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:
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).
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.
There are two anomalous patterns I see every once in a while:
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!).
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??
However, there are other possibilities to consider!
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.
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.
In my last few posts, I have commented on the lack of standard measures to use for enterprise search (leading to challenges of comparing various solutions to others among other things) and suggested some criteria for what standard measures to use.
In this post, I am going to propose a few basic measures that I think meet the criteria and that any enterprise search solution should be able to provide. The labels are not critical for these, but the meaning of them is, I think, very important.
First, and most important, is a search. A search is a single action in which a user retrieves a set of results from the search engine. Different user experiences may “count” these events differently.
When a user starts the process (in my experience, typically with a search term typed into a box on a web page somewhere), that is a single search.
If that user navigates to a second page of results, that is another search. Navigating to a third page counts as yet another search, etc.
Applying a filter (if the user interface supports such) counts as yet another search.
Re-sorting results counts as yet another search.
In a browser-based experience, even a user simply doing a page refresh counts as another search (though I will also say that in this case, if the interface uses some kind of caching of results, this might not actually truly retrieve a new set of results from the search engine, so this one could be a bit “squishy”).
In a user experience with an infinite scroll, the act of a user scrolling to the bottom of one ‘chunk’ of results and thus triggering the interface to retrieve the next ‘chunk’ also counts as yet another search (this is effectively equivalent to paging through result except it doesn’t require any action by the user).
The second basic measure is the click. A click is counted any time a user clicks on any results in the experience.
Depending on the implementation, differentiating the type of thing a user clicks on (an organic result or a ‘best bet’, etc.) can be useful – but I don’t consider that differentiation critical at the high level.
One thing to note here that I know is a gap – there are some scenarios where a user does not need to click on anything in the search results. The user might meet their information need simply by seeing the search results.
This could be because they just wanted to know if anything was returned at all. It could be because the information they need is visible right on the results screen (the classic example of this would be a search experience that shows people profiles and the display shows some pertinent piece of information like a phone number). In a sophisticated search experience that offers “answers” to question, the answer might be displayed right on the results screen. I have been puzzled about how to measure this scenario for a while. Other than some mechanism on the interface that allows a user to take some action to acknowledge that they achieved there need (“Was this answer useful?”), I’m not sure what that is. Very interested if others have solved this puzzle.
A third important metric is the search session. This is closely related to the search metric, but I do think that it is important to differentiate.
A search session is a series of actions a user takes that, together, constitute an attempt to satisfy a specific information need.
This definition, though, is really not deterministically measurable. There is no meaningful way (unless you can read the user’s mind) to know when they are “done”.
One possibility is to equate a search session to a visit – I find a good definition for this on Wikipedia in the Web analytics article:
A visit or session is defined as a series of page requests or, in the case of tags, image requests from the same uniquely identified client.
In the current solution I am working with, however, we have defined a search session to be a series of actions taken in sequence where the user does not change their search term. The user might navigate through a series of pages of results, reorder them, apply multiple filters, click on one or more results, etc., but, none of these count as another search session.
The rationale for this is that, based on anecdotal discussions with users, users tend to think of an effort using a single search term as a notional “search”. If the user fails with that term, they try another, but that is a different “search”.
Obviously, this is not truly accurate in all situations – if we could meaningfully detect (at scale, meaning across all of our activity) when changing the search term is really a restatement of the same information need vs. a completely different information need, we could do something more accurate, but we are not there, yet.
The last basic measure I propose is the first click.
A first click is counted the first time a user clicks on a result within a search session. If a user clicks on multiple things within a search session, they are all still counted as clicks, but not as first clicks.
If the user starts a new search session (which, in the current solution I work with, means they have changed their search term), then, if they click on some result, that is another first click.
That is the set of basic measures that I think could be useful to establish as a standard.
Next steps – I hope to engage with others working in this domain to refine these and tighten them up (especially a search session). I hope to make some contacts through the Enterprise Search Engine Professionals group on LinkedIn and perhaps other communities for this. If you are interested, please let me know!
In my next post, I will be sharing definitions of some important metrics derived from the basic measures above that I use and provide some examples of each.
In my last post, I wondered about the lack of meaningful standards for evaluating enterprise search implementations.
I did get some excellent comments on the post and also some very useful commentary from a LinkedIn discussion about this topic – I would recommend you read through that discussion. Udo Kruschwitz and Charlie Hull both provided links to some very good resources.
In this post, I thought I would describe what I think to be some important attributes of any standard measures that could be adopted. Here I will be addressing the specific actions to measure – in a subsequent post I will write about how these can be used to actually evaluate a solution.
Measurable
To state the obvious, we need to have metrics that are measurable and objective. Ideally, metrics that directly reflect user interaction with the search solution.
Measures that depend on subjective evaluation or get feedback from users through means other than their direct use of the tool can be very useful but introduce problems in terms of interpretation differences and sustainability.
For example, a feedback function built into the interface (“Are these results useful?” or even a more specific, “Is this specific result useful for you here?”) can provide excellent insight but are used so little that the data is not useful overall.
Surveys of users inevitably fall into the problem of faulty or biased memory – in my experience, users have such a negative perception of enterprise search that individual negative experiences will overwhelm positive experiences with the search when you ask them to recall and assess their experience a day or week after their usage.
Common / Useful to compare implementations
Another important consideration is that a standard for evaluating enterprise search should include aspects of search that are common across the broad variety of solutions you might see.
In addition, they should lend themselves to comparing different solutions in a useful way.
Some implementations might be web-based (in my experience, this is by far the most common way to make enterprise search available). Some might be based on a desktop application or mobile app. Some implementations might depend only on users enterprise search terms to start a search session; some might only support searching based on search terms (no filtering or refining at all). Some implementations might provide a “search as you type” (showing results immediately based on part of what the user has entered). Many variations to consider here.
I would want to have measures that allow me to compare one solution to another – “Is this one better than that one?” “Are there specific user needs where this solution is better than that one?”
Likely to be insightful
Another obvious aspect is that we want to include measures that are likely to be useful.
Useful in what way, though?
My first thought is that it must measure if the solution is useful for the users – does it meet the users’ needs? (With search, I would simplify this to “does it provide the information the user needs efficiently?” but there are likely a lot of other ways to define “useful” even within a search experience.
Operationalizable
I would want all measures I use to be consistently available (no need to “take a measurement” at a given time) and also to not depend on someone actively having to “take a measurement”.
As mentioned above, measures that directly reflect what happens in the user experience are what I would be looking for. In this case, I would add in that the measures should be taken directly from the user experience – data captured into a search log file somewhere or captured via some other means.
This provides a data set that can be reviewed and used at basically any time and which (other than maintaining the system capturing the measurements) don’t require any effort to capture and maintain – the users use the search solution and their activities are captured.
Usable for overall and when broken down by dimensions
Finally, I would expect that measures would support analysis at broad scales and also should support the ability to drill in to details and use the same measures?
Examples of “broad scale” applicability: How good is this search solution overall? How good is my search solution in comparison to the overall industry average? How good are search solutions supporting the needs of users in the XYZ industry? How good are search solutions at supporting “known item” searching in comparison with “exploratory searching”?
Examples of drilling in: Within my user base, how successful are my users by department? How useful is the search solution in different topic areas of content? How good are results for individual, specific search criteria?
Others?
I’m sure I am missing a lot of potential criteria here – What would you add? Remove? Edit?
Over the past several years of working very closely with the enterprise search solution at Deloitte, I have tried to look “outside” as best as I can in order to understand what others in the industry are doing to evaluate their solutions in order to understand where ours ‘fits’.
I’ve attended a number of conferences and webcasts and read papers (many, I’ll admit, that are highlighted by Martin White on Twitter. I can’t recommend a follow of Martin enough!)
One thing I have never found is any common way to evaluate or talk about enterprise search solutions. I have seen several people (including Martin) comment on the relatively little research on enterprise search (as opposed to internet search, which has a lot of research behind it), and I am sure a significant reason for that is that there is no common way to evaluate the solutions.
If we could compare in a systematic way, we could start to understand how to do things like:
Why do we not have a common set of definitions?
One possibility is certainly that I have still not read up enough on the topic – perhaps there is a common set of definitions – if so, feel free to share.
Another possibility is that this is a result of dependency on the metrics that are implemented within the search solutions enterprises are using. I have found that these are useful but they don’t come with a lot of detail or clarity of definition. And, more specifically, they don’t seem common across products. That said, I have relatively limited exposure to multiple search solutions – Again, I would be interested in insights from those who have (perhaps any consultants working in this space?)
And, one more possible driver behind a lack of commonality is the proprietary nature of most implementations. I try to speak externally as frequently as I can, but I am always hesitant (and have been coached) to not be too detailed on the implementation.
I do plan to put up a small series here, though, with some of the more elemental components of our metrics implementation for comparison with anyone who cares to share.
More soon!
Last week, I moderated a discussion for the weekly KMers.org Twitter chat about “The Importance of Search in your KM Solution”.
My intent was to try to get an understanding about how important search is relative to other components of a KM search (connecting people, collecting and managing content, etc.).
It was a good discussion with about a dozen or so people taking part (that I could tell).
You can read through the transcript of the session here. Let me know what you think on the topic!
During the discussion, a great question came up about measuring the success of your search solution (thanks to Ed Dale) which I thought deserved its own discussion, so I have submitted a suggestion for a new topic for an upcoming KMers.org chat.
Please visit the suggestion here and vote for it!
My first post back after too-long a period of time off. I wanted to jump back in and share some concrete thoughts on best bet governance.
I’ve previously written about best bets and how I thought, while not perfect, they were an important part of a search solution. In that post, I also described the process we had adopted for managing best bets, which was a relatively indirect means supported by the search engine we used for the search solution.
Since moving employers, I now have responsibility for a local search solution as well as input on an enterprise search solution where neither of the search engines supports a similar model. Instead, both support the (more typical?) model where you identify particular search terms that you feel need to have a best bet and you then need to identify a specific target (perhaps multiple targets) for those search terms.
This model offers some advantages such as specificity in the results and the ability to actively determine what search terms have a best bet that will show.
This model also offers some disadvantages, the primary one (in my mind) being that they must be managed – you must have a means to identify which terms should have best bets and which targets those terms should show as a best bet. This implies some kind of manual management, which, in resource-constrained environments, can be a challenge. As noted in my previous article, others have provided insight about how they have implemented and how they manage best bets.
Now having responsibility for a search solution requiring manual management of best bets, we’ve faced the same questions of governance and management and I thought I would share the governance model we’ve adopted. I did review many of the previous writings on this to help shape these, so thanks to those who have written before on the topic!
Our governance model is largely based on trying to provide a framework for consistency and usability of our best bets. We need some way to ensure we do not spend inordinate time on managing requests while also ensuring that we can identify new, valuable search terms and targets for best bets.
Without further ado, here is an overview of the governance we are using:
The one interesting experience we’ve had so far with this governance model is that we get a lot of push back from site publishers who want to provide a lengthy laundry list of terms for their site, even when 75% of that list is never used (or at least in a twelve month period we’ll sometimes check). They seem convinced that there is value in setting up best bets for terms even when you can show that there is none. We are currently making changes in the way we manage best bets and also in how we can use these desirable terms to enhance the organic results directly. More on that later.
There you have our current governance model. Not too fancy or complicated and still not ideal, but it’s working for us and we recognize that it’s a work in progress.
Now that I have the “monkey off my back” in terms of getting a new post published, I plan to re-start regular writing. Check back soon for more on search, content management and taxonomy!
Last summer, I read the article by Kas Thomas from CMS Watch titled “Best Bets – a Worst Practice” with some interest. I found his thesis to be provocative and posted a note to the SearchCoP community asking for other’s insights on the use of Best Bets. I received a number of responses taking some issue with Kas’ concept of what best bets is and some also some responses describing different means to manage best bets (hopefully without requiring the “serious amounts of human intervention” described by Kas.
In this post, I’ll provide a summary of sorts and also describe some of the ways described for managing best bets and also the way we have managed best bets.
Kas’ thesis is that best bets are not a good practice because they are largely a hack layered on top of a search engine and require significant manual intervention. Further, if your search engine isn’t already providing access to appropriate “best bets” for queries, you should get yourself a new search engine.
Some of the most interesting comments from the thread of discussion on the SearchCoP include (I’ll try to provide as cohesive picture of sentiment as I can but will only provide parts of the discussion – if I have portrayed intent incorrectly – that’s my fault and not the original author):
From Tim W:
“Search analytics are not used to determine BB … BB are links commonly used, enterprise resources that the search engine may not always rank highly because for a number of reasons. For example, lack of metadata, lack of links to the resource and content that does not reflect how people might look for the document. Perhaps it is an application and not a document at all.”
From Walter U:
“…manual Best Bets are expensive and error-prone. I consider them a last resort.”
From Jon T:
“Best Bets are not just about pushing certain results to the top. It is also about providing confidence in the results to users.
If you separate out Best Bets from the automatic results, it will show a user that these have been manually singled out as great content – a sign that some quality review has been applied.”
From Avi R:
“Best Bets can be hard to manage, because they require resources.
If no one keeps checking on them, they become stale, full of old content and bad links.
…
Best Bets are also incredibly useful.
They’re good for linking to content that can’t be indexed, and may even be on another site entirely. They’re good for dealing with … all the sorts of things that are obvious to humans but don’t fit the search paradigm.”
So, lots of differing opinions on best bets and their utility, I guess.
A few more pieces of background for you to consider: Walter U has posted on his blog (Most Casual Observer) a great piece titled “Good to Great Search” that discusses best bets (among other things); and, Dennis Deacon posted an article titled, “Enterprise Search Engine Best Bets – Pros & Cons” (which was also referenced in Kas Thomas’ post). Good reading on both – go take a look at them!
My own opinion – I believe that best bets are an important piece of search and agree with Jon T’s comment above that their presence (and, hopefully, quality!) give users some confidence that there is some human intelligence going into the presentation of the search results as a whole. I also have to agree with Kas’s argument that search engines should be able to consistently place the “right” item at the top of results, but I do not believe any search engine is really able to today – there are still many issues to deal with (see details in my posts on coverage, identity, and relevance for my own insights on some of the major issues).
That being said, I also agree that you need to manage best bets in a way that does not cost your organization more than their value – or to manage them in a way that the value is realized in multiple ways.
Contrary to what Tim W says, and as I have written about in my posts on search analytics (especially in the use of search results usage), I do believe you can use search analytics to inform your best bets but they do not provide a complete solution by any means.
From here on out, I’ll describe some of the ways best bets can be managed – the first few will be summary of what people shared on the SearchCoP community and then I’ll provide some more detail on how we have managed them. The emphasis (bolding) is my own to highlight some of what I think are important points of differentiation.
From Tim W:
“We have a company Intranet index; kind of a phone book for web sites (A B C D…Z). It’s been around for a long time. If you want your web site listed in the company index, it must be registered in our “Content Tracker” application. Basically, the Content Tracker allows content owners to register their web site name, URL, add a description, metadata and an expiration date. This simple database table drives the Intranet index. The content owner must update their record once per year or it expires out of the index.
This database was never intended for Enterprise Search but it has proven to be a great source for Best Bets. We point our ODBC Database Fetch (Autonomy crawler) at the SQL database for the Content Tracker and we got instant, user-driven, high quality Best Bets.
Instead of managing 150+ Best Bets myself, we now have around 800 user-managed Best Bets. They expire out of the search engine if the content owner doesn’t update their record once per year. It has proven very effective for web content. In effect, we’ve turned over management of Best Bets to the collective wisdom of the employees.”
From Jim S:
“We have added an enterprise/business group best bet key word/phrase meta data.
All documents that are best bet are hosted through our WCM and have a keyword meta tag added to indicate they are a best bet. This list is limited and managed through a steering team and search administrator. We primarily only do best bets for popular searches. Employee can suggest a best bet – both the term and the associated link(s). It is collaborative/wiki like but still moderated and in the end approved or rejected by a team. There is probably less than 1 best bet suggestion a month.
If a document is removed or deleted the meta data tag also is removed and the best bet disappears automatically.
Our WCM also has a required review date for all content. The date is adjustable so that content will be deactivated at a specific date if the date is not extended. This is great for posting information that has a short life as well as requiring content owners to interact with the content at least every 30 Months (maximum) to verify that the content is still relevant to the audience. The Content is not removed from the system, rather it’s deactivated (unpublished) so it no longer accessible and the dynamic links and search index automatically remove the invalid references. The content owner can reactivate it by setting the review date into the future.
If an external link (not one in our WCM) is classified as a best bet then a WCM redirect page is created that stores the best bet meta tag. Of course it has a review/expiration so the link doesn’t go on forever and our link testing can flag if the link is no longer responding. If the document is in the DMS it would rarely be deleted. In normal cases it would be archived and a archive note would be placed to indicate the change. Thus no broken links.
Good content engineering on the front end will help automate the maintenance on the back end to keep the quality in search high.“
The first process is external to the content and doesn’t require modifying the content (assuming I’m understanding Tim’s description correctly). There are obvious pros and cons to this approach.
By contrast, the second process embeds the “best bet” attribution in the content (perhaps more accurately in the content management system around the content) and also embeds the content in a larger management process – again, some obvious pros and cons to the approach.
Now for a description of our process -The process and tools in place in our solution are similar to the description provided by Tim W. I spoke about this topic at the Enterprise Search Summit West in November 2007, so you might be able to find the presentation for it there (though I could not just now in a few minutes of searching).
With the search engine we use, the results displayed in best bets are actually just a secondary search performed when a user performs any search – the engine searches the standard corpus (whatever context the user has chosen, which would normally default to “everything”) and separately searches a specific index that include all content that is a potential best bet.
The top 5 (a number that’s configurable) results that match the user’s search from the best bets index are displayed above the regular results and are designated “best bets”.
How do items get into the best bets index, then? Similar to what Tim W describes, on our intranet, we have an “A-Z index” – in our case, it’s a web page that provides a list of all of the resources that have been identified as “important” at some point in the past by a user. (The A-Z index does provide category pages that provide subsets of links, but the main A-Z index includes all items so the sub-pages are not really relevant here.)
So the simple answer to, “How do items get into the best bets index?” is, “They are added to the A-Z index!” The longer answer is that users (any user) can request an item be added to the A-Z index and there is then a simple review process to get it into the A-Z index. We have defined some specific criteria for entries added to the A-Z, several of which are related to ensuring quality search results for the new item, so when a request is submitted, it is reviewed against these criteria and only added if it meets all of the criteria. Typically, findability is not something considered by the submitter, so there will be a cycle with the submitter to improve the findability of the item being added (normally, this would include improving the title of the item, adding keywords and a good description).
Once an item is added to the A-Z index, it is a potential best bet. The search engine indexes the items in the A-Z through a web crawler that is configured to start with the A-Z index page and goes just one link away from that (i.e., it only indexes items directly linked to from the A-Z index).
In this process, there is no way to directly map specific searches (keywords) to specific results showing up in best bets. The best bets will show up in the results for a given search based on normally calculated relevance for the search. However, the best bet population numbers only about 800 items instead of the roughly half million items that might show up in the regular results – as long as the targets in the A-Z index have good titles and are tagged with the proper keywords and description, they will normally show up in best bets results for those words.
Some advantages of this approach:
Some disadvantages of this approach
Having written about what I consider to be the principles of enterprise search, about people search in the enterprise, about search analytics and several other topics related to search in some detail, I thought I would share some insights on a role I have called search analyst – the person(s) who are responsible for the care and feeding of an enterprise search solution. The purpose of this post is to share some thoughts and experiences and help others who might be facing a problem similar to what my team faced several years back – we had a search solution in place that no one was maintaining and we needed to figure out what to do to improve it.
Regarding the name of the role – when this role first came into being in my company, I did not know what to call the role, exactly, but we started using the term search analyst because it related to the domain (search) and reflected the fact that the role was detailed (analytical) but was not a technical job like a developer. Subsequently, I’ve heard the term used by others so it seems to be fairly common terminology now – it’s possible that by now I’ve muddled the timeline enough in my head that I had heard the term prior to using it but just don’t recall that!
What does a search analyst do for you? The short answer is that a search analyst is the point person for improving the quality of results in your search solution. The longer answer is that a search analyst needs to:
In order to define success for a search analyst, you need to set some specific objectives for the search analyst(s). Ultimately, given the job description, they translate to measuring how the search analyst has been successful in improving search, but here are some specific suggestions about how you might measure that:
Another common question I’ve received is what percentage of time should a search analyst expect to spend on this type of work? Some organizations may have large enough search needs to warrant multiple full-time people on this task but we are not such an organization and I suspect many other organizations will be in the same situation. So you might have someone who splits their time among several roles and this is just one of them.
I don’t have a full answer to the question because, ultimately, it will depend on the value your organization does place on search. My experience has been that in an organization of approximately 5-6,000 users (employees) covering a total corpus of about a million items spread across several dozen sites / applications / repositories, spending about .25 FTE on search analyst tasks seems to provide for steady improvements and progress.
Spending less than that (down to about .1 FTE), I’ve found, results in a “steady state” – no real improvements but at least the solution does not seem to degrade. Obviously, spending more than that could result in better improvements but I find that dependence on others (content owners, application owners, etc.) can be a limiting factor in effectiveness – full organizational support for the efforts of the search analyst (giving the search analyst a voice in prioritization of work) can help alleviate that. (A search analyst with a software development background may find this less of an issue as, depending on your organization, you may find yourself less tied to development resources than you would otherwise be, though this also likely raises your own FTE commitment.)
The above description is worded as if your organization has a single person focused on search analyst responsibilities. It might also be useful to spread the responsibility among multiple people. One reason would be if your enterprise’s search solution is large enough to warrant a team of people instead of a single person. A second would be that it can be useful to have different search analysts focused (perhaps part time still for each of them) on different content areas. In this second situation, you will want to be careful about how “territorial” search analysts are, especially in the face of significant new content sources (you want to ensure that someone takes on whatever responsibility there might be for that content in regards to ensuring good findability).
So far I’ve provided a description of the role of a search analyst, suggestions for objectives you can assign to a search analyst and at least an idea of the time commitment you might expect to have an effective search analyst. But, if you were looking to staff such a position, what kinds of skills should you look for? Here are my thoughts:
If your search needs warrant more than one person focused on improving your enterprise search solution, as much overlap in the above as feasible is good, though you may have team members specializing in some skills while others focus on other areas.
Another important issue to address is where in your overall organization should the search analyst responsibility rest? I don’t have a good answer for this question and am interested in others’ opinions. My own experiences:
Enough about my own insights – What does anyone else have to share about how you perceive this role? Where does it fit in your organization? What are your objectives for this role?
In my previous two posts, I’ve written about some basic search analytics and then some more advanced analysis you can also apply. In this post, I’ll write about the types of analysis you can and should be doing on data captured about the usage of search results from your search solution. This is largely a topic that could be in the “advanced” analytics topic but for our search solution, it is not built into the search solution and has been implemented only in the last year through some custom work, so it feels different enough (to me) and also has enough details within it that I decided to break it out.
When I first started working on our search solution and dug into the reports and data we had available about search behavior, I found we had things like:
and much more. However, I was frustrated by this because it did not give me a very complete picture. We could see the searches people were using – at least the top searches – but we could not get any indication of “success” or what people found useful in search, even. The closest we got from the reports was the last item listed above, which in a typical report might look something like:
Search Results Pages
However, all this really reflects is the percentage of each page number visited by a searcher – so 95% of users never go beyond page 1 and the engine assumes that means they found what they wanted there. That’s a very bad assumption, obviously.
I wanted to be able to understand what people were actually clicking on (if anything) when they performed a search! I ended up solving this with a very simple solution (simple once I thought of it). I believe this emulates what Google (and probably many other search engines) do. I built a simple servlet that takes a number of parameters, including a URL (encoded) and the various pieces of data about a search result target and stores an event in a database from those parameters and then forwards the user to the desired URL. Then the search results page was updated to provide the URL for that servlet in the search results instead of the direct URL to the target. That’s been in place for a while now and the data is extremely useful!
By way of explanation, the following are the data elements being captured for each “click” on a search result:
This data provides for a lot of insight on behavior. You can guess what someone might be looking for based on understanding the searches they are performing but you can come a lot closer to understanding what they’re really looking for by understanding what they actually accessed. Of course, it’s important to remember that this does not really necessarily equate to the user finding what they are looking for, but may only indicate which result looks most attractive to them, so there is still some uncertainty in understand this.
While I ended up having to do some custom development to achieve this, some search engines will capture this type of data, so you might have access to all of this without any special effort on your part!
Also – I assume that it would be possible to capture a lot of this using a standard web analytics tool as well – I had several discussions with our web analytics vendor about this but had some resource constraints that kept it from getting implemented and also it seemed it would depend in part on the target of the click being instrumented in the right way (having JavaScript in it to capture the event). So any page that did not have that (say a web application whose template could not be modified) or any document (something like a PDF, etc) would likely not be captured correctly.
Given the type of data described above, here are some of the questions and actions you can take as a search analyst:
You can also combine data from this source with data from your web analytics solution to do some additional analysis. If you capture the search usage data in your web analytics tool (as I mention above should be possible), doing this type of analysis should be much easier, too!
Here’s a wrap (for now) on the types of actionable metrics you might consider for your search program. I’ve covered some basic metrics that just about any search engine should be able to support; then some more complex metrics (requiring combining data from other sources or some kind of processing on the data used for the basic metrics) and in this post, I’ve covered some data and analysis that provides a more comprehensive picture of the overall flow of a user through your search solution.
There are a lot more interesting questions I’ve come up with in the time I’ve had access to the data described above and also with the data that I discussed in my previous two posts, but many of them seem a bit academic and I have not been able to identify possible actions to take based on the insights from them.
Please share your thoughts or, if you would, point me to any other resources you might know of in this area!
In my last post, I provided a description of some basic metrics you might want to look into using for your search solution (assuming you’re not already). In this post, I’ll describe a few more metrics that may take a bit more effort to pull together (depending on your search engine).
First up – there is quite a lot of insight to be gained from combining your search analytics data with your web analytics data. It is even possible to capture almost all of your search analytics in your web analytics solution which makes this combination easier, though that can take work. For your external site, it’s also very likely that your web analytics solution will provide insight on the searches that lead people to your site.
A first useful piece of analysis you can perform is to review your top N searches, perform the same searches yourself and review the resulting top target’s usage as reported in your web analytics tool.
A second step would be to review your web analytics report for the most highly used content on your site. For the most highly utilized targets, determine what are the obvious searches that should expose those targets and then try those searches out and see where the highly used targets fall in the results.
Another fruitful area to explore is to consider what people actually use from search results after they’ve done a search (do they click on the first item, second? what is the most common target for a given keyword? Etc.). I’ll post about this separately.
I’m sure there are other areas that could be explored here – please share if you have some ideas.
When I first got involved in supporting a search solution, I spent some time understanding the reports I got from my search engine. We had our engine configured to provide reports on a weekly basis and the reports provided the top 100 searches for the week. All very interesting and as we started out, we tried to understand (given limited time to invest) how best to use the insight from just these 100 searches each week.
We quickly realized that there was no really good, sustainable answer and this was compounded by the fact that the engine reported two searches as different searches if there was *any* difference between two searches (even something as simple as case difference, even though the engine itself does not consider case when doing a search – go figure).
In order to see the forest for the trees, we decided what would be desirable is to categorize the searches – associate individual searches with a larger grouping that allows us to focus at a higher level. The question was how best to do this?
Soon after trying to work out how to do this, I attended Enterprise Search Summit West 2007 and attended a session titled “Taxonomize Your Search Logs” by Marilyn Chartrand from Kaiser Permanente. She spoke about exactly this topic, and, more specifically, the value of doing this as a way to understand search behavior better, to be able to talk to stakeholders in ways that make more sense to them, and more.
Marilyn’s approach was to have a database (she showed it to me and I think it was actually in a taxonomy tool but I don’t recall the details – sorry!) where she maintained a mapping from individual search terms to the taxonomy values.
After that, I’ve started working on the same type of structure and have made good headway. Further, I’ve also managed to have a way to capture every single search (not just the top N) into a SQL database so that it’s possible to view the “long tail” and categorize that as well. I still don’t have a good automated solution to anything like auto-categorizing the terms but the level of re-use from one reporting period to the next is high enough that dumping in a new period’s data requires categorization of only part of the new data. [Updated 26 Jan 2009 to add the following] Part of the challenge is that you will likely want to apply many of the same textual conversions to your database of captured searches that are applied by your search engine – synonyms, stemming, lemmatization, etc. These conversions can help simplify the categorization of the captured searches.
Anyway – the types of questions this enables you to answer and why it can be useful include:
Another useful type of analysis you can perform on search data is to look at simple metrics of the searches. Louis Rosenfeld identified several of these – I’m including those here and a few additional thoughts.
Chart of Searches per Word Count
Chart of Search Length to number of searches
Another interesting view of your search data is hinted at by the discussion above of “secondary” search words – words that are used in conjunction with other words. I have not yet managed to complete this view (lack of time and, frankly, the volume of data is a bit daunting with the tools I’ve tried).
The idea is to parse your searches into their constituent words and then build a network between the words, where the each word is a node and the links between the words represent the strength of the connection between the words – where “strength” is the number of times those two words appear in the same searches.
Having this available as a visual tool to explore words in search seems like it would be valuable as a way to understand their relationships and could give good insight on the overall information needs of your searchers.
The cost (in myown time if nothing else) of taking the data and manipulating it into a format that could then be exposed in this, however, has been high enough to keep me from doing it without some more concrete ideas for what actionable steps I could take from the insight gained. I’m just not confident enough to think that this would expose anything much more than “the most common words used tend to be used together most commonly”.
I’m missing a lot of interesting additional types of analyses above – feel free to share your thoughts and ideas.
In my next post, I’ll explore in some more detail the insights to be gained from analyzing what people are using in search results (not just what people are searching for).