Lee Romero

Archive for February 28th, 2021

Having introduced some basic, standard, definitions in my previous post, in this one I am going to propose some standard measures derived from those that enable comparisons across solutions. These also are extremely useful for individual solutions where you, as an enterprise search manager, might want to have tools at hand to proactively improve your users’ experience.

A quick recap of what I defined before:

• Search: A single action a user takes that retrieves a set of results. Initiating a searching effort, applying a sort to result, pagination, applying filters would typically all increment this metric.
• Click: A user clicking on a result presented to them.
• Search Session: A sequence of actions (clicks or searches) that are taken in order without changing the search term (more generally, the criteria of the search).
• First Click: The first click within a search session.

Lost Clicks

The first derived measure is one I call “lost clicks”. This measures the raw number of search sessions that resulted in no click:

This is a useful measure that tells you how many times, in total, users initiated a session but found nothing of interest to click on.

You can also think of this as an indicator that measures the number of total failed search sessions.

One more point I’ll make on this is that, because it is a raw number (not a ratio or percentage), it is not useful as a key performance indicator (KPI).

Abandonment rate

Now, finally, to my proposal for a standard measure of the quality of a search solution – a measure that, I think, can be usefully applied to all enterprise search solutions, can be used to drive improvement within a solution, and can be used to compare across such solutions.

That measure is “abandonment rate”, which I define as the percent of sessions that are ‘failed sessions’:

which, after a bit of simplifying, I normally write as:

This measure has some important advantages over a simpler click-rate model (e.g., [success rate] = [click] / [search]). For one thing, it avoids some simple problems that can be caused by a few anomalous users; for a second, it avoids the ‘trap’ of assuming a click is a success.

Anomalous usage patterns

There are two anomalous patterns I see every once in a while:

1. A single dedicated user (or a small number of such users) might page through dozens or hundreds of pages of results (I actually have seen this before!) – generating a LOT of search actions – and yet click on nothing or just a result or two.
   • If every other user found something interesting to click on and did so on the first page of results, the click rate is still artificially lowered by these “extra” searches.
2. Inversely, users who are in a ‘research mode’ of usage (not a known item search) will click on a lot of results (I have also seen instances where a single user clicks on 100s of results all in the same search session).
   • Even if no other user found anything interesting to click on, the click rate is still artificially raised by these “extra” clicks.

By using only the first click and also the search session as the denominator, these scenarios don’t come into play (note that because I am recommending still capturing the simpler ‘search’ and the simpler ‘click’ metrics, you can still do some interesting analyses with these!).

Bad Success and Good Abandonment

The second advantage I mentioned above is more of a philosophical one – the success rate measure as defined builds in more strongly that you are measuring user success. This is a strong statement.

By focusing on abandonment, I find it a more honest view – your metrics don’t build in an assumption that a click is likely a success but, instead, that a failure to find something of interest to click on is more clearly an indication of likely failure.

What do I mean?

When I consider the ideas of “success” and “failure” in a search solution, I always have to remind myself of the good and bad sides of both – what do I mean by that??

• Good success – Good success is a click on a result that was actually useful and what the user needs to do their job. This, ultimately, is what you want to get to – however, because there is no way for a search solution to (at scale) know if any given result is “good” or “useful”, this is impossible to really measure.
• Bad abandonment – This is the flip side – this is how I think of the experience where a user has a search session where they find nothing useful at all. Again, this is the clear definition of failure.

However, there are other possibilities to consider!

• Bad success – This is when a user finds something that appears to be useful or what they need and they click on it, but it turns out to be something entirely different and not useful at all.
  • A classic example of bad success I have seen is in regard to my firm’s branding library (named ‘Brand Space’). For whatever reason, many intranet managers like to create image libraries in their sites and name them ‘Brand Space’ (I think this is because they think of this image library as their own instance of ‘Brand Space’). They then leave that image library exposed in search (we train them not to do so, but sometimes they don’t listen) and if an end user initiates a search session looking for Brand Space, they find the image library in results, click on it, and are likely disappointed (I imagine such a user thinking, “What is this useless web page?”)
  • A different way to think of this is in regard to the perspective of someone who is responsible for a particular type of content (let’s say benefits information for your company) – they may think they know what users *should* access when they search in particular ways and clicking on anything else is an instance of ‘bad success’. I get this but, as the manager of the search solution, I am not in the position of defining what users *should* click on – I cannot read their minds to understand intent.
• Good abandonment – This is when a user finds the information they need right on the search results screen. Technically, such a session would count as ‘abandoned’ even though the user got what they needed.
  • This is exactly the scenario I mentioned in the definition of a ‘click’ in my last post where I would like to define how to measure this but have never been able to figure out a way to do so.

Getting back to my description of how measuring and tracking abandonment rate is better then a success rate – my assumption has been that good abandonment and bad success will always exist for your users, however, good abandonment is likely a much smaller percentage of sessions than bad success and, more importantly, it is much easier to “improve” your search by increasing bad success then decreasing good abandonment.

Conclusion

There is my proposal for a measure to be used to assess search solutions for the quality of the user experience – abandonment rate.

It is not perfect and it is still just an indicator but I have found it incredibly useful to actually drive action for improvement. I’ll share more on this in my next post.