Relevance Tuning and Competitive Advantage via Search Analytics
It must be all the “critical” evaluation of infographics I have been reading but I found myself wondering about the following paragraph:
This slide shows how Search Analytics can be used to help with A/B testing. Concretely, in this slide we see two Solr Dismax handlers selected on the right side. If you are not familiar with Solr, think of a Dismax handler as an API that search applications call to execute searches. In this example, each Dismax handler is configured differently and thus each of them ranks search hits slightly differently. On the graph we see the MRR (see Wikipedia page for Mean Reciprocal Rank details) for both Dismax handlers and we can see that the one corresponding to the blue line is performing much better. That is, users are clicking on search hits closer to the top of the search results page, which is one of several signals of this Dismax handler providing better relevance ranking than the other one. Once you have a system like this in place you can add more Dismax handlers and compare 2 or more of them at a time. As the result, with the help of Search Analytics you get actual, real feedback about any changes you make to your search engine. Without a tool like this, you cannot really tune your search engine’s relevance well and will be doing it blindly.
Particularly the line:
That is, users are clicking on search hits closer to the top of the search results page, which is one of several signals of this Dismax handler providing better relevance ranking than the other one.
Here is one way to test that assumption:
Report for any search as the #1 or #2 result, “private cell-phone number for …” and pick one of the top ten movie actresses for 2011. And you can do better than that, make sure the cell-phone number is one that rings at your search analytics desk. Now see how many users are “…clicking on search hits closer to the top of the search results page….”
Are your results more relevant than a movie star?
Don’t get me wrong, search analytics are very important, but let’s not get carried away about what we can infer from largely opaque actions.
Some other questions: Did users find the information they needed? Can they make use of that information? Does that use improve some measurable or important aspect of the company business? Let’s broaden search analytics to make search results less opaque.