Category: Zendesk ticket categories

Zendesk ticket categories

You can organize your knowledge base content into categories and sections and manage the order of those categories and sections. Categories are the top-level organizing containers of the Help Center. Categories contain sections. The Help Center must have at least one category.

3 Zendesk Ticket Assignment

If you have only one category in your Help Center, then the category itself is hidden to end-users, and they see only the sections in your Help Center. For more information, see About the Help Center knowledge base structure. If you do not select an alternate template, the default category template will be applied. The category is created. Remember, if you have only one category, then the category itself is hidden to end-users, and they see only the sections that are in your Help Center.

Sections are collections of related articles. To add a section, you must have at least one category to act as its parent container. On Guide Lite and Professional, each section must be child of a category.

On Guide Enterprise, a section can be a child of a category or a section. Permissions for viewing and managing articles in a section are set at the article level, not the section level. If visibility for all the articles in a section is internal Professional and Enterprise onlythen the section will not be visible to end-users, but if one article in an internal section is visible to any end-users, then the section will become visible as well.

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If you select to manually order of articles, see Manually changing the order of articles to change the order of your articles.

On Guide Lite and Professional, you must select a category as the parent for this section. Sections cannot contain sections. On Guide Enterprise, you can select a category or a section as the parent for this section. You can nest sections in sections for a maximum of five section levels, with up to 20 sections in a section. If you do not select an alternate template, the default section template will be applied.

The section is created. Remember, if you have only one category, then the category itself is hidden to end-users, and users see only the sections that are in your Help Center. You can change the properties of a category or section, including its title or description. When you delete a category, all the sections contained in the category are also deleted, and all the articles contained the sections are archived.

You cannot recover deleted categories or sections. Any articles contained in a deleted section are archived. You can restore archived articles to another section if you need to. In the edit page, click Delete in the bottom of the sidebar. If the edit page doesn't open when you click the name of the category or section from Arrange Articles, it is likely being blocked by a pop-up blocker.

If you create a category or section that is a work in progress, and want to restrict who can access it, you can mark it as a draft. Marking a category or section as a draft applies the draft status to any elements within it. The steps for marking a newly-created category or section as a draft are slightly different than those for marking an existing category as a draft. Categories and sections marked as drafts are not viewable by end-users, and can only be accessed by Guide Managers.

To determine whether a category or section is marked as a draft.Do you mean that you want to have custom views for your tickets? Or are you referring to something within in the ticket itself?

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As we receive a ticket, we assign it to the supporter and then deeding on the situation of the ticket, it is either Solved, open, or pending. Ticket statuses open, pending, etc. The reason for this is that it would break the logic and therefore functionality of the system. So regardless of the "package" we have at Capitolis, we would not be able to customize any of the ticket statuses?

Since so much of how the system functions is based on those statuses, customizing them would also require rebuilding the logic of the entire ticketing system.

zendesk ticket categories

Please sign in to leave a comment. Anyone knows how can I do this? Sort by Date Votes. Hi yoavp!

Organizing knowledge base content in categories and sections

Welcome to the Community! Hi Jessie, thanks for replying When you say custom views, you mean different categories? What I meant in my question is that. I want to edit those categories, and as a result, I want to have different view points. Was this a little understandable? Hope it is :.

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Hi Nicole, thanks for your response. Ticket statuses are not customizable.

zendesk ticket categories

Didn't find what you were looking for? New post. Powered by Zendesk.This average needs to be calculated over a month period of time. Hey Zeynel- What you're hoping to do likely requires custom metrics to get an average. What: Backlog tickets How: Backlog status filtered to open How: Month event You could then filter further by your product categories depending on how those are tracked in your Zendesk.

We had defined an extra attribute, ''Product Category'' and I don't know how to bring that in when I use the Backlog metrics. The Product Category could be pulled in by adding as an additional metric in your report. Depending on what type of custom fields it is, it will either be an attribute or a fact. Attributes are automatically created as metrics under How, where facts need to be created.

Here's some of our resources on this. Reporting on the average is not quite possible with default metrics; you could create a custom metrics however.

zendesk ticket categories

Here's a an example on how you could go about this:. I think I miss some fundamental knowledge in this: I know how to create a custom metric, but I don't know how to construct the metric in order to give me the result I want. Until now, with the backlog metric, I have the historical data, but not from what I want, i. This average is an average calculated over a month period of time. If I need to add a metric for the product category, what exactly do I need to select in there, in order to get the split per category?

The backlog metrics are apart of a different data set then the custom ticket fields- the backlog data set and events data set respectively.

In Gooddata, metrics in the different data sets do not directly "communicate" with each other and as a result building reports across data sets is not always plausible and often involves very complex custom metrics and event then the reports do not accomplish exactly what is intended.

I have to reach out to you once again as I didn't succeed to realize what I want, even not with ticket created -metric. The backlog metric gives me almost what I need In the end I want to calculate an average backlog per category of a month. Just to add to Rebecca's comments. Custom fields are not part of the historical data set, so you cannot slice metrics by this field. If your product category can be identified by Group or Brand, then you can use these fields for historical reporting.

Thanks for your prompt reply - I mentioned the backlog metrics because I can not pull the data from other metricses.Zendesk makes support, sales, and customer engagement software for everyone. With Zendesk, it takes hours — not weeks — to get up and running. CX leaders build agility and resilience in their customer support. Watch our recent livestream to learn their strategies. Learn from the leaders. Check out the new features that keep you five steps ahead through rapid change.

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About ticket fields

Start a free trial. Best Practices Future-proof your CX A complete guide to future-proofing your customer service, including the skills and approaches you need to succeed. Our diversity commitment We can't be silent How Zendesk is standing with our Black employees against racism and bias.Typically, when end users submit support requests, they provide the subject and description of their question or support issue. They may also be prompted to provide additional data such as a product type or model number using custom ticket fields.

All of the other data in a ticket is set by you or behind the scenes using the business rules. The standard fields that agents see in a ticket are referred to as system fields. See table below. Additional system fields are added to the ticket page when you activate additional Zendesk Support features such as ticket sharing.

If a ticket is created by an agent and the requester field is left empty, then the agent will be the requester of the ticket.

If needed, the ticket requester can be changed to someone else. See Updating the ticket requester. You can also create a ticket on someone else's behalf.

See Creating a ticket on behalf of the requester. By default, all of these four values are available, but you can allow only the Normal and High values to appear. To do so, edit the priority fieldthen change the setting under Field values.

Priority is not a required field, so you do not always need to select a value. How you weigh the priority of your tickets is up to you. Tags are enabled by default but can be disabled. See Enabling and disabling ticket tags. You can deactivate and reactivate some but not all of the system fields and add and manage custom fields on tickets.

For more information, see Viewing your ticket fields and Editing and managing your ticket fields. Some system fields are inborn and cannot be reconfigured. See What are the inborn system ticket rules? In addition to these system ticket fields, tickets can also contain custom fields, which are used to gather additional information from the person who is requesting support. For example, you may add a custom field prompting them to select a product name or model number.

zendesk ticket categories

For more information, see Adding custom fields to your tickets and support request form. Tickets contain other data that you can access using placeholders and the Zendesk API. For more information about this additional data, see Ticket data.Boris Johnson's 'frank' talks with Iran in in a bid to get British mother held there on spying charges. Trump ignores protesters and boycotts from top civil rights leaders as he calls for 'peace and equality' at.

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Average open tickets per category per month

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At launch, the platform will cover the four major US sports (Football, Basketball, Baseball, Hockey), Soccer and Tennis. Moreover, the models provide stats to measure the performance of the algorithm for the current season (for each sport) along with bankroll comparaison stats with bookmaker (scraping from the oddsportal's website to do so). Here's below an image from the mobile app propotype based on the platform. This is contrary to another popular method of representing the teams as the favorite and underdog.

The point differential is chosen to be positive when the home team scores more points than the away team. To represent the difference between the two teams that are playing in the matchup, the ratio or the difference between the same attributes are taken between the two teams.

Therefore, when attributes are a positive indicator of performance, a value greater than 1 indicates that the home team performs better for that particular attribute. Examples of this are win streak, compared to statistics such as points per game that would be compared by taking the ratio. Web Scraping The data is scraped from several websites according to each sport using Python and the Selenium and BeautifulSoup (only for MLB data) packages for Python.

Data sources for each sports are described in the "Link" section. Game stat data, team stat data, and datetime data are merge later into a feature file (. In the same scripts, After having obtained the raw data set, data is cleaned throughout the script.

The script checked the completeness and validity of all the data files, and eliminated any CSV parsing errors or erroneous data values. Here is the main idea: Having validated all our available data, the script then proceeded to load the data from the csv files into an SQL database using the SQLite single-file database engine and a few Python scripts.

The flexibility of SQL queries allowed one to easily perform complex joins and merges between multiple tables via the the script.

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Thus, the script converts the clean scraped data to data structures that the libraries in scikit-learn can easily use. The ultimate result of the routines included in this file is a numpy array containing all the features and game results for the historical game data.

The features have not been normalized but the next script provide one the ability to easily normalize or standardize the data. Algorithm Tuning and Running This step is located in the "RunModelLeague. A logistic regression predictive model with the L1 penalty is created.

Analysis of results are output to csv files. Then, webscraping (through the ScrapeMatchupDatetimeOddsTwoChoicesLeague.


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