Chatbot quality assurance (QA): Definition, importance & more

Generative AI is breaking swift ground in chatbot capabilities. Its remarkable ability to generate conversations, solve problems, and even produce correct lines of code has opened up a new era in human-machine communication.

But, traditional chatbots often fell short, restricted to handling basic queries, and lacking the finesse to engage in complex, two-way dialogues with users. AI agents can now actually do what it says on the tin: chat.

While we can celebrate customer service progress in delivering more sophisticated and satisfying conversations, it is also crucial to understand the best practices for employing—and improving—chatbots for customer service.

In this guide:

• Common chatbot issues that can be solved with quality assurance
• What is chatbot quality assurance (QA)?
• Why is it important to review bot conversations automatically?
• Perfect the AI agent flow with Zendesk QA

Common chatbot issues that can be solved with quality assurance

60 percent of customers are frequently disappointed with chatbot experiences. It’s not surprising, considering the typical issues with chatbots:

1. Falsified truths

Due to the nature of generative AI, distinguishing between fact and falsehood is often its downfall. This means that your AI agents need to be trained regularly with accurate data and real-time facts that are pertinent for solving customer issues.

2. Question misinterpretation

Ambiguous or unusual questions may cause chatbots to produce incorrect information. This is a problem that must be shouldered by the company, not levied on the customer.

3. Lack of empathy

Customers crave compassion in certain circumstances, and knowing exactly where AI falls short in this regard is pack and parcel of an excellent customer service AI strategy.

All of the above can be solved with advanced AI-powered bots, like Zendesk AI agents, and a robust quality assurance program that works alongside them to augment and perfect the customer experience. Quality assurance for chatbots is essential to ensure reliability, accuracy, and a seamless user experience.

What is chatbot quality assurance (QA)?

Customer service quality assurance (QA) involves reviewing support interactions to ensure that both company and customer expectations are met. For your customer service agents, QA data feeds coaching efforts. When applied to chatbots and AI agents, QA entails thoroughly assessing their responses’ accuracy and quality, ensuring they receive the correct data to improve.

This can be done manually, automatically, or both.

Why is it important to review bot conversations automatically?

Deploying AI requires careful and consistent upkeep, especially when using AI agents to solve problems for your customers. The more data AI is fed, the more efficient and effective its output will be.

One of the most significant benefits of using AI agents in customer service is they alleviate the burden of high ticket volume for your team. What logically follows, though, is a mass of conversation data falling into the bucket for chatbot QA purposes. To review this manually would be time-consuming, more than slightly defeating the point of using AI for time-saving measures.

Hence, automated quality assurance is an ideal match for AI agent deployment.

With AI-powered Auto QA for AI agents, you can automate the evaluation of all AI agent interactions, making it easier to spot important conversations and potential issues early. Zendesk AI-driven tools let you scale QA processes by 50x, while AutoQA flags interactions that need human review, ensuring your AI agents consistently provide top-notch customer experiences.

You can also track customer sentiment by detecting when an AI agent’s response triggers a positive or negative reaction. This helps QA managers assess and improve AI performance. Additionally, combining QA for AI agents with Spotlight lets you catch:

• Repetitive AI responses: Spot instances where the AI agent repeated the same message or question multiple times.

• Inefficient communication: Identify conversations where the AI agent took more steps to resolve an issue compared to human agents.

Perfect the AI agent flow with Zendesk QA

Crafting a well-designed conversation flow means mapping out every possible user journey with the precision of a cartographer. QA plays a pivotal role, where reviews help you stress-test the flow from every angle to uncover pitfalls and adjust accordingly.

To maximize customer satisfaction, first understand how to weigh the technological scales. How much do your customers want their customer support to be automated? Giving chatbots free rein leads to disgruntled customers; not giving them enough can weigh too heavily on your team of skilled customer service agents.

QA ensures that your AI agents evolve into truly responsive and intuitive conversational partners. However, if the chatbot quality assurance process adds increasing responsibilities to your quality team, it defeats the purpose of engaging a chatbot to reduce manual labor.

“Any large language model used generatively can fall prey to hallucination. Essentially, such a model is a massive matrix that computes the probability of each word, then the next word, and the next. The matrix has no cognitive powers, it is simply expert at chit chat.”

Mervi Sepp Rei, Senior Manager, Machine Learning – Zendesk

Teams will miss the mark if their quality processes assess chatbots in line with skilled customer service agents. It is important to decipher human from AI interactions.

Zendesk QA automatically deciphers and segments AI agent conversations from the customer service agent interactions. This lets you analyze their performance independently. Plus, since conversation scoring is automatic, your quality team can focus their time on advanced analytics.

Building a quality process as soon as your support team deploys AI agents leads to better outcomes in the long run. Automating how your team manages quality will save time and instill good data practices, systematically building your AI agent success.