Continual advancements in technology have transformed the way customers communicate with businesses, along with the expectations of what makes a good service. Sixty-six percent of customers admit to using more than three channels to reach out to a business, which shows they prefer omnichannel experiences. But they also want seamless, fast, and personalized interactions.
As businesses re-shape their strategies to keep customers happy and improve customer loyalty, many of them are turning their attention towards artificial intelligence (AI) to automate their customer service and offer 24/7 assistance.
Recent research shows that 45% of companies are using or plan to use AI for customer service, and 37% are already using chatbots.
In this article, we’ll outline the benefits of using AI in customer care, introduce some of the AI tools available, and show you how you can get started with AI right away. Keep reading or jump to one of the sections below:
By building smart machines that can process large amounts of data, artificial intelligence (AI) is revolutionizing business and taking automation to the next level.
From improving response times to handling common requests, AI is helping customer service agents to deliver personalized and more efficient customer experiences. For example, if a customer sends a message that says, “I can’t log in because I’ve forgotten my password”, AI-powered technology can recognize the intent behind this query and send the user a quick guide on how to reset their password without any input from a human agent.
Automating monotonous and repetitive tasks not only boosts productivity and customer engagement (increasing customer satisfaction), it also allows agents to focus on more fulfilling tasks that have a greater impact on business performance.
Integrating AI tools into your customer care strategy can lead to:
Agents at American Fidelity Assurance used to spend 45 hours reading 9,000 emails and routing them. Now, with machine learning, they’re able to classify and route emails in just 3 seconds.
Customer service teams can use machine learning to classify tickets by topic, language, sentiment or urgency, and set rules in tools they already use to automatically route tickets to the correct agent or team.
Automating tasks in customer service can significantly improve response times, and help you deliver a smooth and consistent customer experience across multiple channels, increasing customer satisfaction. Fast responses make your customers feel special and increase their loyalty to your brand.
Automation also has an impact on job satisfaction. As customer service reps are relieved of repetitive manual tasks, thanks to technology like chatbots and virtual assistants, they can focus on more fulfilling tasks and offer more personalized customer experiences. According to Aspect’s survey on chatbot perception, 79% of agents feel that handling more complex issues improves their skills.
Machines provide accurate, reliable and consistent results. Unlike humans, computers don’t get tired when performing repetitive tasks, and always apply the same criteria when tagging data.
For instance, customer support agents may incorrectly tag sales inquiries and route them to the wrong department. Humans also have different ideas of what’s urgent, or _what’s _negative or positive, leading to inconsistencies when tagging. Machines, on the contrary, make decisions based on one set of rules, so they are less prone to errors and bias.
Since AI-powered tools are more accurate, businesses can rely on insights they receive and make decisions based on their data. For example, if a SaaS company notices that a large portion of negative support tickets are related to ease of use, this might prompt them to create a guide on how to use their software.
With AI tools, you can analyze texts and assign tickets to the right teams as soon as you receive them. That means, no more wasting time passing tickets from one team to another, and urgent requests won’t slip under the radar. Chatbots, meanwhile, provide 24/7 assistance and can handle frequently asked questions automatically.
AI tools can collect and analyze historical customer data in a matter of seconds, and provide clients with better solutions based on past interactions. They’re also able to detect patterns, trends, and behaviors, helping businesses deliver more personalized customer experiences. Personalizing the customer experience should be a key strategy, since 64% of millennials value anticipation and customization.
You might also leverage AI to identify customers at risk of churn, or anticipate problems that might lead to churn, and take action by sending those customers a personalized solution.
Flight cancellations, for instance, frustrate customers. With AI, airline companies can get smarter at handling these incidents by quickly identifying affected customers and offering them solutions, such as flight alternatives along with a discount voucher for future travels.
Already, nearly 70% of airlines are investing in AI for customer service (or planning to do so).
Implementing AI to automate tasks in customer care reduces time and costs, and enhances the overall customer experience. This leads to higher customer satisfaction and loyalty, and improves the customer’s life cycle. According to a McKinsey global research, sixty-three percent of executives report revenue increases from AI adoption in the areas where it is being used.
There are many AI tools that can help take customer care to the next level. Depending on the type of solution you are looking for, here are some options you may want to consider:
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!
Clients 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.
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.
Zendesk, for example, offers 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 integrate MonkeyLearn with Zendesk to 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 in Zendesk to route tickets to the most suitable agent.
Another AI-enhanced desktop is Freshdesk, which has implemented a chatbot to answer simple “how-to” customer queries, algorithms for filtering important tickets, and a tool that generates reports.
You can also connect Freshdesk to MonkeyLearn and start classifying your customer support tickets right away.
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”. That way, when a customer tags you on Twitter saying they are not able to make payments through your site, you can take immediate action and avoid missing out on a sale.
Even though there are many open-source libraries that you can use to build text analysis models from scratch, using SaaS APIS is simpler and more cost-effective.
MonkeyLearn, for example, offers a series of pre-trained models that help you get started with text analysis right away – no code needed. The platform also enables you to create your own custom models for machine learning tasks like sentiment analysis, keyword extraction or topic classification.
These are some of the text classification models you can use to automate ticket tagging:
In short, chatbots, AI-powered helpdesks, and text analysis tools are improving the way agents handle incoming requests, by automating routine tasks and providing actionable insights through customer support analytics.
Leveraging AI to enhance customer care is not as complex as it sounds. With an AI platform like MonkeyLearn, you can automatically tag all your incoming support tickets and route them to the correct agent or department, prioritize issues, and discover insights related to a specific product feature or your overall brand.
MonkeyLearn’s models use machine learning techniques, and are trained with samples of data so that they can deliver a prediction. You can try the pre-trained models ‒ great if you’re looking for a quick, ready-to-use solution ‒ or build your own custom tool.
If you build a text classifier with MonkeyLearn, you’ll be able to gain more accurate insights because you train your model with data and tags that are more relevant to your business. And you can define how texts are tagged, for example you might have your own definition of positive and negative, urgent and not urgent, and so on.
The great news – building your own classifier involves just a few steps:
There are some best practices for ticket tagging, which you should check out before you start defining your tags.
Put the model to work. You can easily integrate your model with your customer service software, so that it can analyze all your incoming support tickets. There are two ways to do this:
Artificial Intelligence (AI) is revolutionizing customer care, improving agents’ daily workflows and leading to faster, smarter and more personalized customer experiences.
Companies are using AI to automate routine processes, such as tagging incoming support tickets, routing them to the appropriate customer support agent or department, and detecting issues that require immediate attention.
With MonkeyLearn’s machine learning tools, you’ll be able to build customized text analysis models in just a few steps, and easily integrate them with your existing customer service software.
Sign up to MonkeyLearn for free and get started right away.
March 27th, 2020