Join your Zendesk Chat and GitLab data
Stitch can replicate data from all your sources (including Zendesk Chat and GitLab) to a central warehouse. From there, it's easy to perform the in-depth analysis you need.
Stitch can replicate data from all your sources (including Zendesk Chat and GitLab) to a central warehouse. From there, it's easy to perform the in-depth analysis you need.
Integrate Zendesk Chat and GitLab to turn your data into actionable insights.
Zendesk Chat is user-friendly, reliable and feature-rich chat software
Stitch offers detailed documentation on how to sync your Zendesk Chat data.
GitLab provides free unlimited (private) git repositories and unlimited collaborators
Stitch offers detailed documentation on how to sync your GitLab data.
Since Stitch replicates data to a consistent schema, it works well with other tools in your stack. Once you have BLOCKNAME HERE data in your data warehouse you can use it in many ways. Modeling tools such as dbt and Looker Blocks can help you prepare your data for reporting, analytics, or machine learning.
We've developed a Looker Block for Zendesk Chat data provisioned by Stitch. This block includes prebuilt code to create dashboards and models that can help uncover insights from your Zendesk Chat data.
This Looker Block includes three dashboards that provide analysis on agent performance, ticket submissions, and overall customer support metrics. The LookML file shown here produces an overview dashboard that allows you to view understand ticket submission trends. Other dashboards included in this block are: Overview dashboard - View ticket submissions over time to understand the level at which your customers are leveraging your support team - See the breakdown of ticket submissions by channel to understand where most of your support requests are generated - See your top 20 all-time agents, requesters, and organizations by number of tickets to see identify the key players in customer support - See a ticket tag breakdown over time to understand how customer priorities have shifted Agent performance dashboard - Monitor your support team's all-time reply and resolution time to measure against SLAs - See how your team's response and reply time have fluctuated over time to identify trends and opportunities for improvement or celebration groups and more efficiently manage resources - Identify top performers in your organization Ticket submissions dashboard - Evaluate the volume at which organizations are submitting tickets to identify which organizations are requiring the most attention of your team - Identify how average tickets per organization changes over time to see whether documentation, tutorials, demos, and product changes or releases are taking a load off your team - See ticket submission volume by hour of the day and day of the week to more efficiently allocate resources
View the source on GitHub →1
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- dashboard: overview
title: Overview
layout: grid
rows:
- elements: [new_open_tickets, pending_tickets, closed_tickets]
height: 150
- elements: [tickets_and_orgs]
height: 400
- elements: [tickets_by_channel, count_by_status]
height: 400
- elements: [top_orgs, top_requesters, top_assignees]
height: 400
- elements: [ticket_tags]
height: 500
filters:
- name: date
type: date_filter
elements:
- name: new_open_tickets
type: single_value
model: zendesk
explore: tickets
measures: [tickets.count]
filters:
tickets.status: new,open
sorts: [tickets.count desc]
limit: 500
show_single_value_title: true
single_value_title: New and open tickets
show_comparison: false
listen:
date: tickets.created_at_date
- name: pending_tickets
title: Pending tickets
type: single_value
model: zendesk
explore: tickets
dimensions: [tickets.status]
measures: [tickets.count]
filters:
tickets.status: pending
sorts: [tickets.count desc]
limit: 500
show_single_value_title: true
single_value_title: Pending tickets
show_comparison: false
listen:
date: tickets.created_at_date
- name: closed_tickets
title: Untitled Visualization
type: single_value
model: zendesk
explore: tickets
measures: [tickets.count]
filters:
tickets.status: closed,solved
sorts: [tickets.count desc]
limit: 500
show_single_value_title: true
single_value_title: Closed and solved tickets
show_comparison: false
listen:
date: tickets.created_at_date
- name: tickets_by_channel
title: Tickets submitted by channel
type: looker_pie
model: zendesk
explore: tickets
dimensions: [tickets.via__channel]
measures: [tickets.count]
sorts: [tickets.count desc]
limit: 500
value_labels: legend
colors: ['#FFCC00', '#1E2023', '#3399CC', '#CC3399', '#66CC66', '#999999', '#FF4E00', '#A2ECBA', '#9932CC', '#0000CD']
show_view_names: true
listen:
date: tickets.created_at_date
- name: tickets_and_orgs
title: Ticket submissions over time
type: looker_line
model: zendesk
explore: tickets
dimensions: [tickets.created_at_week]
measures: [tickets.count, tickets.count_distinct_organizations]
sorts: [tickets.created_at_week desc]
limit: 500
stacking: ''
show_value_labels: false
label_density: 25
legend_position: center
x_axis_gridlines: false
y_axis_gridlines: true
show_view_names: true
limit_displayed_rows: false
y_axis_combined: true
show_y_axis_labels: true
show_y_axis_ticks: true
y_axis_tick_density: default
show_x_axis_label: true
show_x_axis_ticks: true
x_axis_scale: auto
y_axis_scale_mode: linear
show_null_points: true
point_style: none
interpolation: linear
colors: ['#FFCC00', '#1E2023', '#3399CC', '#CC3399', '#66CC66', '#999999', '#FF4E00', '#A2ECBA', '#9932CC', '#0000CD']
listen:
date: tickets.created_at_date
- name: count_by_status
title: New, open, solved, and pending ticket count
type: looker_column
model: zendesk
explore: tickets
measures: [tickets.count_solved_tickets, tickets.count_new_tickets, tickets.count_open_tickets,
tickets.count_pending_tickets]
sorts: [tickets.count_solved_tickets desc]
limit: 500
stacking: ''
show_value_labels: false
label_density: 25
legend_position: center
x_axis_gridlines: false
y_axis_gridlines: true
show_view_names: true
limit_displayed_rows: false
y_axis_combined: true
show_y_axis_labels: true
show_y_axis_ticks: true
y_axis_tick_density: default
show_x_axis_label: true
show_x_axis_ticks: true
x_axis_scale: auto
y_axis_scale_mode: linear
show_null_labels: false
colors: ['#FFCC00', '#1E2023', '#3399CC', '#CC3399', '#66CC66', '#999999', '#FF4E00', '#A2ECBA', '#9932CC', '#0000CD']
listen:
date: tickets.created_at_date
- name: top_orgs
title: Top 20 organizations by tickets submitted
type: table
model: zendesk
explore: tickets
dimensions: [tickets.organization_name]
measures: [tickets.count]
sorts: [tickets.count desc]
limit: 20
show_view_names: true
show_row_numbers: true
truncate_column_names: false
table_theme: editable
limit_displayed_rows: false
listen:
date: tickets.created_at_date
- name: top_requesters
title: Top 20 requesters by tickets submitted
type: table
model: zendesk
explore: tickets
dimensions: [tickets.requester_email]
measures: [tickets.count]
sorts: [tickets.count desc]
limit: 20
show_view_names: true
show_row_numbers: true
truncate_column_names: false
table_theme: editable
limit_displayed_rows: false
listen:
date: tickets.created_at_date
- name: top_assignees
title: Top 20 agents by all time tickets
type: table
model: zendesk
explore: tickets
dimensions: [tickets.assignee_email]
measures: [tickets.count]
sorts: [tickets.count desc]
limit: 20
show_view_names: true
show_row_numbers: true
truncate_column_names: false
table_theme: editable
limit_displayed_rows: false
listen:
date: tickets.created_at_date
- name: ticket_tags
title: Ticket tags
type: looker_column
model: zendesk
explore: ticket__tags
dimensions: [ticket__tags.value, ticket__tags.created_at_month]
pivots: [ticket__tags.value]
measures: [ticket__tags.count]
sorts: [ticket__tags.created_at_month desc, ticket__tags.value]
limit: 500
column_limit: 50
stacking: percent
show_value_labels: false
label_density: 25
legend_position: center
x_axis_gridlines: false
y_axis_gridlines: true
show_view_names: true
limit_displayed_rows: false
y_axis_combined: true
show_y_axis_labels: true
show_y_axis_ticks: true
y_axis_tick_density: default
show_x_axis_label: true
show_x_axis_ticks: true
x_axis_scale: auto
ordering: none
show_null_labels: false
colors: ['#FFCC00', '#1E2023', '#3399CC', '#CC3399', '#66CC66', '#999999', '#FF4E00', '#A2ECBA', '#9932CC', '#0000CD']
listen:
date: ticket__tags.created_at_date
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