What is Chatbot Containment Rate?

Customer service leaders rely heavily on chatbots, voice bots, and IVRs to automate repetitive queries. The better these technologies perform, the greater the cost savings. This means the performance of chatbots and IVR directly affects an organization’s revenue stream. The containment rate is one of the crucial metrics that indicate the performance of a chatbot, voice bot, or IVR.

Call centers use the same containment rate to measure the percentage of customer interactions resolved by automated systems, such as interactive voice response (IVR) systems, without transferring the call to a live agent.

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How to Measure Chatbot Containment Rate?

Measuring chatbot containment rate is vital for regularly optimizing and improving the performance of the chatbot. The containment rate can be calculated by dividing the number of conversations handled solely by the chatbot by the total number of queries handled by the team, then multiplying by 100.

Here is the formula to calculate chatbot containment rate:

Chatbot Containment Rate (%) = (Number of Interactions Handled by the chatbot / Total Number of Interactions Received) * 100

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Why Chatbot Containment Rate Can Be Misleading?

While chatbot containment rate is a crucial metric for customer service and product leaders, it does not necessarily account for a successful query resolution. The ultimate success of any customer query resolution should be measured by a combination of containment rate, customer satisfaction score, resolution time, and first-contact resolution rate.

Your customers often interact with your chatbot. But not at all instances, do they get the resolution or the answer they are looking for. It is very hard to understand the satisfaction level of your customers until they submit the CSAT score for evey interaction done with the chatbot. But we all know, that’s not how it happens. Customers leave immediately without providing a score for the conversation. That’s why to truly understand the performance of the chatbot, customer service leaders should measure customer satisfaction score, resolution time, and first-contact resolution rate in addition to the chatbot containment rate to truly understand the effectiveness of the chatbot.

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Importance of High Containment Rate

As explained above, containment rate should not be considered the only metric to analyze chatbot performance. Chatbot containment rate is one of the critical metrics that customer support and product managers should consider. Below are some factors that indicate the importance of a high chatbot containment rate and why it is necessary.

• Cost Efficiency
• Reducing the need for human agents allows organizations to allocate resources more efficiently, saving costs on staffing and training.
• Customer Satisfaction
• Quick resolutions lead to higher customer satisfaction, as users receive the help they need promptly without waiting for a human agent. Higher customer satisfaction increases the likelihood that your customers will renew with you.
• Operational Efficiency
• By automating routine inquiries, human agents can focus on more complex issues, enhancing the overall service quality. Support agents are happier with their work when tasked with challenging and critical tasks that impact the organizations revenue, rather than being given easy, repetitive tasks.
• Data Insights
• High containment rates provide valuable insights into common customer queries, helping improve products and services.

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Factors Affecting Chatbot Containment Rate

Most customer support and product managers evaluate chatbot performance solely based on the CSAT score. While the CSAT score is essential for understanding customer satisfaction, other factors affect the performance of the chatbot containment rate.

• Chatbot Design and User Experience
• A well-designed interface and user-friendly interaction flow are crucial for effective containment. Ensuring the chatbot understands natural language and responds appropriately is key.
• Training and Knowledge Base
• The chatbots ability to access and utilize a comprehensive knowledge base determines its effectiveness in resolving queries.
• Contextual Understanding
• Incorporating machine learning and natural language processing (NLP) allows chatbots to understand context better, improving their response accuracy.
• Integration with Backend Systems
• Seamless integration with CRM and other backend systems enables chatbots to retrieve and process information effectively, enhancing their ability to resolve customer issues. For example, Kommunicate’s AI chatbot seamlessly integrates with Zendesk products like Zendesk Chat, Zendesk Sunshine, Zendesk Ticketing, and Zendesk Guide.

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Strategies to Improve Chatbot Containment Rate

• Regular Updates and Training: Continuously updating the chatbot’s knowledge base and retraining it with new data ensures it stays relevant and accurate.
• Analyzing Failed Interactions: Regularly analyzing interactions that require escalation to human agents helps identify areas for improvement.
• Feedback Loops: Implementing feedback loops where users can rate and provide feedback on interactions helps refine the chatbots performance.
• Personalization: Customizing interactions based on user data and preferences can improve engagement and resolution rates.
• Hybrid Approach: Implementing a hybrid model where chatbots and human agents work collaboratively can improve overall containment. The chatbot can handle routine queries, while complex issues are seamlessly transferred to human agents.

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Measuring and Monitoring Containment Rate

Implementing robust analytics tools to monitor the containment rate is crucial. Key metrics to track include:

• First Contact Resolution (FCR)
• Measures the percentage of queries resolved on the first interaction.
• Escalation Rate
• Tracks how often interactions are transferred to human agents.
• Customer Satisfaction Scores
• Provides insights into the quality of interactions and user satisfaction.

Optimizing the chatbot containment rate is essential for enhancing customer service efficiency and satisfaction. By focusing on continuous improvement and leveraging advanced technologies, organizations can ensure their chatbots deliver value and meet the ever-evolving needs of their customers.

As customer service leaders and product managers, understanding and implementing these strategies will be crucial to unlocking the full potential of AI chatbots and staying ahead in the competitive landscape.