So far, we’ve trod the nightmare hellscape that is terrible enterprise search along with the path to something much better. Then a quick taxonomy dive into the four main types of enterprise search engine and a rundown of critical capabilities.

Another crucial part of any enterprise search deployment is tracking its success and usage. With other search projects like your company’s product catalog, it’s much more straightforward: Customers either purchase something or they don’t. With enterprise search, there’s a lot more nuance in trying to prove that a new system has increased productivity and justified its investment.

Application Performance

The first set of metrics to measure and track are oriented around the scalability and performance of your search applications and the overall tech stack they are sitting on top of.

Query volume measures how many queries per second are coming into the system and will be a function of how large your user base is and how many of them you expect to be using the app concurrently.

Query response time looks at how much time it takes the system to receive a search query, process it, fetch results, and serve them back to the user.

Number of concurrent users can show you how well the system performs with various volumes of users using it as well as if the system can scale to handle the stress of all those users. Smaller organizations with fewer overall users will find this less important.

Index size is how large the search engine’s index is and will indicate if the searcy system can support the index where it is – and how it is expected to grow.

Number of zero results queries is a report showing you which queries are returning zero results, giving the user a dead end.

Engagement Metrics

On the human side, you’ll want to measure how users are actually using the search app.

Number of active users is usually calculated as monthly active users, how many users have used the system in the last 30 days.

User satisfaction scores are a softer measure to be sure, but getting feedback from the users directly on how they feel about the search app can be valuable in proving success and efficacy of the API.

Clickthrough from a search result shows how frequently users ended up viewing a document or downloading it after searching.

User behaviors and signals track behaviors like viewing documents, or downloading, bookmarking, sharing, or rating them.

Be Sure to Benchmark!

And if you are moving to a new search platform from an older legacy one, be sure you capture enough data so you can present a good before and after picture to show the success of your initiative and ROI.