How AI Chatbots Handle Complex Customer Support Queries?

Customer expectations from businesses are increasing by the day and it’s becoming hard for businesses to keep up. Topping the charts is the demand for prompt, accurate, and personalized customer support across channels. 

Traditional customer support centers around human agents who often struggle to keep pace with the demand – especially when it comes to handling complex queries. 

In this post, we’re sharing how custom AI chatbots can help address complex queries at scale. 

What is a complex customer support query?

Before we go into exploring how AI chatbots and co-pilots can help, let’s understand what counts as complex. These are customer queries that cannot be quickly addressed with a standard self-service platform or FAQs – they require a deeper level of understanding, interaction, and problem-solving. Some common characteristics of such queries include: 

• Multifaceted issues – These involve several layers of information, requiring the agent to pull information from various sources to provide a solution. For example, a query about integrating two separate products with yours. 
• Specialized knowledge – These queries need skilled, specific, or even technical expertise to resolve. For example, a customer wants to set up a custom billing cycle for using your product. 
• Tailored responses – Complex queries cannot have standardized responses. They need to be personalized based on the circumstances, customer sentiment, and urgency. For example, troubleshooting a feature. 
• High-stake issues – These are queries that could potentially impact business continuity. They require handling queries with more urgency and emotional connection with the customers.

Common challenges in handling complex customer support queries

Here are some of the common challenges that customer support team agents face when handling complex queries: 

• Longer response times – Complex queries take longer to resolve as they require taking multiple steps or coordinating with different team members. For example, an agent may be required to seek assistance from the product team while pulling resources from the knowledge base for the customer to find a solution. 
• Knowledge gaps – Customer support agents can often struggle to find the right resources at the right time to reference when handling queries. Jumping between workspaces, document management systems, knowledge bases, and CRMs can take time. 
• Inconsistent responses – Different agents may use varying responses to address the same complex problem. This can result in inconsistent troubleshooting or unclear internal documentation, which can lead to errors in the future. 
• Handling high volume – Support agents can get bogged down by the volume of requests as the business scales. This can take away the time and attention agents need to address complex queries, leading to bottlenecks. 
• Lack of personalization – Complex queries come with unique customer needs. This requires agents to focus on understanding the intent and sentiment during an interaction, which can be hard to deliver when handling volumes of queries manually. 

How AI chatbots and co-pilots handle complex customer support queries? 

Here’s how AI co-pilots in customer support work to help resolve complex queries faster: 

1. Advanced natural language processing 

Unlike rule-based systems that respond to trigger keywords, AI-powered chatbots and co-pilots have the ability to understand context. Even if a query has multiple layers of complexity, AI can help understand the intent and recognize the context to offer a resolution faster. 

Take for example a customer asking, ‘Why is my system lagging after the last software update?’. A good AI co-pilot will pull in the required information from the update log, knowledge base, and software patch notes to help the customer with troubleshooting. 

2. Integrated knowledge databases 

Solutions like Auralis AI can integrate with knowledge bases, CRM systems, and other databases in real time. This helps the co-pilots pull information related to a query faster, providing the customer with detailed and specific answers without human intervention. 

For example, a customer is asking about a complex billing issue. AI chatbots can retrieve past billing information, cross-check transactions, and provide a detailed breakdown of the issue to the customer. 

Also read: How real-time AI assistance is revolutionizing customer support

3. Machine learning for continual improvement 

Machine learning enables AI chatbots to learn from past interactions and resolutions. This helps improve their ability to handle complex queries and even predict issues to offer proactive solutions. 

For instance, if the AI chatbot observes the same issue with software time and again, it can create a log of the same and its solution in the knowledge. The new entry helps other agents resolve these issues faster in the future. 

4. Co-piloting with human agents 

When a query gets too complex, AI chatbots have the capability to trigger a hand-off to the human agent. In this case, AI co-pilots can gather essential information, streamline the troubleshooting process, and suggest the best-suited response to human agents. 

For example, a query related to legal compliances. The AI co-pilot will provide the agent with a summary of the issue, past interactions, and potential solutions, allowing them to start the conversation with complete context. 

Also read: How AI co-pilots enhance customer support efficiency

5. Faster resolution with workflow automation 

AI chatbots can initiate multiple workflows during an interaction. This helps the customer support team handle different aspects of reaching query resolution in tandem instead of one by one, saving time. 

Let’s say a customer reports a system outage. The AI co-pilots and chatbots can trigger diagnostics, escalate the issue to the technical team, pull in the resources to answer the customer, and at the same time, update the logs. 

6. Personalization at scale 

AI has the ability to create comprehensive customer profiles based on past behavior, interactions, preferences, and purchase/ usage history. This helps address their queries with more personalization, leading to higher satisfaction rates. 

For example, a high-value customer reaches out seeking assistance in customizing a feature. The AI chatbot can personalize the troubleshooting guide and escalate the issue so that the agents can prioritize the ticket over others. 

Also read: Best practices for implementing AI-powered support automation

Real-world examples of AI chatbots for complex support queries 

Here’s how we’re seeing different industries use AI chatbots and co-pilots for customer support: 

• Telecom industry – Companies can handle high volumes of complex queries around billing discrepancies, service outages, and technical issues. They can automate the troubleshooting process with AI. 
• eCommerce – AI chatbots can be used to automate tasks like tracking orders, initiating returns, and troubleshooting delivery delays. On the other hand, co-pilots can be used to address refund and exchange-related queries. 
• B2B SaaS – AI co-pilots can be used to provide assistance on queries related to software integrations, customizations and user permissions. This can also help prevent escalations to technical teams. 

Conclusion 

AI chatbots and co-pilots are helping B2B companies handle complex customer queries more effectively and efficiently. 

And with solutions like Auralis AI, companies can build out custom automated workflows to tailor support experiences for their customers. 

In fact, we are seeing companies 5x the productivity of their agents and automate almost 70% of their customer support queries. It has also helped them identify, understand, and resolve complex queries faster by integrating with their tech stack and providing actionable insights on every resolution reached. 

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