List of Widgets
Last updated
Last updated
On the Dashboard page, users have the capability to create, edit, and remove multiple dashboards. A dashboard serves as a centralized platform where users can curate a collection of widgets tailored to their specific needs. These widgets encompass a diverse range of data visualizations, such as charts, graphs, and tables, selected by the user to provide insights into various aspects of their operations or interests.
When a user accesses a Dashboard, provided they have the necessary privileges, they should encounter a "SWITCH TO EDIT MODE" button. Clicking on this button grants them the ability to modify the dashboard's content and settings.
Once in edit mode, users can edit the existing widgets in the Dashboard and/or incorporate new visualizations by clicking on the "ADD NEW VISUALIZATION".
The widgets are organized into distinct categories based on their respective scopes.
This Line Chart depicts the influx of new users engaging with the chatbot for the first time. Users can fine-tune this widget by applying filters based on a specified Time Range. Additionally, they have the flexibility to group the data by Channels and/or bots, allowing for a more granular analysis of user interactions across different communication channels or chatbot instances.
This Line Chart showcases returning users who have previously interacted with the chatbot and are now revisiting after an 8-hour lapse since their last interaction.
As for the new users, this widget allows filtering by Time Range and/or channels and bots.
The Line Chart displays the total number of outbound messages sent by an account within a specified period of time. This visualization provides valuable insights into communication activity and outreach efforts conducted by the account over time.
The Line Chart showcases the quantity of messages sent by users to your bots within a designated timeframe. This visualization serves as a vital metric for assessing user engagement and interaction with your chatbot platform. By monitoring the volume of incoming messages, users can gauge the level of user activity, identify peak periods of interaction, and detect trends in user communication behavior.
This Counter provides an overview of the cumulative number of outgoing messages sent by an account within a specified time frame. This metric is essential for assessing the overall volume of communication activity conducted by the account over time. By tracking the total number of outgoing messages, users can evaluate the effectiveness of their communication strategies, monitor messaging trends, and measure the scale of their outreach efforts.
The Counter presents the aggregate number of new users acquired by a system or platform within a defined period. This metric offers a holistic view of user growth over time, serving as a fundamental indicator of platform expansion and adoption.
The Counter presents the returning users to the bot after an 8-hour lapse since the last conversation session provides a comprehensive tally of users who have re-engaged with the bot after a specified period of inactivity. This metric is crucial for assessing user retention and re-engagement strategies, as it indicates the effectiveness of the platform in rekindling user interest over time.
The Counter represents the cumulative number of assigned Tags within conversations.
The Pie Chart offers a breakdown of the number of outgoing messages sent by an account across various channels within a specified time frame. This metric provides valuable insights into communication distribution and channel-specific engagement. By dividing the total count of outgoing messages by channel, users can assess the effectiveness of their messaging strategies across different communication channels such as: SMS, Whatsapp, Telegram, Social media and etc...
The "Goal Reached" metric is the attainment of a predefined goal. This metric serves as a key performance indicator (KPI) for tracking progress towards specific objectives or targets.
The "Fallback Replies" metric denotes the total number of fallback replies sent by the bot in response to user inputs that the system couldn't adequately process. Fallback replies are triggered when the bot encounters queries or commands it doesn't understand or can't fulfill based on its programmed capabilities. Monitoring the frequency of fallback replies provides insights into the bot's performance and areas where it may need improvement or additional training data.
The "NLP Detection" metric represents the total number of successful natural language processing (NLP) detections performed on user inputs by the system. This metric indicates the system's ability to accurately interpret and understand user queries or commands through NLP techniques. Monitoring the frequency of successful NLP detections provides insights into the effectiveness of the system's language understanding capabilities and its ability to extract meaning from user inputs.
The "NLP Missed Detections" metric quantifies the number of instances where the system failed to accurately detect or understand user inputs using natural language processing (NLP) techniques. These missed detections occur when the system fails to interpret user queries or commands correctly or doesn't recognize their intended meaning.
The "Total NLP Detections and Fails" metric encompasses both successful NLP detections and missed detections (fails) on user inputs. It calculates the overall percentage of NLP detection accuracy by comparing the number of successful detections to the total number of user inputs processed by the system.
The "Average Conversation Length" represents the average duration of conversations between users and the system, typically measured in minutes. This metric provides insights into the duration of user engagements and interactions with the system.
The "Take Overs" metric refers to the number of conversations that were taken over by a human operator during a specific period of time. This metric is important for understanding the level of human intervention required in handling user interactions and providing support or assistance when needed.
The "Average Support Conversation Length" represents the average duration of conversations specifically related to support interactions, typically measured in minutes. This metric provides insights into the average amount of time spent addressing support-related issues or inquiries.
The "Average Time to First Message" in the context of a taken-over conversation refers to the average duration it takes for a human operator to respond to a user after taking over a conversation. This metric helps gauge the responsiveness of operators in addressing user inquiries or issues during taken-over conversations.
The "Average Conversation Pickup Time" refers to the average duration it takes for an operator to take over or pick up a conversation request after it becomes available for handling.
The "Agent Performance on Availability" metric provides a visual representation of an agent's availability over time, typically displayed in a calendar view. This visualization helps monitor and assess when operators are available to handle conversations and support requests and allows you to view the availability and activity status of operators, making scheduling and coordination more precise.
The "Operators Messages" metric refers to the total number of messages sent by operators during support interactions. This metric provides insights into the level of engagement and communication between operators and users during support sessions.
The "API Success and Failures" widget provides a comprehensive overview of the number of API requests made by bots, effectively split between successful and failed requests. This detailed breakdown offers valuable insights into the performance and reliability of your API interactions.
The "API Requests" widget is a crucial tool for monitoring and analyzing the number of API requests made by bots. This widget provides detailed insights into the activity and performance of your bots as they interact with your API.
The "API Errors" widget provides a detailed table visualization of API errors, offering a comprehensive view of the issues affecting your API interactions. This widget is designed to help you quickly identify, analyze, and address errors, improving the overall reliability and performance of your API.
The "Script JS Errors" widget offers a detailed table visualization of JavaScript errors, providing a comprehensive view of issues affecting your web applications. This widget is designed to help developers quickly identify, analyze, and resolve JavaScript errors, improving the overall performance and reliability of your website.
The "Account Bots" widget provides a comprehensive overview of the number of bots currently in use based on the bots available for your plan. This widget is essential for managing bot resources effectively and ensuring optimal utilization within your subscription limits.
The "Sent Messages" widget provides a detailed overview of the number of messages sent by all your bots within the current month. This widget is a vital tool for monitoring bot activity and ensuring effective communication management.
The "Storage" widget provides a comprehensive overview of the total number of gigabytes used by your bots and media items associated with conversations, such as videos, images, and other multimedia content. This widget is essential for managing storage resources effectively and ensuring the seamless storage and retrieval of media assets.
The "NLP Intents" widget provides valuable insights into the number of natural language processing (NLP) intents used by all your bots, based on the total amount of intents included in your plan. This widget is essential for monitoring and optimizing the usage of NLP capabilities across your bot ecosystem.
