Companies receive support inquiries from various channels. This may include emails, support tickets, tweets, chat conversations with customer support representatives (CSRs), chatbot conversations, and more.
This is a lot of data that you are dealing with and it’s mostly unstructured and scattered in nature, making it that much harder to manage.
All this text data can actually be leveraged to improve speed in responding to customer service inquiries and reduce the volume of incoming tickets. According to a research done by supperoffice, the average response time to respond to customer service requests is 12 hours and 10 minutes. That’s long!
So, how can you reduce response times while also being effective?
This can partly be done with automation using Machine Learning and Natural Language Processing (NLP), both subfields within AI.
Let’s look at 5 areas where AI can help streamline your customer service workflow so that you can reduce response times and increase overall efficiency. This is by no means an exhaustive list, but it’ll help you get ideas on where you can start leveraging AI in customer service.
What’s Machine Learning & NLP?
NLP or Natural Language Processing is a form of artificial intelligence that enables computer programs to process and analyze unstructured data, primarily free-form text data. Most of your customer service requests are in fact unstructured. Take support tickets, emails, and Tweets—all free-form text!
Machine learning on the other hand is a process to automatically learn a computer program leveraging data without being explicitly programmed. Machine learning can leverage both structured and unstructured data and it can also learn really complex patterns that a human eye may not detect easily.
NLP in combination with machine learning can be powerful tools for transforming messy unstructured data to something more structured (e.g. predict labels for each service request). It can also help you find patterns in your data (e.g. groups of similar inquiries) as well as have a natural language conversation with humans (e.g. a chatbot).
Without these automation approaches, you’ll be left to perform a lot of manual, repetitive work, or use a set of complex rules to achieve some level of automation.
5 Ways to Use NLP in Customer Service
Here are 5 ways AI, specifically, NLP and Machine Learning can be useful in reducing response times and increasing efficiency in your customer service processes.
#1: Recommend Answers
Customer service representatives spend a lot of time researching answers to customer questions. When your service representative tries to respond to a customer question, they can be overwhelmed with identifying the best answer from the pool of possible answers. What they really need is one to two answers that will address the question.
Some companies maintain an exhaustive list of problems and corresponding answers which the service representatives have to search through, sometimes even manually. This can be painfully slow and energy-draining if you have to perform a search for each and every question.
Machine Learning and NLP can be really useful here in recommending the best answers given a support question. What’s good about this is that a “score” can be generated to indicate the likelihood of an answer being able to resolve the customer problem.
With this approach, instead of explicitly performing a search, the service representatives are now having information pushed to them automatically, preventing a break in their workflow.
By not wasting time searching for answers to common questions, response times can be improved, which also means, your service representatives will be able to handle a larger volume of support issues. And the side benefit—they’re not seeing doubles by the end of the day.
#2: Suggest Historical Threads
While some support questions can be easily answered with the recommended best answers, others can be complex, requiring extended research. One way for CSRs to solve complex issues is by looking into related historical threads (which have been successfully resolved), and understanding how those issues were resolved. With this, your service representatives will be able to better resolve the issue at hand or form a more complete answer to the support question.
With Machine Learning and NLP, we can automate this process by recommending related historical threads for any given support request. This saves your service representatives from having to conduct extensive searches, contact peers, and managers for help on an issue.
This again can help improve response times and maybe even improve your first contact resolution as the service representatives are better equipped at handling issues. As a side-effect, you reduce follow-up support requests.
#3: Group Similar Questions
As we all know, context switching can be hard. Going from resolving issues related to signups to billing and then back to signups can be a productivity killer. According to Bud Roth, author of Be More Productive: Slow Down, distributing your energy over a wide variety of tasks can dilute your effectiveness the same way interruptions do.
By grouping similar support questions, service representatives can address similar problems in chunks, where the knowledge bank that they’ll have to tap into and the pool of potential answers are related.
Grouped customer support questions
With AI, we can automatically group similar questions as shown in the example above. Notice that the first question is all about adding a profile picture.
The benefit of doing this is that it maintains the same train of thought in resolving issues. In some cases, the solutions may be identical, while in others your service representatives will know what steps to take to resolve an issue while everything is still fresh in memory. By limiting context switching you can expect to see a reduction in response times.
#4: Auto-route Questions
Support questions can be extremely messy. One question may be related to billing another related to login and next related to a hacked account. Routing tickets to a service desk that manually reassigns them to appropriate support teams or representatives is a slow, inefficient, and error-prone strategy. However, many organizations are still doing it.
Unfortunately, delays in assignment to the right personnel lead to delays in resolution.
With the help of machine learning and natural language processing techniques, support questions can be automatically routed to the appropriate service representatives. This can be done by classifying each incoming question to a predefined set of categories (e.g. “accounts and profile”, “security” and “billing”). These categories can be used to route questions to representatives or teams best at handling those topics.
By intelligently routing questions to relevant expertise, you’ll be ensuring a fast, timely response.
#5: Auto-prioritize Service Tickets
Some companies address support issues in the First in First Out order (FIFO), meaning the oldest support issues get addressed first. While others, manually assign priority based on the severity of the issue.
Don’t forget, that not all customers are equal and not all problems deserve the same level of attention. Some of your customers are high-value customers who have been using your services for a long time or are high-ticket purchasers.
By clubbing them as one and addressing threads in the FIFO order, you’re missing out on the opportunity to retain your highest-valued customers. If you’re spending your time solving 100 low-priority problems for low-value customers before serving your most valuable ones – it’s time to think about making changes.
While you can start serving your high-value customers first, with AI-based automation, you can combine various factors into support issue prioritization.
For example, you can develop a machine learning model that considers the severity of support threads, customer lifetime value, tenure, and purchase value to auto-prioritize new support questions. This will ensure that your highest value customers with high priority issues get served promptly and by your best service representatives.
How to Get Started with AI in Customer Service
So where do you start? Start by nailing down your most inefficient processes. What’s taking you the most time? Is it the search for answers or ineffective question routing?
Once you have a handle on the pain points, the next step is to determine if the inefficiency can benefit from AI. Otherwise, you’ll be using AI for the sake of AI and this will become an expensive experiment. Despite the hype, not all problems benefit from AI. Many can be solved with other solutions, such as better software engineering.
Always start small and slowly grow your AI capabilities over time.
Trying to do it all at once will set you up for failure as there’ll be too many changes in your workflow. In addition, some of the automation can be at odds with each other. For example, by auto-prioritizing support issues, trying to then group questions may or may not be effective.
Optimizing all at once also prevents your ability to measure success. You’ll not know if the reduction in response times was due to recommendation of answers or if it was due to auto-prioritization of support issues.
Customer service is a great place for leveraging AI and directly seeing its impact. This is largely due to the manual, repetitive nature of some of the tasks.
On top of that, the large amounts of data generated in interactions with customers can actually be a blessing in disguise. While it’s scattered and unwieldy, with a good data strategy, it can be used to train several machine learning models to automate away some of the inefficiencies.
Finally, the icing on the cake is that in customer service you have established metrics like average response times and first-contact resolution. These can be used to assess the success of your AI automation projects. So you’ll not be in the dark when it comes to assessing your return on investment!