Improving a customer’s experience is always a top priority for any brand. Customer service should be at the forefront of any brand’s priorities because a customer that feels valued and respected is a lot more likely to come back. It might cost extra resources, time and money, but quite often, it’s the only thing that helps one brand stand out from the competition. Remember, acquiring new customers is six times as expensive as retaining one.
Customer service is often deemed ‘bad’ for reasons that are not business’ or customer care representative’s fault in particular. It’s only natural, considering a single person is expected to take care of hundreds of people’s problems, each expecting to be served as quickly and efficiently as possible.
One of the most interesting and perhaps effective ways that have been invented to deal with these problems has been artificial intelligence.
Resource use optimization
According to research by IBM, companies spend $1.3 trillion a year on customer service calls alone. However, a large part of customer care calls goes into answering routine questions and dealing with other minor issues. According to the same research, as much as 80% of all customer service queries could be handled using a bot, freeing up representatives’ time to more pressing and challenging issues.
Call Computerization, also referred to as Computer Telephony Integration, is an advanced method of speech recognition based on machine learning. It’s been shown to be a major improvement to conventional voice response systems, leading to up to 80% cost savings as compared to normal human-powered call centers.
Read more on how to outsource live chat
Time saving
A lot of customer service happens over email helpdesks. However, sorting through and tagging email tickets has always been a troubling problem for customer care representatives. Thanks to better text recognition and natural language processing AI systems, however, it could turn out to be a thing of the past.
The most interesting time-saving feature AI can enable is email auto-tagging. Rather than have the customer care specialists sort through the emails themselves, these bots can scan the content of any email and automatically tag it. These tags can then be sorted through by relevant parties saving a lot of time. This also allows them to deal with more complex tasks that humans are more suited to do.
Assisting customers at every stage of the sales funnel
Different bots can be designed to be present at every stage of the funnel. Or, more correctly, bots can be fed with relevant data to ensure that customers are guided through every step towards making a purchase.
AI makes it easy to collect data about customers and have them in a single central location. This way, regardless of the device or location a customer is visiting a business from, their information is always ready at hand.
AI assistants, therefore, have a much simpler time advising customers about making the right decision at every stage. In case the interaction between human and bot start getting too complex, the rein can then be handed over to an agent.
Improved recommendation engines
Along the same lines, as a customer uses your platform more, you will also have more data about them. In so doing, you no longer have to thrust random information their way or rely on less nuanced solutions like ‘top ten lists.’ Two great examples of companies that have managed to come a long way with their recommendation engines are Netflix and Amazon.
These companies all use NLP (Natural Language Processing) as part of their AI engines, and various NLP examples should be clearly visible in the approach other companies are taking in their overall customer service experience.
Faster response times
The most annoying thing customers complain about when it comes to customer care is having to wait on the line to be served. But since there are only so many people to respond to queries, this wait time is inevitable. This could change as bots are always online and don’t share a lot of the same problems that human beings would otherwise have to, eg. fatigue.
Human agents also spend a lot of time researching answers to customer queries. Since these questions are often repetitive and time-consuming, bots can take the mantle instead. Frequently asked questions can be detected and automatically responded to, again saving up time for more pressing queries.
Better customer engagement
In the olden days, the only thing used to pin customer care teams to their performance was how little time it took to get off the phone with anyone that had inquiries. Of course, this method had a lot of issues and allowed little room for the collection of feedback.
Today, social media has turned that on its head and encourages brands to create and have conversations with people. With the right CRM, artificial intelligence could make consumer data readily available to any agent in need of it, greatly improving customer engagement.
Reviewing customer feedback
Another important part of providing customer service is reviewing customer feedback. Since feedback comes in droves, having agents scan through everything submitted through various forms can be a tedious process. An AI assistant takes care of this problem by compiling all the feedback in comprehensive reports that can then be analyzed rather instead.
The use of this data can then be expanded and used by other departments in the company, too. For instance, if most of the customer queries are from certain topics or segments of your brand, concentrating on these in your content marketing is a great way to drive more traffic.
Conclusion
AI is not meant to replace customer service teams – not by a long shot. Rather, they are meant to improve their productivity and reduce the workload every individual has to deal with. Businesses should integrate AI into their systems to help them gain new customers, retain existing customers and increase brand loyalty and engagement. It enables automation of quick responses, routing to the right stream and fast collection and processing of customer data. Brought together, all these conspire to make customer service teams more efficient.
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