The Top 6 Customer Service AI Trends to Watch
Over the last few years, artificial intelligence (AI) has gone from being a dated sci-fi movie by Steven Spielberg to an accessible technology we’re all pretty comfortable interfacing with, whether we know it or not. (I’m looking at you, Siri.) But one area where AI has potential that isn’t front of mind for many people is in the world of customer service.
In fact, customer service in AI has transformed from being something trendy to being an expectation, at least for the largest brands. If you run a customer service organization today, and you’re not using AI to better serve customers and cut costs, you’ve missed the boat.
Even as customer service AI technology is being integrated into all types of organizations, platforms and tools, the landscape of customer service AI is constantly shifting. Here are the top looming trends for AI in customer service, and how you can take advantage of them.
1. NLP for human-machine interaction
Natural language processing, or NLP, is arguably one of the most talked-about aspects of customer service AI today. A subset of machine learning, NLP takes the wide variety and inherent flaws built into human communication and translates them to computer-speak — and then back again. NLP is what allows you to talk to your phone, for instance, and for your phone to talk back.
This type of AI is particularly useful in enabling conversations between people and bots, like within live chat and in-app support tools. Even when messages contain typos, grammar mistakes, slang and other types of variances, NLP “learns” to understand customers in order to answer questions and route queries to the right bot, agent, or organization. More about intelligent routing in a minute, but first….
2. Translation technology
Another powerful upcoming use of NLP lies in the realm of language translation. There are over 7,000 languages in the world — over 30 used regularly in India alone — and until now, digital translation services have only catered to a handful of them.
But with Google’s recent advances in NLP, including the ability to map data from “data-rich” languages to “data-scarce” languages, it’s now going to be possible to use a single multilingual model to speak to customers across cultures and geographic boundaries.
This is an incredible advance for companies with global customers, for whom it’s simply impossible to scale a human workforce for every single time zone and language dialect.
3. Automated, intelligent issue classification
AI can also enable much more powerful and automatic issue classification by routing tickets based on category, agent specialty, and current agent ticket load. This agility is supported by the ability of AI engines to continuously learn based on agent feedback.
When combined with NLP, automated issue classification becomes much more sophisticated. The way most people communicate today — particularly with typed text formats — is short and not always sweet. AI algorithms that can classify language well with NLP can provide more accurate recommendations and next steps.
Using AI for issue classification makes for more accurate routing in the first place so that customers aren’t bounced around from agent to agent to agent. They get their issues resolved more quickly and feel “heard.”
4. Customizing customer experiences
Recommendation engines are nothing new. Since the early days of Netflix and Amazon, consumers have been used to being shown products based on other things they’ve bought, watched or looked at. But adding AI into this process creates much more accurate recommendation possibilities.
The old way was rudimentary and general: products were mapped together, and if a customer looked at or bought a product from a certain category, they’d automatically be shown another.
But with AI in the mix, recommendations get more explicit and contextual. AI analyzes data sets and adapts in real-time, so it can show products based on need or context rather than simple category filters. And AI learns over time, so recommendations get more and more specific and accurate for each individual customer.
5. Highly specific marketing
AI can help improve other aspects of customer experience and communication as well. Starbucks is a shining model of how AI can be used in email marketing, for instance. Using AI, the company has grown its email marketing strategy from 30 different types of email offers a week to over 400,000, each highly personalized to a particular subset of customers.
This hasn’t just helped Starbucks drive up sales — although it has. According to Forbes, it’s also helped the company do deep-dive research into products and analyze the performance of different locations in order to optimize labor assignments.
6. IoT devices
The last area of AI that’s becoming extremely relevant to and exciting for customer service is IoT: the internet of things. Gartner predicts there will be more than 20 billion IoT devices by next year.
What does this mean for customer service? While IoT devices aren’t AI themselves, they will connect to AI and greatly scale its accessibility. The inherent nature of IoT devices like home voice controllers and smartwatches is to gather data. The more data organizations can gather and analyze through AI, the more insight they’ll have into customers, enabling better customer experience and support.
The future of customer service AI is now
If you have any doubt left lingering about whether AI is soon going to be essential to all kinds of customer service organizations, consider this: McDonalds just acquired an early-stage AI company to revolutionize the way it takes drive-through orders with speech-recognition technology.
AI is on the cusp of becoming a standard customer service technology in the very near future. And by far one of the most profound things about AI is that it holds the potential for so many customized applications. If you can dream up an AI solution, chances are, it can now be done — or will be possible very soon.