talking about AI and the work that Katabat is doing in this field. What applications offer the most exciting possibilities for your business? Contact me at email@example.com to chat about how Katabat can help make them a reality.
Ye cofounded Katabat in 2006 and continues to enjoy creating technology solutions to solve business problems. Ye’s deep experience in artificial intelligence, banking and internet technologies have and continue to shape Katabat’s product development and evolution. Prior to Katabat, Ye worked for Bridgeforce and Ensuredmail, Inc. Ye received a BS and Master’s degree in Electrical and Electronics Engineering from Sichuan University. He also has his Master’s and Ph.D. in Computer Science from the University of Delaware. Ye is an avid technologist and has published multiple conference and journal articles in the fields of computer vision, pattern recognition, and artificial intelligence.
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