Almost half of customer support teams are now incorporating AI into their operations. They now use tools like AI chatbots and AI copilots.
These intelligent systems can enhance efficiency, provide instant support, and deliver personalized experiences, reshaping how businesses interact with customers.
AI chatbots handle routine inquiries seamlessly, freeing up human agents for complex issues, while AI copilots assist support teams by suggesting responses and improving decision-making.
Together, they create a support framework that is faster, more responsive, and cost-effective.
However, there are some foundational steps to follow when implementing AI in customer service:
- Set clear objectives and use cases: Understand precisely how AI will support your customer service strategy, including which situations you plan to address, the impact on customers, and how it will affect your team.
- Define baseline performance metrics: Consider what your customers expect and the complexity of your product. AI is highly effective at handling repetitive and simple queries but is less suited for complex troubleshooting. AI may assist in complex support, but the success metrics might need adjustment.
- Maintain an accurate knowledge base: To reduce AI inaccuracies, invest in a well-maintained knowledge base. AI chatbots are easier to train with a strong foundation of content.
- Implement smooth escalation protocols: Ensure a seamless transition from AI to human agents, enhancing the customer experience and preventing wasted time with the bot.
- Collect and utilize feedback: AI often requires more context than human agents, and it might miss implicit connections. Documentation and regular updates in training data are essential.
Feedback collection methods
Start with a few questions to automate and observe where misunderstandings occur in actual customer interactions. Customer feedback quickly reveals knowledge gaps and preferences, helping you refine chatbot responses.
- Quality Assurance (QA): Have a dedicated team rigorously test the AI’s responses, identifying areas for improvement before launch.
- Surveys: Use post-interaction surveys, such as CSAT and CES, to gather direct insights on user satisfaction.
- Manual analysis: Your support team can periodically review chatbot conversations to detect issues and areas for improvement that automated systems may overlook. Often, teams begin with this approach and later monitor only certain cases, like those with negative feedback.
- Automated feedback tools: Employ AI-based analytics to handle large volumes of interactions, categorize issues, detect patterns, and highlight areas needing attention. This requires its own training but is valuable for high data volumes.
Building a Reliable Chatbot Implies a Strong Feedback Loop
The main steps to create an effective feedback loop for a chatbot are straightforward:
- Collect feedback from a variety of sources.
- Apply that feedback by updating help resources or providing extra training for the chatbot.
- Test the chatbot to confirm it can handle the scenario better.
Handling larger data volumes or coordinating across multiple team members can add complexity.
At this point, it’s important to prioritize feedback and ensure the team stays aligned and consistent. Focus on these four core areas when addressing feedback.
Gathering Feedback from the Support Team
Whether or not you’re using a model like Knowledge-Centered Service (KCS), frontline agents are key for AI feedback.
KCS systems encourage teams to share, capture, and flag information regularly, making it easier to apply that approach to AI chatbot answers.
Regardless of your system, it’s crucial to have a method for agents to flag chatbot answers. Integrating this into their workflow might take a few weeks but will help improve the chatbot faster by drawing on agents’ expertise.
A categorization system (e.g., incorrect info, misunderstood query) can also aid in prioritizing feedback.
Additional ways to collect insights:
- Encourage agents to propose new responses as part of their work.
- Track patterns in conversations routed or escalated to the team.
With this setup, agents can correct chatbot responses immediately when issues are found, which requires consistent team communication, possibly through a dedicated channel or meeting.
Acting on Feedback for Help Articles
Strong knowledge management ensures high-performing chatbots since outdated information is quickly corrected.
Duplicate or repeated flags from agents waste time, so tools like Zendesk’s Help Center Manager allow for quick feedback applications, preventing the chatbot from making recurring mistakes.
Such tools can also facilitate bulk updates (like replacing UI terms, fixing links, or enabling automatic translations).
To maintain quality:
- Prioritize article updates based on feedback frequency and impact, starting with those the chatbot references most.
- Implement a version control system for articles to simplify tracking changes.
- Monitor changes’ effects on customer satisfaction—tools like Help Center Manager allow helpfulness tracking.
- Conduct regular audits to remove outdated content. Feeding the chatbot multiple data sources can be tempting, but some content may mislead it; for instance, change logs or feature request lists may imply nonexistent features.
Using Reports for Improvement
Initially, manually review each chatbot interaction, but once you reach a high accuracy (around 90%), consider creating reports to address the remaining inaccurate responses.
To manage improvements:
- Set up dashboards that highlight negative ratings or frequent responses as indicators for chatbot refinement.
- Use sentiment analysis on customer feedback to identify urgent cases and set up protocols to handle them proactively.
- Develop weighted scoring for prioritizing issues; an automated spreadsheet can identify the most pressing items.
- Establish thresholds for critical issues that the chatbot can flag for quick escalation to your team, simplifying the escalation process.
What Are Copilot Tools?
Copilot tools are AI-driven solutions that boost the productivity of customer service agents.
Known as “agent assist” tools, they provide real-time support, automate repetitive tasks, and deliver intelligent recommendations to aid agents. These tools aren’t customer-facing; their purpose is to support agents.
Key features typically include:
- Real-time response suggestions, often drawn from macros or historical help desk data.
- Automating repetitive tasks, often via APIs, such as processing refunds or updating customer details.
- Adapting responses, like expanding bullet points into emails or adjusting the tone of replies.
- Summarizing ticket information to help agents quickly get up to speed.
Essentially, copilot tools help agents work faster by making information easily accessible and simplifying routine tasks.
What is Zendesk Agent Copilot?
Zendesk Agent Copilot is Zendesk’s built-in agent-assist feature, aimed at reducing agent time on repetitive tasks and allowing focus on complex interactions. Its approach is simple:
- It proactively suggests replies and actions.
- These replies are predefined by Zendesk admins, allowing agents to accept, edit, or send them directly.
Currently, Zendesk Agent Copilot is limited to customers with the Advanced AI add-on. It focuses primarily on:
- Customers with intent predictions enabled, suitable for specific industries based on Zendesk’s trained data.
- E-commerce customers using Shopify who want Copilot for tasks like order cancellations and refunds.
Zendesk plans to expand to other sectors and use cases, but currently, the scope is limited. Though focused integration enhances reliability and security, there are also some downsides:
- Zendesk Agent Copilot operates separately from existing help desk content. Integrating it with Zendesk AI agents could reduce training time.
- Existing macros or historical help desk data could also be used to train Copilot, and linking internal documentation to it could improve response quality.
Alternative Copilot Tools on Zendesk
Agent assist or copilot tools are rapidly gaining popularity. Below is a selection of alternative copilot tools available on Zendesk worth exploring.
Stylo Assist
ChatGPT powers Stylo Assist. Its main features include:
- Drafting replies: Utilizes the knowledge base, past tickets, and macros to customize responses.
- Language translation: Allows messages to be translated to and from any language.
- Standardized agent tone: Helps maintain a uniform tone across interactions, though this may reduce personalization in some cases.
- Summarizing tickets: Condenses ticket information, which is particularly useful for long tickets.
- Researching answers: Pulls relevant resources to aid agents in verifying responses.
- Shopify integration: A common feature in AI tools, helping automate responses related to order status.
- Generating responses via help articles: Enables agents to choose an article and generate a reply based on it if the AI doesn’t automatically make the connection.
- Advanced spam detection: While Zendesk includes a spam filter, Stylo may offer enhanced functionality.
Stylo is notably more robust than Zendesk Agent Copilot. It has only recently started developing a customer-facing AI tool, so if consolidating tools is important and you also need an AI agent, Stylo may not be the ideal option.
SwiftCX AI Copilot
SwiftCX provides a comprehensive set of AI tools: a chatbot for customer interaction, a copilot tool for agents, and data-driven insights for analyzing customer information.
Its AI Copilot includes many features similar to those in the Zendesk Advanced AI add-on:
- Generates suggested replies based on ticket history, macros, help center content, and external sources like Notion or Google Docs, commonly used for internal documentation by CS teams.
- Analyzes customer sentiment and summarizes tickets, enabling quicker responses or prioritization based on sentiment.
However, these suggested replies can easily become outdated, as they rely heavily on historical data.
This setup makes it essential for teams to update their documentation (e.g., add macros or modify internal help articles) when they notice outdated AI suggestions.
SwiftCX’s website provides little information on pricing or screenshots, making it difficult to understand the training process.
Also, it appears to lack API integration, preventing action-based functionalities. This could be limiting for e-commerce businesses where such integrations are common.
Conclusion
Integrating AI chatbots and AI copilots into customer support is transforming how businesses engage with their audiences, enabling faster, more personalized, and more efficient responses.
As AI technology advances, these tools will only become more effective at understanding complex queries and adapting to customer needs, making them invaluable assets for any forward-thinking organization.