Monitor Chatbot Trends for Better User Interactions

Get a clear view of your chatbot's performance over time and make data-driven decisions to enhance efficiency.


Navigate the intricacies of chatbot communication with our Monitoring page, where data visualization meets conversational intelligence. This section of our platform presents a streamlined chart that captures the pulse of your chatbot's message exchanges, plotted against time. With the ability to filter by date, you'll gain precise control over the analysis period, empowering you to make informed decisions for chatbot optimization.

Accessing the Monitoring Page:

To view your chatbot's messaging analytics, follow these quick steps:

  1. Log In: Sign into your account with the necessary credentials.
  2. Main Workspace: Locate the monitoring section in the left-side navigation bar.
  3. Select Monitoring: Click on the 'Monitoring' menu item.
  4. Analyze Data: You'll be taken to the chart view where you can start analyzing message trends.

For any access issues, refer to the user guide or contact support.

Understanding the Chart:

The chart on the Monitoring page is a visual representation of your chatbot's messaging activity over time, designed to provide you with actionable insights into its performance. Here's how to interpret the chart:

  • Chart Layout: The chart is a line or bar graph that displays the frequency of messages exchanged between the chatbot and users.

  • Y-Axis (Message Volume): The vertical axis represents the number of messages. Each point or bar height correlates with the volume of messages sent or received during a specific timeframe.

  • X-Axis (Timeline): The horizontal axis shows time, which could be in hours, days, weeks, or months, depending on your selection. This timeline allows you to track fluctuations and trends in engagement.

  • Data Points: Individual points or bars on the chart correspond to the message volume at a particular time. Hovering over these can often reveal additional details such as exact numbers or percentages.

  • Trends: By observing the chart, you can identify patterns such as peak times of activity, lulls in engagement, or the impact of external events on chatbot usage.

Understanding this chart equips you with the knowledge to make informed decisions about your chatbot's operation and to tailor your strategies for improved user interaction.

Filtering the Data

To refine your analysis and focus on specific periods of interest, the Monitoring page provides robust data filtering options. Here's how to filter the data on the chart:

  • Date Range Selection: Typically, there will be a date picker or a set of predefined date range options. You can select a custom range or choose from options like 'Today', 'Last 7 days', 'Last 30 days', etc.

  • Applying the Filter: After selecting your desired date range, apply the filter to update the chart. The graph will adjust to display only the data from the specified period.

  • Resetting Filters: If you wish to return to the default view or select a new range, look for a 'Reset' button or simply modify the date range selection and reapply.

By effectively using filters, you can drill down into the data that matters most, allowing for a targeted analysis of your chatbot's interactions and the ability to uncover deeper insights.

Interpreting the Results

Once you have navigated the chart and applied the necessary filters, interpreting the results is crucial for optimizing your chatbot's performance. Here's how to make sense of the data presented:

  • Identify Patterns: Look for consistent trends in the data, such as peak usage times or days with higher message volumes. These patterns can inform you about user behavior and preferences.

  • Analyze Peaks and Valleys: Notice any spikes or drops in the chart. Peaks may indicate successful engagement or promotional periods, while valleys could highlight times when your chatbot is underutilized.

  • Compare Timeframes: Use the date range filter to compare different periods. This can reveal the effects of changes you've made to the chatbot or external factors influencing user interaction.

  • Understand User Engagement: High message volumes could signify good engagement, but also consider the context. Are there many unresolved queries or negative feedback messages?

  • Actionable Insights: Translate your findings into action. For example, if you notice a recurring question that leads to high message volumes, consider updating your chatbot's knowledge base to address it directly.

Interpreting the results from the Monitoring page is not just about observing data; it's about understanding the story behind the numbers and using that knowledge to make informed decisions for continuous chatbot improvement.