GC Flow Metrics
What are Flow Metrics in Guided Conversations
Flow metrics in GC helps you measure to assess the performance, effectiveness, and efficiency of the chatbot's conversational flows. These metrics provide valuable insights into how well your chatbot is guiding users through interactions and accomplishing its intended goals.
Why Use Flow Metrics for your Flow
- Identify specific issues within the chatbot’s interactions, such as frequent blocks that face frequent drop-offs, enabling you to determine exactly where the bot users are facing problems.
- Once issues are identified, you can make targeted adjustments to the conversational flow. This might include rephrasing questions, adding clarifying prompts, or streamlining steps to make interactions smoother.
- Regularly monitoring and adjusting the flow based on these metrics ensures the chatbot evolves to meet user needs better and expectations.
- Optimized flows ensure that common queries and issues are resolved quickly without the need for human intervention, accelerating the overall support process.
- With a more efficient chatbot handling routine inquiries, human support agents can focus on more complex issues, thereby improving the overall efficiency of support operations.
Metrics can be viewed only for published flows.
Functions of GC Flow Metrics
Total visitor count
The flow's metrics can be analyzed based on user visits to each block and their actions, such as dropping off or navigating a specific path.
For example, Zylker Electronics has implemented a chatbot on its website to assist visitors with product inquiries, provide recommendations, and facilitate purchases.
How does this metric benefit the business in this scenario?
- The chatbot platform tracks each unique user interaction. At the end of the month, the total user count can be analyzed to see how many visitors interacted with the chatbot.
- Based on this metric data, Zylker Electronics can plan improvements, such as adding more product categories or enhancing the flow of the chatbot.
- Monitoring the total number of user visits within the chatbot will help the business scale its reach and user engagement. This metric will also show if the chatbot aligns with the business's goals.
A low count may mean customers aren't aware of the bot or don't know how to use it. To fix this, increase visibility or educate customers on using the bot and its uses.
User-drop Count
This metric indicates the block-wise user visits that had dropped off.
For example, Zylker Electronics has implemented a chatbot to assist users with product inquiries, recommendations, and purchases.
User drop count metric helps them identify where users are dropping off block-wise in the conversation flow and make appropriate improvements to reduce these rates. How does this metric benefit the business in this scenario?
The metric shows exactly where users are leaving the conversation. For example, if the drop-off rate is high during the purchase process, the business can simplify and improve the checkout flow, making it easier for users to complete transactions.
By continuously monitoring drop-off rates, the shop can track the effectiveness of changes and ensure ongoing optimization of the chatbot.
A well-functioning chatbot that provides a seamless experience will enhance customer loyalty and reduce churn.
'Jump to Block' Count
This metric highlights the blocks that users jump to the most in the conversational flow, suggesting they find these parts particularly useful.
Metrics for blocks that are most used by your users through 'Jump block' will also display the name of the flow the block belongs to, if the jump is made from another flow's block.
For example, Zylker Electronics uses a chatbot to assist users with product inquiries, provide recommendations, and facilitate purchases.
How does this metric benefit the business in this scenario?
- This metric reveals which blocks users jump to most frequently, indicating the information or assistance they prioritize. For example, if many users jump to product recommendations, it shows a high demand for personalized suggestions.
- If users frequently jump to the purchase block after receiving recommendations, it indicates that the recommendation system is effective and can be implied on other products as well.
- Jump to block metrics provide concrete data on user behavior, allowing the business to make informed decisions about where to focus improvements.
Scale Bot Performance
Track your bot's performance over time by switching between different ranges of days (7, 15, 30, or 90 days). Each time a flow is edited, a new version is created, and the metrics for each version can be viewed in the flow version history.This allows comparison between different versions' performance, ensuring that the changes made enhance the conversational experience for customers.
Steps to View your Flow Metrics
1. Go to Guided Conversations > GC Flows.
2. Open the builder of a published flow.
3. Turn ON the toggle near View Metrics.
Points to note about flow metrics:
- Metrics are automatically updated after every conversation ends to provide real-time visibility.
- If the flow is deleted, the flow metrics will be deleted as well.
- If the flow is edited, the flow metrics will reset and start fresh.
- You can access the latest 10 versions of the flow and their corresponding metrics through the Version History
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