 |
| Image credits: unsplash.com |
Customer support has become a complex environment consisting of multiple services and providing a comprehensive support to the customers. Customers address support for a myriad of reasons, from order confirmation to delivery inquiry, to complain, to name just a few. At the same time, this company department is one of the main influences on customer's satisfaction. By providing impeccable service, they can identify the customers' pain points and take action to improve customer experience.
And with the implementation of AI-based tools and assistants, customer support takes a new turn in providing services. Smart solutions optimize their internal processes, while at the same time increasing the external metrics, such as response rate, efficient support, and proactive communication. Let's see how machine learning algorithms help to build a smarter customer support environment.
What Challenges Does Current Customer Support Experience?
With placing the customer in the center of all operational activities, many businesses modify their strategies in order to increase customer satisfaction and their overall experience. By creating a customer-centric culture, businesses remain competitive and expand. That's why the role of customer support has also become more important. To meet the customers' expectation and follow the business strategies, customer support should not only manage customer requests, but also involve in analytics, marketing, and even sales.
To manage these complex tasks, customer support frequently goes through the following challenges:
- High volumes of inquiries. Human agents treat every case individually, unable to quicken the process. Without AI for customer support, they need to check data manually and type the individual response.
- Repetitive requests. Although agents use templates, they individually respond to every customer, even if they only share the link to the knowledge base site.
- Availability 24/7. For businesses who want to operate 24/7, they need to have more people in the team who would cover different shifts. That's why not all businesses can afford to be operational all the time.
- Complex issue resolution. To provide solutions to more complex issues, customer support agents need to go through a deep analysis and the historic data of the customer's request. This extends the time of response.
- Data overload. Customer support agents work with numerous tools. Apart from managing customers' requests in CRM systems, they can contact third parties for deliveries, technical team, or account managers. This requires working with numerous tools and extra time to process customers' requests.
AI Comprehensive Solutions for Customer Support
With the evolving deep learning technology, large language models, and natural language processing, customer support can receive a real boost in their operations. McKinsey expects AI to automate 30% of hours of work today, which means that the agents will be able to get rid of the majority of manual and repetitive tasks. Let's see what ready solutions we already have in place.
Agent Performance
AI assistants that scan customers' requests analyze the history of interactions and pre-populate ready responses that customer agents need to validate and send. This solution saves agents time on response and decreases the response time. In the form of copilot assistants or bots, such AI tools can integrate with CRM and third-party systems to retrieve the information.
Personalized Support
The feature of sentiment analysis allows AI tools to tailor individual responses with custom information for every client. As a result, AI assistants suggest the response based on customer's behavior, customer's previous communication, and purchase history. Not only can they give personalized solutions, but also suggest similar products or services (e.g. in e-commerce or retail).
Analytics and Insights
Machine learning algorithms streamline the operations with their analytical features. Integrated in customer support's working environment, AI tools can classify customers' inquiries in terms of prioritization. They can analyze the historical data and proactively signal if any technical issues occur. By classifying issues, the algorithms form statistics to highlight the pain points in the processes. Besides, the algorithms can gather customer feedback and suggest ways to improve.
Automation
Some repetitive tasks can be automated. For example, a bot answering customers' general inquiries can share knowledge base sites and materials for self-serve activity. This spares customer support's time on answering repetitive questions for troubleshooting or for account management. At the same time, algorithms integrated into CRM systems can identify customers and recognize their intention, allowing agents to focus on the solution and the final response.
However, it's important to highlight that the main focus that we have today across industries is to leverage operations by implementing various technical solutions. We don't want to replace agents by integrating bots and virtual assistants across various channels. Because only human agents can provide a human, empathetic touch in the interaction with every customer. And this human personalized approach is what all customer-centric businesses strive for.
Smarter Performance, Endless Possibilities
 |
| Image credits: unsplash.com |
We can see that customer support has become a complex environment, resolving customers' issues and proactively offering smart solutions that increase customer satisfaction. To manage the multitude of tools and tasks and remain as productive as customers want them to be, customer support agents leverage their performance with the help of AI-based assistants. Gradually integrating into various tools and industries, AI solutions increase performance, prepopulate responses, operate 24/7, and improve agents' responses.
The benefits of AI in customer support are diverse, including reduced wait times and personalized interactions. With higher operational efficiency and equipped with the most accurate analytics, customer support agents can tailor their responses, offering an individual approach to every customer. Businesses can establish a smarter customer support system that provides great service. In the end, with the changing customer experience landscape, integration of AI technologies plays the main role to promote a human-centric approach.