Your Zendesk data is split into differentdatasets. Each dataset contains metrics and attributes that you can use tocreate Explore reports. You must select a specific dataset before you can create a report.
Use this article to help choose the right dataset for your reports and to learn more advanced information about how datasets store your business information.
This article contains the following topics:
Related articles:
Understanding the available default datasets
The table below describes the datasets that are available for each product.
| Zendesk product | Dataset name | What it contains |
|---|---|---|
| Support | Tickets | Information about ticket details, like ticket ID and assignee. Does not include ticket update events. |
| 更新历史记录 | Information about updates made to tickets during their lifetime. | |
| Backlog history | Information about your unsolved tickets at the end of a given date. | |
| SLAs | Information about your service level agreement (SLA) performance. Available only if you have tickets with SLA policies applied. SeeDefining and using SLA policies. |
|
| Group SLAs | Information about your group service level agreement (SLA) performance. Available only if you have tickets with SLA policies applied. SeeDefining group SLA policies for internal teams. |
|
| Guide | Knowledge Capture | Information to help you understand the efficiency of selecting articles to deflect support tickets. |
| Team Publishing | Information to help you understand your team activity in Guide, including when articles are created, published, edited and more. Available only on Professional plans and above. |
|
| 亚博官方app | Information to help you understand how often your help center articles are being viewed, which articles are being voted up or down, and more. | |
| Search | Information about the searches that users performed and the terms they searched for in your knowledge base. | |
| Community | Information about the activity in your community forums, including the number of posts and comments, upvotes and downvotes, community members, and more. | |
| Messaging and live chat | Messaging tickets | Information about all messaging channels, including web, mobile, and social messaging channels. Includes number of tickets, resolution times, satisfaction, and more. |
| Engagement | Information about your customer engagement using Chat. | |
| Chat Concurrency | Information about your agents' handling of concurrent chat engagements. | |
| Answer Bot | Article Recommendations | Information about the performance of help center articles automatically recommended to customers. |
| Flow Builder | Information about bot performance across Zendesk channels. | |
| Talk | Calls | Information about your call center and agent activity. |
| Sell | Sell | Information about your sales pipeline. |
| Calls | Information about your sales calls, such as incoming and outgoing calls, call time, and more. | |
| Products | Information about your product sales, such as how many products were sold over time or which products sold the most. | |
| Activities | Information about sales activity volume and sales team contribution, including the number of emails sent, calls made, tasks completed, appointments held, and visits made. |
Understanding dataset structure
Explore datasets contain all of the available information for your product. To query your data efficiently and avoid duplicate or inconsistent data, Explore groups your data into multiple data tables. You can think of a data table as a kind of "box" in which your data is stored. Each data table is not isolated; instead, they're joined to one another byconnection points special attributesthat act as unique identifiers for each row of data in the table.
In the example diagram below, ticket data is stored in theTicketsdata table and user data is stored in a separateUsersdata table. These data tables are joined in the datasets using connection points special attributes.
For example,Ticket IDis the connection point for theTicketdata table, butRequester IDis the connection point for theUserstable.
When a user runs a report, Explore determines which tables contain the required metrics and attributes and whether the tables need to be joined. If the required metrics and attributes are located in one table, then no connections (or joins) are made. An example of this is a report that counts ticket IDs by status.
However, if the required metrics and attributes are in multiple data tables, then the tables will be joined. An example of this is a report that counts ticket updates by assignee name. In this case, theTicket updates,Tickets, andUserstables are joined to generate the result.
Explore data tables are connected using the LEFT JOIN method. This means that when the tables are joined, the report returns all rows from the table on the left, even if there are no matches from the table on the right. In the example above, a count of ticket IDs by assignee name will return all tickets with or without an assignee.
In some cases, it's technically not possible to store data in multiple data tables due to the high volume of the data or high speed of the report execution required. An example of this is the Backlog dataset. This uses only one table for storing data.
7 Comments
I'm wanting to report on the "Initial Group" or "Previous Group" for tickets that have a SLA breach. I can create a query with a table of breached tickets from the tickets dataset, and can create a query with a table of tickets and their "First Group" or "Previous Group" (but I can't make this table for only SLA breach tickets). Is there a way to do this? We have done all that we can on reporting and changing behavior with regards to First Reply Time. We now need to report on the groups that are re-assigning tickets late to further improve. I'm thinking that I'll just have to report on new tickets that are not updated in less than 8 business hours (but SLA would be preferable). Thanks
Hi, I'm trying to use custom fields in explore and I couldn't find it.
Is there any way to use custom fields in the Sales database?
Hi,
什么指标称为解决了抽搐的原因kets in the Tickets dataset shows a different value than the metric called Tickets solved in the Ticket updates dataset.
Should they not both show the same amount of total solved tickets?
See below:
Cheers
Chad
@...our product manager has responded to a similar question in the comments thread here:Metrics and attributes for Zendesk Sell (Beta), namely that this is an area of focus for that team.
Hi@..., one of our intrepid community moderators provided some insight into your question here:Tickets vs Ticket Updates Datasets yield different results for solved tickets. Hope that helps!
An image has been removed from this article in the last week. It is still being referenced in the text:
" In the example above, a count of ticket ids by assignee name will return all tickets with or without an assignee."
HiCJ Johnson实际上,文本是referencing the example introduced in the preceding paragraph ("An example of this is a query that counts ticket updates by assignee name"). I've double-checked to make sure all images are still in the article!
Pleasesign into leave a comment.