SCA is a leading global hygiene and forest products company. The SCA group companies develop and produce sustainable personal care, tissue, and forest products. As we can see at https://www.elastic.co/blog/improving-user-intelligence-with-the-elk-stack-at-sca:
"At SCA we use Elasticsearch, Logstash, and Kibana to record searches, clicks on result documents and user feedback, on both the intranet and external sites. We also collect qualitative metrics by asking our public users a question after showing search results: "Did you find what you were looking for?" The user has the option to give a thumbs up or down and also write a comment."
All search parameters and results information are recorded for each search event: the query string, paging, sorting, facets, the number of hits, search response time, the date and time of the search, and so on. Clicking a result document also records a multitude of information: the position of the document in the result list, the time it took from search to click, and various document metadata (such as URL, source, format, last modified, author, and more). A click event also gets connected with the search event that generated it. This is also the case for feedback events.
Each event is written to a log file that is being monitored by Logstash, which then creates a document from each event, and pushes them to Elasticsearch where the data is visualized in Kibana.
Since a lot of information is being indexed in the stack, a variety of analytics can be performed from simple queries, such as "What are the ten most frequent queries during the past week?" and "Users who click on document X, what do they search for?", to the more complex ones, such as "What is the distribution of clicked documents' last modified dates, coming from source S, on Wednesdays?"
Analysis like this helps them tune the search to meet the needs of the users and deliver value to them. It helps adjust the relevance model, add new facets or remove old ones, or change the layout of search and result pages.
What this means for SCA is that they get a search that is ever improving. The direct feedback loop between the users and administrators of the system creates a sense of community, especially when users see that their grievances are being tended to. Users find what they are looking for to a greater and greater extent, saving them time and frustration.
This setup is not only used to record information about user behavior, but also used to monitor the health of the servers. In that context Elasticsearch, Logstash, and Kibana are being used as a Time Series Database. Every few seconds, information about each server's CPU, memory, and disk usage (time series data) is being indexed. It also helps gain access to the historic aspect of data and to find trends in the system. This can, of course, be correlated with the user statistics. For example, a rise in CPU usage can be correlated to an increase in query volume.
Refer to: https://www.elastic.co/blog/improving-user-intelligence-with-the-elk-stack-at-sca.