timestamp_utc

Time stamp in UTC (Coordinated Universal Time). Quick conversion tips: PST is 7 hours earlier than UTC time; EST is 4 hours earlier than UTC.

email_address

Email address of the user who experienced the resolution or interaction.

initiator

The value in this column delineates whether it was the User or the Bot that initiated the conversation that led to this event. Thus, the only possible values are ‘User’ and ‘Bot’.

skill_category

The high-level skill category for this event. The possible values are ‘Access Software’, ‘Access Group’, ‘Access Account’, ‘AskMe’, ‘Lookups’, ‘Form Filing’, and ‘Interaction’.

skill_detail

More precise information on the use case and/or entity involved in this resolution. The type of information provided here depends on the skill category (the previous column).

If the skill_category is ‘Access Software’, this column will contain the name of the app that was provisioned or for which self-service instructions were given to the user.

If the skill_category is ‘Access Group’, this column will contain an indicator of whether the resolution was a create operation (‘Create’), an add operation (‘Add to’), or remove operation (‘Remove from’).

If the skill_category is ‘Access Account’, this column will contain the precise use case. The possible values are: ‘Change PW prompted’ (upon a password expiration reminder), ‘Change PW unprompted’ (the user asking to reset their password), ‘Unlock Account’, ‘Reset MFA’, or ‘Account Expiry’ (if the user took action on a contractor/contingent worker account expiry reminder)

If the skill_category is ‘AskMe’, there are two possible values for this column: ‘AskMe answer Served’ (if the bot provided a possible answer to a user’s question and the user did not rate it as “not helpful”) or ‘AskMe rated Not Helpful’ (if the bot provided a possible answer, but the user clicked the button to say it was not helpful or filed a ticket)

If the skill_category is ‘Form Filing’, this column will contain the name of the catalog item that the user submitted

If the skill_category is ‘Lookups’, there are several possible values for this column. If the lookup was considered successful (i.e. the user did not rate it as “not helpful”), the value of this column will correspond to the type of lookup it was: ‘people’, ‘rooms’, ‘finance’, or ‘partner’. If the bot provided more than one potential matches, the column will be empty.

If the skill_category is ‘Interaction’, this column will contain the precise use case for the user’s interaction with the ticketing system. The possible values are: ‘Nudge Agent’, ‘Comment added’, ‘Ticket closed in bot’, ‘Ticket weblink clicked’, ‘Check Status’, ‘Click on Got it Thanks’ (when provided an update on the ticket), ‘Ticket submitted in bot’, ‘Tickets approved’ (for ServiceNow RITM only), ‘Live Chat clicked’, ‘Other interaction’ (ticket reopening or interactions with tickets that were opened due to resolution events), ‘Campaign response’.

ticket_id

This column contains the ID number of the ticket that is associated with this event, if one exists. A ticket is associated with an event in two cases: (1) if it is responsible for the bot initiating the conversation (i.e., the bot “reached out” to the user upon reading their ticket), or (2) if the bot files a ticket in the process of automatically resolving the user’s issue (to create a log of the event for audit purposes).

user_id*

Numeric ID of the user who experienced the resolution or interaction, generated internally by Moveworks

minutes_to_resolution*

Time between when an issue was created and when it was resolved in the bot, in minutes

domain*

Domain of enterprise service related to the issue described, as detected by ML models per user utterance

issue_origin*

Origin of issue indicates where user described their issue. It can be user utterance in the bot chat or in IT chat channel, or ticket intercepted by bot.

timestamp_utc

Time stamp in UTC (Coordinated Universal Time) when ticket is polled by bot

ticket_id

ID of the ITSM ticket corresponding to the request

predicted_field

The specific field that is being predicted by the model. These fields include but are not limited to assignment_group, business_service, category, subcategory, and components.

start_value

The field value when the ticket was CREATED (before the model makes a prediction)

predicted_value

The model prediction of the field value

final_value

The field value when the ticket was CLOSED. The final value can either match the predicted value or be updated by agents.

short_description

The short description of the ticket id

triage_result

This field is populated once the ticket is CLOSED. The type of information provided here depends on the predicted field:

If the predicted field is “call type”, a successful prediction would result in a ticket “Predicted” (incident, service request, etc), “No Prediction” (no prediction of field value), or “Out of Scope” (an agent updated the ticket before the bot). 

For other predicted fields such as “Assignment Group”, a prediction can be “Correct” or “Incorrect” (comparing predicted value to final value), “No Prediction” (no prediction of field value) or “Out of Scope” (an agent updated the ticket before the bot).

predicted_field_type

This field is populated once the ticket is CREATED. The type of action the model is performing to the specific field. For CALL tickets, a model would transfer a ticket “Transferred”. For other tickets, such as INC or ITHELP types, they can be “Routed” (routed the ticket to the appropriate support group), “Categorized” (labeled the ticket for reporting purposes), or “No Change” (predicted value is the same as the start value).

timestamp_utc

Timestamp (UTC) indicating when the question was asked in bot or when bot reached out with suggestion on a ticket

article_added_date

Date when the knowledge article was added

user_id

Unique user ID generated internally by Moveworks

user_name

User name (will be redacted for Enterprise Answers)

department

User department (will be redacted for Enterprise Answers)

language

Language of the request (may be empty in case of feature not enabled)

user_text

Text of the user request or ticket

knowledge_title

Title of the knowledge snippet served to user

article_id

Knowledge article ID

doc_source

Source of the knowledge snippet (Internal Knowledge Base/Moveworks FAQ Sheet/Moveworks External Knowledge)

doc_domain

Domain of the knowledge article, as set when the article was added

issue_topic

Entity detected in user request (Email/Outlook/Zoom/VPN/…)

result

Result of the bot actions on the request

“Not Served”: No solution is offered to the user, instead bot suggests user to hand the issue off to an agent. 

“No Engagement: Bot is able to find a relevant answer, however user does not engage to see the suggestion. This can happen in Ticket route if the user does not click on any article links. Another case is when the bot suggests solutions from multiple skills but user does not choose to see results from Answers.

“Not Rated”: Bot offers an answer to the user; the user does not give any explicit feedback and does not hand the issue off to an agent. (Agent handoff includes filing a ticket, click on a Live Agent link or redirection to another domain portal.)

“Agent Handoff”: Bot offers an answer to the user but the user proceeds to an agent handoff.

“Rated Negative”: Bot offers an answer to the user but the user rates the answer as not helpful.

“Rated Positive”: Bot offers an answer to the user and the user rated the answer as helpful.

source

Origin of the request

“DM”: direct message to bot

“Ticket route”: intercepted ticket

“Group channel”:message intercepted in a group channel

article_url

URL of the knowledge article served

ticket_id

ID of the ITSM ticket corresponding to the request

ticket_url

URL of the ITSM ticket corresponding to the request

Active Users

Count unique number of users (by email address) in the daily active users table

select count(distinct email_address) from {{date}}_daily_active_users

Resolution

Count all resolution events from the main events table, excluding Interactions and negative feedback (i.e. where the user rated the bot’s response “not helpful).

select count(*)
from {{date}}_daily_detailed_report 
where skill_category not in ( ‘Interaction’, ‘Lookups’)
and skill_detail not in (‘AskMe rated Not Helpful’)

Interactions

Count the number of unique tickets that users interacted with through the bot, from the main events table

select count(distinct ticket_id)
from
{{date}}_daily_detailed_report
where skill_category=‘Interaction’

Resolution by skill

Count rows in the main event table by respective resolution skill_category, excluding Interactions and negative feedback (i.e. where the user rated the bot’s response “not helpful).

select skill_category, count(*) from {{date}}_daily_detailed_report where skill_category not in ( ‘Interaction’, ‘Lookups’) and skill_detail<>‘AskMe rated Not Helpful’ order by count desc

Access Group by type of operation

Count Access Software resolutions by software application

select SPLIT_PART(skill_detail, ’ ’, 1) as type, count(*) from {{date}}_daily_detailed_report where skill_category = ‘Access Group’

group by type

Access Account by use case

Count Access Account resolutions by use case

select skill_detail, count(*) from {{date}}_daily_detailed_report where skill_category = ‘Access Account’

group by skill_detail

Access Software by App

Count Access Software resolutions by software application

select skill_detail, count(*) from {{date}}_daily_detailed_report where skill_category = ‘Access Software’

group by skill_detail

AskMe by feedback

Count AskMe events by those resolved (‘AskMe answer served’) and those not resolved (‘AskMe rated Not Helpful’)

select skill_detail, count(*) from {{date}}_daily_detailed_report where skill_category = ‘AskMe’

group by skill_detail

Forms filed

Count the number of forms filed, grouping by the catalog item name.

select skill_detail, count(*)
from {{date}}_daily_detailed_report
where skill_category=‘Form Filing’

group by skill_detail

Lookups by feedback

Count successful Lookup events by type of lookup

select skill_detail, count(*) from {{date}}_daily_detailed_report where skill_category = ‘Lookups’ and skill_detail <> ‘Lookup rated Not Helpful’

group by skill_detail

Interactions by type

Count the interaction events in the main events table, grouping by the type of interaction. It is a simple count for all types of interaction.

select skill_detail, count(*)
from {{date}}_daily_detailed_report
where skill_category =‘Interaction’

group by skill_detail

Number of active users by department

Count unique users (by email address), grouping by department, after joining the active user table to the user roster table

select {{date}}_daily_user_roster.department, count(distinct {{date}}_daily_active_users.email_address)

from {{date}}_daily_active_users join {{date}}_daily_user_roster on {{date}}_daily_active_users.email_address={{date}}_daily_user_roster.email_address

group by {{date}}_daily_user_roster.department

Number of active users by geo

Count unique users (by email address), grouping by geographic location, after joining the active user table to the user roster table

select {{date}}_daily_user_roster.geo, count(distinct {{date}}_daily_active_users.email_address)

from {{date}}_daily_active_users join {{date}}_daily_user_roster on {{date}}_daily_active_users.email_address={{date}}_daily_user_roster.email_address

group by {{date}}_daily_user_roster.geo

Triage Result by Field

Count the number of triage_result_type, grouped by the predicted_field and triage_result.

select predicted_field, triage_result, count(*)

from {{date}}_daily_triage_record

group by predicted_field, triage_result

Breakdown of Triage Result by Predicted Values

Count the number of predicted_value, grouped by the predicted_field, triage_result, and predicted_value.

select predicted_field, triage_result, predicted_value, count(*)

from {{date}}_daily_triage_record

group by predicted_field, triage_result, predicted_value

Articles Served

Total number of times an article was found

select count(distinct article_id||timestamp)

from {{date}}_answers_insights_user_interactions

where result in (‘Rated Positive’, ‘Not Rated’, ‘Rated Negative’, ‘Agent Handoff’, ‘No Engagement’)

Answers Provided

Total number of issues when a solution was suggested to user

select count(distinct user_text||timestamp)

from {{date}}_answers_insights_user_interactions

where result in (‘Rated Positive’, ‘Not Rated’, ‘Rated Negative’, ‘Agent Handoff’)

Resolutions Provided

Total number of issues when a solution was suggested to user and there is no negative feedback or a hand-off to an agent

• In Channel route, only issues with positive feedback are counted

select count(distinct user_id||timestamp)

from {{date}}_answers_insights_user_interactions
where
source=‘Group channel’ and result=‘Rated Positive’
or
source=‘Ticket route’ and (result=‘Rated Positive’ or result=‘Not Rated’)
or
source=‘DM’ and (result=‘Rated Positive’ or result=‘Not Rated’)

Employees with Resolutions

Number of unique users with a solution suggested and no negative feedback or a hand-off to an agent

• If a user reported an issue in Channel route, only resolutions with positive feedback are taken into account

select count(distinct user_id)

from {{date}}_answers_insights_user_interactions
where
source=‘Group channel’ and result=‘Rated Positive’
or
source=‘Ticket route’ and (result=‘Rated Positive’ or result=‘Not Rated’)
or
source=‘DM’ and (result=‘Rated Positive’ or result=‘Not Rated’)

Knowledge Effectiveness by Article

Ratio of the number of issues with resolution in Answers (not rated or rated positive) to the number of issues with Answers suggestion served to user

select knowledge_title, 1.0*count(distinct case when result not in (‘Rated Negative’, ‘Agent Handoff’, ‘Not Served’, ‘No Engagement’) then timestamp else null end)/count(distinct case when result not in (‘Not Served’, ‘No Engagement’) then timestamp else null end) as knowledge_effectiveness_rate

from {{date}}_answers_insights_user_interactions

group by knowledge_title

Knowledge Coverage by Issue Topic

Ratio of the number of issues with a knowledge article found to the number of issues with an attempt to find an article

select issue_topic, 1.0*count(distinct case when result not in (‘Not Served’) then timestamp else null end)/count(distinct timestamp) as knowledge_coverage_rate

from {{date}}_answers_insights_user_interactions

group by issue_topic