Assessing Your Chatbot's Efficiency Using Critical Performance Indicators
Businesses are increasingly implementing chatbots to enhance user interactions and leverage digital marketing opportunities. These artificial-intelligence and machine-learning powered tools offer instant responses and perform various micro-tasks for humans daily. According to estimates, over 80% of businesses are expected to deploy chatbots by 2020.
However, a common concern among businesses is determining the efficiency of their developed chatbots. The following metrics can help evaluate the performance of a chatbot system:
- Activation rate: This refers to the percentage of users who engage with the bot after the initial message, demonstrating their interest and willingness to interact further.
- Average session duration: This measures the time period the chatbot interacts with a user, with shorter timeframes for service-oriented bots and longer ones for interactive bots like shopping or storytelling assistants.
- Session per user: This metric indicates the number of interactions per user, reflecting the bot's inability to provide satisfactory answers in a single session or user interest in the product or service.
- Voluntary User Engagement: This shows the number of times users interact with the chatbot without any prompting, signifying high levels of interest and engagement.
- Retention rate: This measures the return rate of users to the chatbot over a specific time frame, crucial for chatbots serving purposes such as fitness or daily updates.
- Goal Completion Rate (GCR): Every chatbot's primary function is unique. If the assigned goal is not met, despite high conversation levels, the chatbot cannot be considered efficient.
- Revenue growth: The financial gain resulting from client conversion and cost savings on maintaining a customer service team signifies the chatbot's performance and value addition to the business.
- Confusion rate: High confusion rates mean lower user experience, necessitating more training efforts for the chatbot.
- Human Fallback rate: This indicates the number of times the chatbot had to involve a human agent for customer service, signifying poor performance in handling conversations.
- Conversion sentiment: This evaluates user responses towards the chatbot, determining if they are satisfied and engaged or not.
- Artificial Intelligence (AI) and Machine Learning rate: A chatbot with higher AI and machine learning capabilities will offer superior services to users, increasing engagement and adding value to the business.
Adopting the right metrics to evaluate chatbot performance is essential for optimizing it for delivering outstanding user experiences and increasing business profits.
- To effectively measure the efficiency of a machine-learning powered chatbot, businesses can monitor the Artificial Intelligence (AI) and Machine Learning rate, as a higher level of these capabilities will likely lead to superior services for users, resulting in increased engagement.
- By assessing the Activation rate and Voluntary User Engagement, businesses can gauge the level of user interest and eagerness to interact further with the technology-driven chatbot, which is crucial for delivering outstanding user experiences and driving digital marketing opportunities.
