List of Trackable Metrics

# conversations over time. E.g. Last week there were 13,173 conversations
# messages over time. E.g. Last week there were 82,263 messages
Average global user input sentiment over time. E.g. Last week the average sentiment of user inputs trended down
Average global user rating over time. E.g. Last week the user feedback rating trended down
# messages received in each language. E.g. Last week there were 872 messages in French

Classify messages and conversations into custom categories:
- Pre-define a taxonomy of keywords, semantic meanings, user intents, or custom categories defined by LLM prompts
- Context.ai will also suggest salient groups of messages
# conversations matching a specific semantic, keyword, or user intent category over time. E.g. Last week there were 2,920 messages about hair dyes
Average user input sentiment for a specific semantic, keyword, or user intent category over time. E.g. Last week the average sentiment of user inputs for conversations about hair dyes trended lower
Average user rating for a specific semantic, keyword, or user intent category over time. E.g. Last week the average user rating for conversations about hair dyes trended lower

If User IDs are provided, Context.ai can report:

User retention rates per week. E.g. For the cohort who signed up on date X, we retained Y% of users after Z weeks
Topics of conversation discussed most often by new users. E.g. Last week new users most frequently discussed: hair dyes, dandruff, shampoo
Topics of conversation discussed most often by veteran users. E.g. Last week frequently engaged users most often discussed: product prices, product availability
Table of users reporting: user ID, # messages sent, # conversations, average sentiment, average feedback rating, first seen, last seen

List all the conversations matching any of the following filter criteria:

User rating: positive, negative, neutral
User input sentiment: positive, negative, neutral
User input sentiment trending: upwards, downwards
Freetext feedback: positive, negative, neutral
Custom events: occurred or did not occur. Examples: conversion events, clicks, or other interactions
Category: any semantic, keyword, or intent category
Labels: any user-applied label
Ratings: any user-applied rating: 1 star to 5 stars
Custom metadata: any metadata provided with the transcripts, including: model, user ID, environment, experiment arm

Last updated 9 months ago

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# conversations over time. E.g. Last week there were 13,173 conversations
# messages over time. E.g. Last week there were 82,263 messages
Average global user input sentiment over time. E.g. Last week the average sentiment of user inputs trended down
Average global user rating over time. E.g. Last week the user feedback rating trended down
# messages received in each language. E.g. Last week there were 872 messages in French

Classify messages and conversations into custom categories:
- Pre-define a taxonomy of keywords, semantic meanings, user intents, or custom categories defined by LLM prompts
- Context.ai will also suggest salient groups of messages
# conversations matching a specific semantic, keyword, or user intent category over time. E.g. Last week there were 2,920 messages about hair dyes
Average user input sentiment for a specific semantic, keyword, or user intent category over time. E.g. Last week the average sentiment of user inputs for conversations about hair dyes trended lower
Average user rating for a specific semantic, keyword, or user intent category over time. E.g. Last week the average user rating for conversations about hair dyes trended lower

If User IDs are provided, Context.ai can report:

User retention rates per week. E.g. For the cohort who signed up on date X, we retained Y% of users after Z weeks
Topics of conversation discussed most often by new users. E.g. Last week new users most frequently discussed: hair dyes, dandruff, shampoo
Topics of conversation discussed most often by veteran users. E.g. Last week frequently engaged users most often discussed: product prices, product availability
Table of users reporting: user ID, # messages sent, # conversations, average sentiment, average feedback rating, first seen, last seen

List all the conversations matching any of the following filter criteria:

User rating: positive, negative, neutral
User input sentiment: positive, negative, neutral
User input sentiment trending: upwards, downwards
Freetext feedback: positive, negative, neutral
Custom events: occurred or did not occur. Examples: conversion events, clicks, or other interactions
Category: any semantic, keyword, or intent category
Labels: any user-applied label
Ratings: any user-applied rating: 1 star to 5 stars
Custom metadata: any metadata provided with the transcripts, including: model, user ID, environment, experiment arm

Last updated 9 months ago

Was this helpful?