Sessions
This report analyzes user sessions in the web application, displaying interaction patterns, usage time, and visited sections, helping to identify areas for improving the user experience.
All charts include an interactive legend that allows dynamic data exploration. Clicking on a legend item temporarily hides the corresponding values, making it easier to analyze other categories.
Sessions
The purpose of this chart is to visually display the volume of user sessions in a tenant over time, highlighting their temporal distribution. It allows analysis of usage patterns and trends based on the channels used, which is useful for identifying activity spikes or drops related to specific events.
Data Interpretation
X-Axis (Dates or Timestamps): Represents dates or timestamps. Each bar corresponds to an interval (date or hour) based on the selected range in the filters.
Y-Axis (Number of Sessions): Indicates the total number of sessions recorded in the tenant. The height of each bar reflects the session volume, facilitating comparisons across different days or hours.
Legend
The chart includes an interactive legend to aid interpretation:
- Bars: Represent the total number of sessions, segmented by channel to identify the origin of the interactions.
- Lines: Indicate the number of sessions transferred to Cat (handled by a human agent), also broken down by channel.
Chart Usefulness
- Identify Spikes: Peaks in the bars may signal events or conditions that increased activity.
- Channel Analysis: The proportion of segmented sessions helps evaluate each channel’s performance.
- Tracking Transfers: The lines help determine what percentage of sessions required agent intervention, aiding in resource allocation.
Use Case
Session Volume by Tenant
Description: A customer service team wants to adjust agent availability based on peak times or high-demand channels.
How to Use the Chart:
- Detect Spikes: Correlate periods of high activity with specific campaigns or events.
- Analyze by Channel: Evaluate which channels generate more sessions and how they contribute to the overall volume.
- Agent Transfers: Identify the proportion of sessions requiring human interaction to optimize staffing strategies.
Session Lifetime
This chart aims to show how the average session lifetime varies across different channels over time. It allows comparison of session duration by channel, identification of patterns or behavioral changes, and informed decision-making to improve efficiency and resource management based on interaction length.
Data Interpretation
- X-Axis (Time): Shows how session duration varies over time, helping to identify fluctuations or changes in interactions.
- Y-Axis (Average Session Lifetime): Displays the average time that sessions remain active, allowing comparisons of interaction duration across different channels.
A high value on the Y-axis for a specific channel suggests longer interactions, which could indicate more complex processes or more detailed attention.
A low value indicates faster interactions, possibly due to simpler or less in-depth sessions.
Use Case
Customer Support Optimization Across Channels
Description: A telecommunications company offers customer service through various channels like live chat, social media (e.g., X), email, and phone. The company wants to understand how average session lifetime varies by channel to identify opportunities to optimize support processes.
The chart helps the company analyze differences in customer support session duration across channels. The goal is to identify whether any channels have abnormally long or short session times, potentially signaling efficiency issues or improvement areas.
Chart Interpretation:
- X-Axis (Time): Shows data trends over the past 6 months, helping identify peaks or drops over time.
- Y-Axis (Average Session Lifetime): Shows the average duration of sessions per channel.
Scenario:
- Social Media shows a sharp drop in average session lifetime over the last month, more pronounced than in other channels.
- Interpretation: This could indicate users are receiving faster—but potentially lower-quality—responses. The company might be using more automated replies, which speeds up session closure but reduces depth. Investigation may be needed to assess customer satisfaction.
Total Sessions
This pie chart shows the percentage of total sessions originating from each channel during a specific period, making it easy to identify the most and least used channels. It also helps observe changes in channel popularity over time, aiding decisions on how to allocate resources or improve service on high- or low-activity channels.
Data Interpretation
The pie chart consists of several sections, each representing a specific support channel:
- Each section shows the percentage of total sessions generated by a channel during the selected period.
- The size of each section is determined by the number of sessions from that channel compared to the total. The more sessions, the larger the section.
- Each channel has a distinct color for easy visual identification.
- The chart includes a legend showing which channel corresponds to each color, helping to quickly identify session proportions.
Use Case
Evaluating Customer Support Channel Efficiency
Description: A company offering customer support via various channels (phone, email, social media) wants to optimize its resources to enhance customer experience.
Chart Interpretation:
- Analyze Most Used Channels: The company checks the pie chart showing total sessions by channel for the past month. It notices that the phone channel accounts for a significant portion of sessions, while social media has a smaller share.
- Make Informed Decisions: With the chart, the support team sees that the phone channel generates the most interactions. This suggests the need to increase capacity for handling phone requests, while the social media team might remain unchanged.
- Plan Adjustments: Based on chart trends, the company prioritizes training and hiring for the phone channel. It also plans regular chart reviews to adjust resource allocation as customer interaction patterns evolve.
- Measure Impact: At the end of the next quarter, the company uses the same chart to evaluate whether resource reallocation improved efficiency and customer satisfaction. Improved response times and higher overall satisfaction confirm the strategy's effectiveness.
Session List
This table provides a detailed log of user interactions, helping to analyze session origin, duration, and other key data. It supports evaluating service efficiency, identifying issues, and optimizing resources.
Data Interpretation
Each session includes the following information:
- Creation: Date the session was initiated.
- Session ID: Unique identifier to track interactions.
- Criteria: Classification based on a user identifier.
- Criteria Value: Specific value of the used criteria.
- Channel: Channel where the interaction session originated (Teams, WhatsApp, Telegram, etc.).
- Service: Service number associated with the channel that processed the session.
- Division: Service identifier associated with the channel.
- Creation Date: Exact date and time the session started.
- Duration in Seconds: Total duration of the interaction.
Additional Session Views:
- Dialogs: Shows the conversation history in a chat-like format.
- Debug: Details technical logs and errors.
- Health: Indicates the overall session status (success, issues).
Each column includes filters to search and organize data efficiently.
Dialogs
This view shows the conversation details during the session. On the right, the dialogs appear in a chat-like format. Clicking a specific dialog reveals a key-value table with detailed interaction information.
- conversationPart: Indicates who is participating in this part of the conversation (Client-Bot-Agent).
- text: Displays the message sent by the conversationPart.
- options: Options the system offers the client in response, if the bot’s reply entity is of type menu.
- date: Shows the exact date and time the conversationPart sent the message.
- currentIntent: Intent being executed in the current interaction.
- previousIntent: Intent executed prior to the current interaction.
- confidence: Indicates how confidently the cognitive engine identified the intent.
- cognitiveEngine: Indicates which cognitive engine is handling the conversation.
- minConfidence: The minimum confidence threshold required for an intent to be considered valid.
- isOnChat: Indicates whether the conversation is happening in a live chat.
- conversationId: Unique identifier to track and associate all parts of a single conversation.
- partName: Specifies the name of the part or segment of the conversation being handled.
- correlationTrace: Field used to trace any correlation between parts of the conversation.
- subLevel: Represents the level within the conversation flow structure.
Debug
Provides technical information about session-specific errors or issues.
Health
Monitors success indicators or problems in sessions to identify improvement areas.
Use Case
Analysis and Troubleshooting of Customer Service Sessions
Description: A company uses a detailed session table to identify problems, assess the efficiency of support flows, and improve customer experience.
Table Interpretation:
- Identify Critical Sessions: Filter by Duration in Seconds or Channel to find long or problematic interactions.
- Review Technical Issues: Use the Debug view to analyze errors and fix system faults.
- Optimize Conversation Flows: In Dialogs, check intents and confidence levels. Adjust thresholds or retrain the cognitive engine if needed.
- Evaluate Overall Status: Monitor Health metrics to measure session success and apply improvements.
- Implement and Evaluate Changes: Adjust flows or train staff. Review metrics to validate the impact of improvements.