7 Essential Chatbot Best Practices for Call Centers

What Is a Call Center Chatbot?

A call center chatbot is an AI-powered virtual assistant that interacts with customers in a contact center via text or voice. It handles routine customer inquiries, answers common questions, and escalates complex issues to agents. More and more call centers are offering live chat support to guide customers, engage with leads, and escalate chats to phone conversations.

An effective chatbot implementation helps you meet customer demands across multiple communication channels, including social media (WhatsApp, Facebook Messenger) and e-commerce platforms. Implementing chatbots in call centers also increases efficiency and agent productivity — but only if done right.

Poor chatbot implementation can disrupt your contact center workflows, burden your agents with unproductive tasks, and degrade the customer experience. It’s important to automate customer service properly. We’ll discuss how.

Benefits of Using Chatbots for Call Center Operations

Integrating a chatbot into your call center operations will:

1. Reduce wait times

Nearly 45% of participants in a Replicant survey said they find wait times of up to 15 minutes frustrating. A chatbot can significantly reduce the wait, allowing you to provide prompt and valuable customer support.

A chatbot can independently handle routine customer queries, such as order status requests and general billing inquiries. It can also create tickets and automatically route more complex questions requiring human input to the agents that are the best fit. Quick replies lead to higher customer satisfaction, as issues are resolved on time.

2. Increase operational efficiency

In addition to responding to customer inquiries, a chatbot handles tedious call center tasks like appointment scheduling and order processing, freeing agents to handle more complex issues.

A chatbot, for instance, can collect user details such as date, time, and service, integrate with a calendar to check availability, and send booking confirmations automatically so that agents don’t need to deal with manual appointment scheduling from scratch.

3. Reduce call center costs

Gartner predicts that conversational AI deployment in contact centers will reduce agent labor costs by up to $80 billion globally. It argues that one in 10 customer interactions will be fully automated, resulting in up to a 30% reduction in customer service and support agents by 2026.

Many call centers already let conversational AI chatbots manage a significant volume of customer interactions, reducing the need for additional agents and lowering overhead costs.

Say a chatbot handles 50% of all troubleshooting calls. That would allow your call center to reduce staffing needs by a similar percentage, leading to significant cost savings on salaries, training, and call center infrastructure.

4. Collect customer data

Chatbots can gather valuable customer data, which can then be used for market research, product development, and personalized experiences.

For example, a chatbot can ask about the customer’s preferences, budget, and specific requirements during a sales inquiry and personalize product recommendations based on this information. It can also track browsing behavior and interactions to understand customer interests and needs.

5. Boost scalability

A chatbot can handle sudden spikes in call volume, ensuring consistent customer service levels without significantly increasing the number of human agents and operational costs.

It can scale up and down to manage more interactions during peak periods and fewer in off-seasons, so you don’t have to struggle with recruiting and onboarding new agents during peak periods only to lay them off in the quieter seasons.

7 Call Center Chatbot Best Practices

Here are a few things to remember when integrating a chatbot solution into your call center operations:

1. Start small

Starting small when implementing a call center chatbot can help you manage the integration process more effectively. For example, instead of deploying the AI bot across your entire call center operations, you can start with pilot use cases like answering frequently asked questions (FAQs) or handling password resets.

This approach allows you to test the chatbot’s functionality and address workflow challenges without overwhelming your resources. You can then gradually expand the chatbot’s capabilities and ensure a smooth transition for your support team and customers.

2. Build a solid knowledge base

A knowledge base is the repository of information from which the chatbot pulls accurate and relevant responses to customer inquiries. Without it, your chatbot might provide misleading information or spend too much time looking for answers to customer issues.

You can create a knowledge base using customer service software like Help Scout or note-taking apps like Slite or Notion. What matters most is that you control, review, and update the information regularly so your chatbot can always access correct data.

3. Leverage AI

Use artificial intelligence to automate self-learning processes and help the chatbot improve its responses over time. For example, you can set up reinforcement learning to allow the chatbot to learn from its actions. The chatbot receives feedback from customers or your support team (positive for correct answers and negative for mistakes) and uses that to improve future responses.

You can also use advanced natural language processing (NLP) techniques like word embedding to help the bot interpret variations in language. Or you could implement AI techniques like entity recognition and information extraction that allow the chatbot to pull relevant new data from user interactions, company updates, or external sources.

4. Include a human in the loop

Human-in-the-loop is a machine learning technique that allows customer service agents and chatbots to work together. The chatbot operates autonomously, but call center agents can step in when needed, typically for quality control, training, or handling more complex tasks that AI can’t handle alone.

There are multiple ways to set this process up. For example, a human can review and approve generative AI responses before sending them to the customer to ensure accuracy, especially for important or high-risk inquiries. You can also program the chatbot to flag conversations and request a human touch when it encounters a difficult question or situation.

5. Test the customer service chatbot before deployment

Thoroughly test the chatbot in various scenarios before launching it. Here are the key things to look out for in the testing phase:

• Does the chatbot provide accurate and relevant responses to inquiries?
• Are there any bugs, glitches, or logic errors in the chatbot’s responses or behaviors?
• Is the chatbot intuitive and easy to use?
• How well does the chatbot understand natural language, including varied sentence structures and slang?
• Does the chatbot perform well under high traffic or heavy usage?
• How well does the chatbot integrate with data systems like your CRM?

6. Monitor and analyze performance

Continuously track chatbot performance metrics, like missed chats and human takeover rate, to understand how well it meets agents’ and customers’ expectations. This insight will help you make data-driven adjustments for better performance.

For example, if your AI-powered chatbot has a high number of missed utterances, it might be struggling to understand customer inquiries. In that case, you can invest in advanced NLP systems to help the chatbot understand user inputs and interpret language variations better.

Track these key performance indicators (KPIs) to measure how well chatbot conversations benefit both your agents and your target audience:

• Customer satisfaction score (CSAT): Shows how customers rate their chatbot experience through short post-interaction surveys to understand whether your chatbot design meets user needs and is user-friendly.
• First call resolution (FCR) rate: Measures how often the chatbot resolves user queries without follow-up.
• Containment rate: Monitors the percentage of conversations fully handled by the chatbot without agent involvement. A higher containment rate indicates a more successful chatbot that optimizes conversation flow and response times.
• Average handle time (AHT): Indicates how chatbot usage affects the time agents spend on transferred calls. A high-performing chatbot should reduce AHT by handling simple queries.
• Conversion rate: Measures how well the chatbot achieves desired outcomes such as appointment bookings, service registrations, or purchases—particularly important for e-commerce and lead generation.
• User engagement rate: Measures how frequently and willingly customers interact with the chatbot during support sessions across various communication channels.

7. Keep an eye on future technology

Staying informed about not just AI advancements but also upcoming customer service trends keeps your contact center productive, competitive, and future-proof. Watch out for the chatbot developments:

• Hyper-personalization: AI is customizing conversations based on customer history, preferences, and real-time context, improving conversation flow and meeting different user needs.
• Voice biometrics: Many businesses are adopting voice authentication for secure, frictionless verification during calls and chats across communication channels.
• Predictive analytics: AI call center chatbots are anticipating customer needs based on past interactions to offer proactive solutions before customers reach out.
• The rise of ‘super agents’: Agentic AI is offering support reps with real-time insights, recommended actions, and customer context to improve resolution quality and shorten response times.

Chatbot Implementation Challenges to Keep in Mind

Integrating a chatbot into your call center doesn’t immediately translate into smooth operations. You might struggle with the following growing and long-term pains:

1. Misinterpretation of customer intent

It might be difficult for a chatbot to always understand and respond correctly to customer inquiries. Why? Because it’s not human. No matter how much data it’s trained on, it can never fully grasp human language, experiences, and emotions because these things are dynamic.

Some chatbots rely on rules or keyword matching to understand input, which can lead to misinterpretation if the customer uses synonyms, slang, or nonstandard phrasing. Also, the chatbot may not understand the tone or sentiment behind a customer’s message, leading to misunderstandings when customers are sarcastic, frustrated, or emotional.

Imagine a customer has been waiting a long time for a response. When they finally get one, they might answer with a sarcastic message like “Thank you for the quick response.” The chatbot might misunderstand this and take it literally.

How to overcome this:

• Leverage advanced NLP models (like GPT) for handling complex or open-ended queries.
• Incorporate feedback loops where customers can rate or correct the chatbot’s responses to fine-tune its understanding.
• Set a confidence threshold that only provides answers if the chatbot is confident in the answer. Otherwise, ask for clarification or escalate to a human agent.

2. User resistance

About 60% of respondents in a Userlike survey said they’d rather wait in line for a live agent than have a chatbot resolve their issue immediately. They believe a human agent offers more personalized support, protects sensitive information, and promptly resolves complex problems.

If your customers are hesitant or unwilling to engage with chatbots in your call center, it will slow down the adoption of the technology.

How to overcome this:

• Be transparent about what the chatbot can and cannot do. Clearly communicate its purpose, whether it’s customer service, FAQs, or something else.
• Ensure that customers can easily switch to a human agent when needed. This hybrid approach builds trust in the chatbot’s capabilities while providing a safety net.
• Offer demos or tutorials to show how easy and effective chatbot interactions can be.

3. Technical integration issues

Deploying an AI chatbot is one thing; integrating it to work smoothly with the rest of your call center operations is another.

Your call center likely relies on numerous business applications, such as CRM software, customer data platforms, VoIP phone systems, and other communication tools. If the chatbot cannot smoothly integrate with these existing tools, it can create bottlenecks that disrupt your call center workflow.

How to overcome this:

• Choose a chatbot provider like Nextiva that offers robust deployment and integration support.
• Ensure the chatbot platform is compatible with the system you’re integrating, whether it’s a website, app, CRM, or other software.
• Run test cases to simulate various customer interactions and integration points to ensure everything works smoothly.

4. Ethical considerations and data privacy

With the increasing use of chatbots, ethical practices and data security must be a top priority. To maintain customer trust, protect your brand, and comply with regulatory requirements, focus on these key areas:

• Data security: Encrypt sensitive information and restrict access to protect customer information. Stay compliant with regulations like HIPAA in healthcare, GDPR, CCPA, and more that apply to your call center.
• Transparency: Let customers know when they’re interacting with a chatbot. Give easy access to connect with human support.

Use chatbots to handle repetitive, low-value tasks and leave complex or emotionally sensitive conversations to human agents rather than replacing staff entirely.

Tips To Choose the Right Chatbot Platform

The right chatbot platform should align with your customer service goals and be flexible enough to support the communication channels you use. Consider these factors when choosing a platform that offers smooth integration, secure operation, and a chatbot experience that meets your business needs:

• Integration capabilities: Easily connects with your CRM, VoIP, helpdesk, and other business systems for optimized workflows.
• Scalability and performance: Handles your current workload and scales even during peak times without compromising response times.
• Customization options: Offers flexible chatbot design features that match your brand’s tone, language, and workflows, with an intuitive user interface.
• Security features: Offers strong encryption, role-based access, and compliance with data privacy regulations.
• Vendor support and training: Provides clear onboarding resources, documentation, and ongoing support for maintenance and troubleshooting.

A good chatbot platform reduces operational headaches, improves user experiences, and supports your long-term chatbot strategy.

Scale Your Contact Center With Nextiva AI Chatbot

A chatbot solution like Nextiva can mitigate implementation challenges and ensure the best possible outcome for your call center, agents, and customers.

Nextiva’s AI chatbots are self-learning and capable of human-like interactions at scale. You can integrate them into multiple customer journey channels, providing on-time, omnichannel support no matter where your customers are.

Learn more about how Nextiva AI improves your call center operations. 👇