Monitor Knowbler Adoption with Analytics

Analytics provides insights into how well agents are taking to the knowledge-first approach. You can monitor adoption with the reports in Analytics, which update every 30 minutes.

Information on knowledge generation can be found in Overview. Through Contributor Analytics, you can recognize star performers. This report has everything you'll need; the number of articles created, numbers on agent productivity, and the impact of articles on case resolution. To measure content performance and quantify success, consult Support Effectiveness, Article Usage Analytics and Low Impact KB Articles. The following filters are common to all the reports:

  • Service Desk Management Tool. Use this filter to select the platform where Support Console has been configured.

  • Date Range. Use this filter to generate reports for a specific day, week, month, or a period up to six months long.

Overview

It shows the total number of articles created on the selected Service Desk Management Tool over a period specified in Date Range. The report is a doughnut chart. The share of drafts and published articles is in the ring and the sum of drafts and published articles is in the center.

The deleted articles don’t impact the count in the report because the report only monitors article creation and not deletion. Suppose your team created 100 articles last week and discarded 30, the total count for the week would remain 100.

Data in Overview can be downloaded as a CSV, which consists of five columns: Article ID, Article Title, Created Date, Status, and Case Number. To initiate a download, click . Alternatively, click to share the CSV file with one or more users over email.

Support Effectiveness

Support Effectiveness is a stacked area graph that keeps you on top of two crucial metrics; the total number of cases closed and the contribution of rep-generated content in closing those cases. In the following chart, out of 200 closed cases, 80 have been resolved wholly or partly using rep-generated content. Therefore, support effectiveness is Divide 80 divided by 200 and then multiply the result by 100 to obtain 40%..

Closed cases are coded in light blue and cases closed with rep-generated content are in dark blue. The closed cases are along the y-axis and date stamps are along the x-axis. On hovering the cursor over the boundary of light blue or dark blue region, a small dialog with two metrics, Closed Cases with Articles Linked and Total Closed Cases, opens. Together, they combine to give you Link Rate. A high Link Rate suggests that rep-generated content is a big role in resolving cases.

Data in Support Effectiveness can be downloaded as a CSV which consists of six columns: Case Number, Case Subject, Case Closed Date, Article Linked (ID), Article Linked (Title), and Link Date. To initiate a download, click . Alternatively, click to share the CSV file with one or more users over email.

Contributor Analytics

While the biggest advantage of adopting the rep-generated content approach is obvious—solve it once, use it often—finding a way to convince a team to adopt it is not obvious. One of the strategies is to start in waves and phases. In an organization of 250 knowledge workers, 30-50 knowledge workers can adopt KCS first. Others can join later.

Contributor Analytics keeps you on top of adoption statistics. Through this report, you can find the knowledge workers who create the most articles and the impact of their work on case resolution.

The email of the knowledge worker who drafts one or more articles is in the first column, Author/Agent. The column is searchable, so you can pull data for a knowledge worker just by looking them up.

Author/Agent lists two kinds of knowledge workers:

  1. Who have written at least one article

  2. Whose at least one article has been linked to a case during the selected Date Range

To find the articles created by a knowledge worker, consult the second column. A dialog opens when you click a row in Articles Created.

In the dialog are the articles created by a knowledge worker. The Service Desk IDs and titles are in the first and second columns, Article ID and Article Title. Through the search box, you can pull up any article if you know its ID or Title. The dialog also contains a third column, Created Date, which tells the creation day of an article. In the fourth and last column, Linked to Cases, is the list of cases the article has been attached to. When an article has been attached to more than one cases, then each case is listed in a new row.

The third column, Contribution Impact, shows the contribution of a knowledge worker in the whole. If your team shared 500 articles in November of which 20 have been written by an agent named Jane, then her contribution is 20 divided by 500 equals four percent.

Last Created tells you the last time a knowledge worker created or updated an article.

Cases Influenced is the rightmost column in Contributor Analysis, and arguably the one with the most impact on your business. Instead of monitoring article volume, as Articles Created does, it monitors content usefulness. In the next image, 60% in the first row means that ******ja@grazitti.com's articles have been linked to 24 different cases in the period specified in Date Range. To find the cases, click the number.

The dialog that opens lists the cases with the ID (Case Number) and title (Case Subject). The title of the created article is also in the dialog (Article Title), along with the person who linked the article to the case (Associated By) and the time of linking (Link Date). You can look up a case or an article through the search box. Data in the first four columns is searchable. Link Date allows SU Admins to sort the table.

The last column in the dialog is Associated Via, which tells you how an article is linked with a case.

Data in Contribution Analysis can be downloaded as a CSV file. To initiate a download, click . Alternatively, click to share the CSV file with one or more users over email. After emailing, ask the recipients to check for a email with the title "Subject: SearchUnify Contributor Analytics."

Articles Usage Analytics

Articles Usage Analytics monitors metrics that can be used to quantify the impact of Knowbler on your support team’s efficiency. The impact, measured in terms of article shares, can be converted into hours of effort and dollars saved.

Here’s a hypothetical scenario. Assume that an agent spends an hour to solve a ticket which reoccurs 20 times in a year. That means, 20 hours of work and pay. The alternative is to document the solution through Knowbler and share link to the published article in response to each customer ticket. Sharing links is faster than typing a solution in an email. Even if the documentation takes 10 hours, you will have more than recovered your investment in the course of a year.

Your savings add up when multiple agents and hundreds of articles are involved. Going over the report at regular intervals, you can also get a handle on who among your agents is most active in creating the most shared articles.

The report is sorted by article IDs which are to be found in the first column, Article ID (#). The octothorpe represents the total number of articles. In the next figure, you can see that the number of articles published. The article ID is 000001045.

Article Title serves two functions: A quick survey of this column provides a reader a gut feeling about the kind of articles being created. A search function helps a reader quickly pull up an article by its title.

Article Origin is a Salesforce Case ID. It captures the case for which the article is created. An agent can share an article on several cases. In the image, you can spot the case IDs for which articles have been created.

Author/Agent captures the email addresses of the support agents who draft the articles. In the next image, both the articles are by ******yap@grazitti.com.

Created Date displays the date and time of creation of the article.

If measuring productivity is your goal then Total Shares is the column you should keep an eye on. It displays the share count of each article.

The number field in each row is clickable and can be utilized to further find out articles shared through SearchUnify. Through it, you can learn: 

What cases the article was attached to (Case Title), whether the article emailed (Shared via Email), posted as a comment on a case page (Shared via Case Comment), attached to a case (Attached to Case). The fourth column, Copied to Clipboard, captures the events when a link to an article is copied, which happens when any of the three sharing options is exercised: email, case comment, or case attachment. It’s sufficient to select an option, the actual sharing doesn’t necessarily have to occur.

Low Impact KB Articles

Discover articles having zero to low impact on case solving with Low Impact KB Articles. The impact is measured in terms of share count.

An article that’s never been shared or shared only one, two, or three times, may require some action. This report helps you identify those low-performing articles and take appropriate action.

The sole section in Low Impact KB Articles is Least Used Articles. You can filter the list using:

  • Max Share Count. One million is a low share count for a popular Hollywood track and one hundred is a high share count for a scientific article exploring the relationship between the theory of topological spaces and number theory. Max Share Count lets you set a popularity parameter that’s valid for your organization. If Max Share Count is set to 4, then all articles shared four or fewer times are considered low impact. You see only the low impact articles when this filter is applied.

  • Last Modified Date is the second filter. You can use it to find articles updated in today, yesterday, last 7 days, this month, last month, or during a custom range.

Except for the serial number (#), the report includes six different columns:

  1. Articles ID. The Article ID number. Users can also use the search functionality to search an article by its ID.

  2. Article Title. The title of the article associated with respective Article IDs. Users can use the search functionality to search an article using its title.

  3. Created By. Name of the author who created the article.

  4. Created Date. Date when the article was created.

  5. Last Modified. Date on which the article was last updated.

  6. Shares. Number of times the article has been shared.

While you click on a number in the Shares column, a pop-up appears on the screen. This pop-up shows the list of cases on which that article has been shared. The screen includes three columns: Case Number, Case Subject, and Link Date.