Support categoryCertified by Stitch

Expected Zendesk Support data

Here’s a sample of the raw Zendesk Support data that Stitch will replicate to your analytics warehouse:

Tickets

This table records all ticket details, including the requester and assignee IDs.

Table name:tickets

Audits

This table contains information about activity associated with a ticket, including status changes and both customer and agent responses.

Table name:audits

Groups

Groups are how agents are organized in your Zendesk account. The groups table records information such as the gruop ID, URL, name, and creation and update information.

Table name:groups

Organizations

The organizations table records company information about your end-users such as the name, ID, associated domain names, tags, and any custom fields.

Table name:organizations

Users

This table contains details on end-users and agents, including names and emails.

Table name:users

Tags

The tags table contains a list of all the tags in your account.

Table name:tags

View all tables

Learn more about the Zendesk Support integration

Documentation

Detailed documentation on how to start syncing Zendesk Support data.

Zendesk Support Documentation

Zendesk Support to your data warehouse in minutes

ETL your Zendesk Support data to the destination of your choice.

Jumpstart your Zendesk Support analytics with reusable blocks

dbt packages can speed up your work

Once you replicate your Zendesk Support data with Stitch, you can use it in many ways. For example, you can use the data modeling and transformation tool dbt to prepare data for reporting, analytics, or machine learning applications.

Dbt has prebuilt packages for many Stitch data sources, including Zendesk Support. Here’s a look at code for modeling Zendesk Support data. This particular block of code transforms your Zendesk Support data so you can analyze your ticket metrics.

View the source on GitHub →
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 with tickets as ( select * from {{ref('zendesk_tickets')}} ), url as ( select * from {{ref('zendesk_org_url')}} ), metrics as ( select * from {{ref('zendesk_ticket_metrics')}} ), organizations as ( select * from {{ref('zendesk_organizations')}} ), users as ( select * from {{ ref('zendesk_users') }} ), final as ( select tickets.*, metrics.created_at as ticket_created, metrics.initially_assigned_at, metrics.solved_at, metrics.reopens, metrics.replies, metrics.first_resolution_time_in_minutes_business, metrics.first_resolution_time_in_minutes_calendar, metrics.full_resolution_time_in_minutes_business, metrics.full_resolution_time_in_minutes_calendar, metrics.on_hold_time_in_minutes_business, metrics.on_hold_time_in_minutes_calendar, metrics.reply_time_in_minutes_business, metrics.reply_time_in_minutes_calendar, metrics.requester_wait_time_in_minutes_business, metrics.requester_wait_time_in_minutes_calendar, organizations.name as organization_name, requesters.email as requester_email, assignees.email as assignee_email, 'https://' || url.org_url_identifier || '.zendesk.com/agent/tickets/' || tickets.ticket_id::varchar as web_url from tickets left join metrics on metrics.ticket_id = tickets.ticket_id left join organizations on organizations.organization_id = tickets.organization_id left join users as requesters on requesters.user_id = tickets.requester_id left join users as assignees on assignees.user_id = tickets.assignee_id inner join url on 1 = 1 ) select * from final

... or use Looker blocks with your Zendesk Support data

We've developed a Looker Block for Zendesk Support data provisioned by Stitch. This block includes prebuilt code to create dashboards and models that can help uncover insights from your Zendesk Support 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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 - 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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We set up all of the integrations we needed in a matter of hours. Writing custom code to do all of that would have taken up a month's worth of back-end engineering time. Using Stitch allowed us to move forward without having to hire another person.

Mark Marcantano

Senior DevOps Engineer, Calm

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