During the design of our Elasticsearch setup and its search queries, we often make assumptions on how the customer will use our search engine.

In this lightning talk I will show you how we can leverage real world user data to tune our Elasticsearch Full-Text search based on a quantitative analysis. I will cover the following questions:

- What are the key metrics used for information retrieval in full-text searches?
- How can we leverage real world user data to improve our full-text search?
- How can we run a quantitative analysis on our full-text search engine?
- How can we make sure that changing our Elasticsearch query does not negatively affect the overall quality of our full-text search engine?
- How can we sustainably improve the quality of our full-text search engine?

I will dive into defining our key metrics, collecting real world user data, running a quantitative analysis on your full-text search, and finally iterating to improve our key metrics.