Data Explorer

Data Explorer

The Data Explorer is an interactive visual tool for exploratory data analysis, feature engineering, and data reporting of your chatbot’s conversations.

If you have any questions, comments, or suggestions, please feel free to contact us.

data explorer


  • View - Data Explorer allows you to select a subset of your conversational data for viewing or analysis. These 5 parameters are different ways of aggregating subsets of your JSON payload to explore your data further.
    • Messages - Count of unique messages
    • Users - Count of unique users across all messages
    • Intents - Count of all unique incoming/outgoing intents
    • General Field - Specific fields associated with parameters related to our platform. You can select to view from any of the following general fields and apply one of the following aggregate functions: count, count_distinct, sum, avg, min, and max
      • platformUserID - the ID for your user on a particular bot platform
      • sessionID - the ID for a distinct conversation session
      • Sentiment - the corresponding sentiment value for a session
      • Datetime - the timestamp for each message
    • JSON Field - Specific fields associated with the JSON payload. This allows for specific/custom fields that you pass through your JSON payload (e.g. intentConfidence). You can select to view any specific category from your JSON payload and apply one of the following aggregate functions: count, count_distinct, sum, and avg.
      • Below is an example of selecting to view the Intent Confidence from the JSON metadata
      • Example - fm.metadata/intentConfidence count as IntentConfidence
  • Filter - An initial filter of your bot’s data before viewing the output. A filter selects a subset of your bot’s data determined by specific constraints. You can find the list of possible filter constraints in the Glossary section below.
  • Breakdown - A breakdown classifies the resulting data into segments based on the selected category. You can find the list of possible breakdowns in the Glossary section below.


  • Date Range - You can select the time range for the conversational history of your chatbot
  • Linear vs Logarithmic - You can select to view the time series graph as either linear or logarithmic
  • Output/Graph Type - The following are the different graph views available for Data Explorer:
    • Line
    • Stacked Line
    • Bar
    • Stacked Bar
    • Table
    • Condensed Table
    • Pie
    • KPI


  • Data Explorer let’s you save and download your unique DE configurations.
    • Load - load a previously saved DE configuration
    • Save - saves the current configuration
    • Save As New - saves as a new configuration
    • New - resets the configuration (pretty much a refresh)
    • Download CSV - To download as a CSV file click the ellipses … in the upper right hand corner of the report

Use Cases

The following are a few popular use cases to explain how you might use Data Explorer to answer common questions regarding your data:

Demo video on using the Data Explorer to identify which intents and messages types are being sent by your users

  • Use Case 1: View Breakdown of First Message based on Message Type & Intent
    • View: Messages
    • Filter: First Message In Session
    • Breakdown: Incoming, Message Type, Intent
    • Be able to identify which intents and message types are the first to be sent to your chatbot by users
  • Use Case 2: View Intent Matching based on Keywords
    • View: Messages or Users
    • Filter: Text containing “keyword”
    • Breakdown: Intent, Text
    • Be able to easily identify Mishandled/Unhandled intents that should be Handled correctly
    • Example: Filtering for all messages containing the keyword “Help”
      • Identify which “help” messages are mapping to incorrect and unhandled intents to use as training phrases
  • Use Case 3: View Average Intent Confidence for Incoming Messages
    • View:
      • Messages
      • Users
      • sessionId - count_distinct
      • fm.metadata/intentConfidence - avg
    • Filter: Incoming
    • Breakdown: Intent
    • Be able to identify which intents where most often triggered and the respective users, sessions, and intent confidence score


Message Typethe different types of messages from your chatbot’s conversation (text, image, voice, etc)
First Message in Sessionselecting the first message from your chatbot’s conversations
Incomingselecting from only the messages sent to your chatbot
Intentselecting based on distinct intents
Not Handled Intentselecting based on the NotHandled Intent that was selected in Bot Configuration
Team IDselecting based on the team ID
Channel IDselecting based on the channel ID of your bot
Genderselecting by gender
Timezoneselecting by hourly timezone
Localeselecting by Country
Boolean Fieldselecting a boolean value for Incoming
Session IDselecting based on distinct session ID’s
Dashbot Session IDselecting based on Dashbot’s distinct session ID’s
External Session IDselecting based on External session ID’s
Textselecting based on distinct messages from your chatbot’s conversations
UserIdselecting based on distinct user ID’s of your chatbot
urefers to the raw dataset sent by your chatbot
fmrefers to the filtered dataset Dashbot created from your chatbot’s raw data
Datetimeselecting based on the message timestamp
Date Compareselecting for when the u and fm datasets are equal when truncated by day/month/year

Example: u.firstMessageDatetime truncated to day eq fm.datetime truncated to day
Date Partselecting based on grouping by specified timestamp

Example: day = 4, keep all messages from the 4th day of the month
General Fieldselecting based on specific fields associated with parameters related to our platform
JSON Fieldselecting based on custom fields from your JSON payload
Edit this page on GitHub