The goal is not to completely replace human intervention, but to strike a balance where automation takes care of repetitive or simple tasks, freeing your team to focus on the interactions that generate the most value.
By designing your chatbot to seamlessly transition to a human when needed, you ensure that users receive the right assistance at the right time, improving both efficiency and customer satisfaction.
How do I measure my chatbot's contention rate?
To measure your chatbot's contention rate, follow these steps:
1. Track total and escalated interactions
counts the total number of user interactions with the portugal phone numbers chatbot during a given period. This includes all conversations, queries or tasks initiated by users.
Next, track the number of interactions that are escalated to human agents or marked as unresolved by the chatbot.
“Escalations” may include direct transfers to human agents or cases where users explicitly request help.
2. Calculate the containment index
visual representation of the containment index formula.
Use the formula:
containment rate = (1- [scaled interactions / total interactions]) × 100
For example, if your chatbot handled 900 out of 1,000 interactions without escalation, you would calculate:
containment rate = (1-1000/100) × 100 = 90%.
3. Or use analysis tools
Take advantage of chatbot analytics or customer service platforms that automatically track and report metrics like contention rates. These tools often provide additional information about the reasons for escalations and user satisfaction.
By continuously monitoring and optimizing based on this metric, you can improve your chatbot's effectiveness and user experience.
Why is my chatbot's contention rate low?
A low chatbot contention rate usually occurs when the bot has difficulty understanding queries, lacks adequate data, or cannot handle complex tasks. Users escalate their issues when they feel that the chatbot is not meeting their needs.
Here are the most common reasons for low containment rates:
misrecognition of intentions or misinterpreted queries
limited or outdated knowledge base
inability to maintain context in conversations
lack of integration with key data sources
unclear scope of what the chatbot can do
good practices for high-contention chatbots
use llms instead of intent classifiers
High-contention chatbots thrive when powered by LLMS rather than intent classifiers.
