Elastic Workplace Search collects and stores product usage data.
In this video, you’ll learn how to leverage this data by building a dashboard in Kibana.
Elastic Workplace Search will store information about each of the queries and clicks of your
users.
This information is stored in an Elasticsearch index and you can use Kibana to analyze it.
To visualize these metrics, you start by creating an index pattern.
In Kibana, on the main menu, select Stack Management, then Index Patterns.
Select Create index pattern. The data we are looking for is stored in a hidden index.
Toggle this button to include hidden indices, then create an index pattern with the following
pattern.
Choose @timestamp for the time field.
After creating the index pattern, you can see the different fields from this index pattern.
These indices contain data related to your users’ interaction with your search engine.
For every search or click by a user, a new document is indexed into the log index.
In this video, we’ll explore the most important fields.
After setting up the index pattern, you are ready to create a dashboard with visualizations!
In the main menu, select Dashboard under Analytics.
First, we’ll add a visualization that shows the number of queries performed every day
to get some insight into daily search volume.
Click Create visualization to create a new visualization.
This opens the Lens drag and drop editor.
Drag and drop the @timestamp field in the Horizontal axis and use the count for the
Vertical axis.
Queries and clicks events are both stored in the same index, if you want to differentiate
them, you can add a filter in your visualization.
Let’s add a filter by adding the query bar to only show the queries events.
You now have the distribution of your users’ queries over time.
You can change the time interval by selecting @timestamp and choosing a new interval.
After creating the visualization, click on Save and return.
This visualization currently has no title, let’s update it.
You just created a visualization that represents the daily number of queries.
Next, we’ll create a new visualization that displays the most popular searches.
Create a new Lens panel.
We’ll use the SearchMetrics.query.value field, which is used to store the raw queries
typed by the users.
Drag and drop this field into the Horizontal axis.
Choose the count for the Vertical axis.
Notice that Lens will give you suggestions on the bottom.
Select the donut and save this new visualization.
Let’s add the name “Top Queries” to this visualization.
You are starting to gain a good understanding of the queries that your users are running.
Now let’s explore the clicks events.
When a user clicks on a result, a click event is generated containing information about
the document selected by the user.
Let’s start with a simple one.
Create a Lens panel and choose a metric visualization.
In the metric configuration, choose the average of the field SearchMetrics.click.result.rank.
This is the field that gives you the rank of the document selected by the user.
Edit the display name to improve visibility.
Save this visualization to add it to your dashboard.
Finally, it is interesting to learn which content sources the users are clicking most.
Let’s analyze the clicks per content source.
Once again create a new Lens visualization.
Drag and drop the field SearchMetrics.click.result.service_type in the Horizontal axis.
This field stores the name of the source of the result clicked by the user during the
search.
Choose the count for the Vertical axis.
Choose a horizontal bar chart.
Click on Save and return to add this visualization to your dashboard.
Name this visualization “Clicks per content source”.
You can reorganize your visualization by changing the size or the position.
Thanks for watching this short video on how to analyze Workplace Search usage in Kibana.
You learned how to leverage and gain insight from the data collected by Elastic Workplace
Search by creating a dashboard and visualizations based on it.
Workplace Search stores many other fields.
We encourage you to explore them to improve the dashboard we created.