When it comes to imagining the future, customer service AI often gets painted in a dystopian light. Take the 2002 sci-fi film Minority Report. Tom Cruise’s John Anderton walks into the Gap, an identity recognition system scans him, and a hologram asks about a recent purchase.
Something is unsettling in this story—an unsolicited non-human seems to know everything about you. But the truth is, customers today expect this kind of sleek, personalized service. Their relationships with retailers, banks, health-care facilities—and virtually every organization they have business with—are changing. In an always-on, digital economy, they want to connect when they want, how they want. Customers want their product questions answered, account issues addressed, and health appointments rescheduled quickly and without hassle.
The use of artificial intelligence in customer service is not only revolutionizing the customer support function but also improving customer loyalty and its brand reputation. Companies in the B2C industry segment are increasingly entering an age of automated customer service that is boosting the brand experience for customers. A Gartner report predicts that by 2020, around 85% of customer interactions with businesses will be automated without any human intervention.
By definition, customer service AI is a business process aimed to eliminate or reduce the level of human involvement in providing customer service.
Why should you deploy customer service AI in your business?
A recent Forrester analysis identified customer support trends that show an increase in self-service portal usage to over 81% among responding American adults. In fact, by 2020 more than 80% of customer service will be conducted without engaging humans.
Customers have begun to warm up to the idea of dealing with the bots. CX sensitive brands are also exploring options to incorporate any AI that interfaces directly with customers. While AI is a long way away from replacing customer support entirely, it is still a useful tool for enhancing your support offerings. Ignoring the potential of AI in customer service might allow your competitors to surpass you. With that in mind, here are a few reasons you should still be thinking about AI, even if you’re doubtful of the robot revolution.
Agents like AI because it prevents them from doing robotic tasks that they were forced to do before. Agents can get burned out and demotivated answering the same repetitive questions. AI is perfectly suited to repetitive, menial tasks like tagging tickets or surfacing documentation to customers for simple how-to questions.
Implementing automatic answers and routine task automation improves the agent experience by giving them more time to deliver amazing experiences at every interaction. This helps empower your team to do what they do best – be human.
Instead of trying to replace agents with AI, use the machines to make the agent’s work more enjoyable.
Companies are collecting more data than ever on their customers. Product usage, surveys, and customer conversations all contain a lot of insight about what our customer does. It’s difficult for humans to accurately analyze this vast amount of unstructured data.
Getting customers to support a seat at the product table requires quantitative data. AI and machine learning can derive quantitative data from the qualitative – much faster than humans can. AI can also find the patterns that your agents didn’t even think to look for. Because each agent is only seeing a small slice of the total number of customer conversations, they can’t determine if the questions they are answering are one-offs or symptoms of a much bigger issue. While robots are still pretty terrible at talking to customers, they excel when it comes to data.
Even if your brand is customer experience sensitive and firmly against the idea of AI talking directly to customers, AI can still lend a capable hand behind the scenes. Using natural language processing, AI can “read” a ticket and direct it to the right team much faster than a human triage system can.
For example, Uber built COTA (Customer Obsessed Ticket Assistant) to help route tickets better and suggest answers to customer support agents. By empowering customer support agents to deliver quicker and more accurate solutions, COTA’s powerful ML models make the Uber support experience more enjoyable.
Chatbots – also known as conversational agents – are redefining customer care: they can solve queries around the clock, handle thousands of support tickets at the same time, and solve up to 70% of routine customer issues. All without humans!
Customers value fast responses, and chatbots provide instant, reliable, and personalized support 24/7. Not only that, you can train AI-powered chatbots to understand the intent, sentiment, and main topic of a customer query. Once chatbots have processed queries, they’ll then simulate real conversation to provide a solution or refer customers to a human agent. The more queries chatbots handle, the smarter they get.
Companies are increasingly using chatbots in many ways to enhance their customer service, from collecting user data at the beginning of an interaction (like contact information, order number, etc), to handling frequent requests (and routing tickets to the most appropriate person if the subject is out of their scope). This allows human agents to spend less time on routine and repetitive tasks, and more time on complex issues.
For instance, Canadian airline WestJet, for example, introduced a custom bot named Juliet to handle its customer queries in Messenger. Six months after its launch, the bot was able to handle 50% of total inquiries and increase customer satisfaction by 24%.
Some customer service software is equipped with AI to automate ticket tagging and routing, data collection, and even answering simple queries ‒ saving agents valuable time and improving the daily workflow.
For example, AI-powered features such as the “answer bot”, which suggests articles from a knowledge base to customers. There’s also a tool for identifying relevant content in customer queries and suggesting targeted support content that needs to be created. You can easily and automatically tag support tickets by topic, sentiment, or urgency using machine learning – helping you prioritize those that are high risk and require extra attention. Then, you can set triggers to route tickets to the most suitable agent.
You can also implement a chatbot to answer simple “how-to” customer queries, algorithms for filtering important tickets, and a tool that generates reports.
Why is this so important?
Because most customers like to solve a problem or answer queries on their own. Among the leading lessons learned from customer service statistics, 73% of customers want to solve product or service-related issues on their own, while 64% try to solve their issues before contacting customer service. An example of customer self-service is the Frequently Asked Questions (or FAQ) page on business websites.
Content moderation using AI is employed to review user-generated content and ensure it is appropriate for public access. AI can review text content such as product or service reviews, customer service chat logs, social media postings, etc. on a large scale and across multiple channels in real-time. Using Natural Language Processing, AI can detect inappropriate content much faster than a human moderator and ensure it doesn’t slip under their radar. Using an AI Platform you can quickly build and deploy custom content moderation ML models in hours.
Text analysis tools can help customer support teams automatically tag incoming tickets based on topic, sentiment, language, urgency, etc., and then route them to the most suitable pool of agents.
Let’s say you are an e-commerce site receiving hundreds of customer queries a day via different channels. You can benefit from text classification to tag all incoming tickets by topic and classify those that refer to “Payments” as “Urgent”.
Being qualitative, most businesses were unable to do a text analysis of customer feedback and turn them into actionable insights. AI technology can now automate this process through the analysis of descriptive customer data, thus making customer review analysis more actionable. According to a Marketing Charts study in August 2018, 64% of the market researchers believe that AI will take over the task of “finding relevant insights in feedback data” in the next 10 years.
An IVR has a set of simplistic predefined rules that it follows in a deterministic manner. In contrast, AI, which includes areas like Natural Language Processing and Machine Learning techniques, can understand statements instead of simply giving the user a set of choices.
Also, with IVR, a predefined input gives a predefined output. With AI, a predefined input may give a completely different output depending on what the system has learned through probability calculations.
AI should also eventually improve the caller experience by ending the often frustrating “Press 1 for sales” or “Press 2 for customer service”, followed by a queue that negatively impacts modern contact center interactions.
Both AI voice agents and chatbots can capture a lot of granular data around each customer interaction, which can be fed into analytics engines to help optimize the call center process.
AI tools such as sentiment analysis can also help speed up this process by quickly spotting trends like anger or dissatisfaction within a large data set, often faster than a human advisor can.
Yet the notion of AI completely replacing a human contact center team is still a long way off, especially considering the attitudes of many towards customer service AI.
Every day, contact centers accumulate vast amounts of customer data. Customers are certainly aware of this and have come to expect improved customer service in return for providing extensive amounts of personal information for companies to use.
One frustration that customers often face is having to repeat their details on multiple occasions when calling in, or they may receive an odd offer from the company that is completely unrelated to them. When these things happen, dissatisfaction starts to set in.
To tackle this, robotic process automation (RPA) helps to eliminate redundant customer and employee effort by capturing, analyzing, cross-referencing, and sharing information across platforms and channels; all of this without being intrusive.
AI will enable new trends in customer behaviour to be identified at very early stages in their development. Interaction analytics tools already can do this, but the addition of AI will accelerate the identification and mean that there is less need for human intervention.
Providing this early insight into businesses will enable them to brief advisors so that they handle the emerging customer needs and expectations more effectively. This could lead to retaining customers who might have been about to defect or up-selling to customers who are looking for information about a new topic.
The ability to spot trends in customer data will also enable businesses to model best practices and predict the outcomes or the consequences of a particular course of action.
By using AI in this way, an organization could see benefits in resource planning, sales, and marketing campaign planning, as well as attaining a more accurate Voice of the Customer (VoC).
Combining process automation technology with optical character recognition (OCR) enables the automation of more complex business processes.
Customer letters, emails, and web forms are ingested into the system as scanned images (through OCR functionality). The system has the capability to understand the intent of the inquiry and extract all the relevant details from the content. It then produces and sends a recommended customer response over to the human employee. The employee has the option to edit the content before sending it over to the customer. The structured input is received by an RPA robot for data verification and enrichment (adding additional relevant info to the case). The updated data is then automatically uploaded to the case management system.
In short, Customer service AI is improving the overall customer experience, by automating routine tasks and providing actionable insights through customer support analytics. Tap into the potentiality of artificial intelligence in order to bolster your customer service.