Lee Romero

Archive for the ‘Enterprise Search’ Category

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.

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.

Search

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).

Click

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.

Search Session

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.

First Click

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.

Conclusion and what’s next

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:

• Identify common use cases that are visible in user behavior (via metrics)
• Compare how ‘good’ different solutions are at meeting the core need (an employee needs to access some resource to do their job)
• Compare different industries approaches to information seeking (again, as identified by user behavior via metrics) – for example, do users search differently in industrial companies vs. professional services companies vs. research companies?

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:

• We will accept best bet requests from all users, though most requests come from site publishers on our portal.  Most of our best bets have web sites as targets, though about 30% have individual pieces of published content (documents) as targets.  As managers of the search solution, my team will also identify best bets when appropriate.
• When we receive a request for a new best bet, we review the request against the following the following criteria:
  • No more than five targets can be identified for any one search term, though we prefer to keep it to one or two targets.
    • Any request for a best bet that would result in more than 2 targets for the search term forces a review of usage of the targets (usage is measured by our web analytics solution for both sites and published content).
    • The overall usage of the targets will identify if one or more targets should be dropped.
  • For a given target, no more than 20 individual search terms can be identified.  Typically, we try to keep this to fewer than 5 when possible.
  • If a target is identified as a best bet target that has not had a best bet search term associated with it previously, we confirm that it is either a highly used piece of content or that it is a significant new piece that is highly known or publicized (or may soon be by way of some type of marketing).
  • We also review the search terms identified for the best bet.  We will not use search terms with little to no usage during the previous 3 months.
  • We will not set up a best bet search term that matches the title of the target.  The relevancy algorithm for our search engine heavily weights titles, so this is not necessary.
  • We do prefer that the best bet search terms do have a logical connection to the title or summary of the target.  This ensures that a user will understand the connection between their search terms and a resulting best bet.  This is not a hard requirement, but a preference.  We do allow for spelling variants, synonyms, pluralized forms, etc.
  • We prefer terms that use words from our global taxonomy.
• Our governance (management process, really) for managing best bets includes:.
  • Our search analyst reviews the usage of each best bet term.
    • If usage over an extended time is too low to warrant the best bet term, it is removed.
  • We also plan to use path analysis (pending some enhancements needed as this is written) to determine if, for specific terms, the best bet selections are used preferentially.  If that is found to not be the case, our intent is that the best bet target is removed.
  • We have integrated the best bet management into both our site life cycle process and our content life cycle
    • With the first, when we are retiring a site or changing the URL of a site we know to remove or update the best bet target
    • With the second, as content is retired, the best bets are removed
    • In each of these cases, we also evaluate the terms to see if there could be other good targets to use.

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.

Are Best Bets Worth the Investment?

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.

Managing Best Bets

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.

Managing Best Bets at Novell

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:

• This approach works with our search engine and takes advantage of a long-standing “solution” our users are used to (the A-Z index has long been part of our intranet and many users turn to the A-Z index whenever they need to find anything, so its importance is well-ingrained in the company).
• Given that the items in the A-Z index have been identified at some point in the past as “important”, we can arguably say that everything that should possibly be a best bet is included.
• We have a point in a process to enforce some findability requirements (when a new item is added).
• The items included can be any web resource, regardless of where it is (no need to be on our web site or in our CM system)
• This approach provides a somewhat automated way to keep the A-Z index cleaned up – the search engine identifies broken links as it indexes content and by monitoring those for the best bets index, we know when content included the A-Z has been removed.
• Because this approach depends on the “organic” results from the engine (just on a specially-selected subset of content), we do not have to directly manage keyword-to-result mapping – we delegate that to the content owner (by way of assigning appropriate keywords in the content).

Some disadvantages of this approach

• The tool we use to manage the A-Z index content is a database but, it is not integrated with our content management system. Most specifically, it does not take advantage of automated expiration (or notification about expiration).
• As a follow-on from the above point, there is no systematically enforced review cycle on individual items to ensure they are still relevant.
• Because this approach depends on the organic results from the engine, we can not directly map keywords to specific results. (Both a good and bad thing, I guess!)
• Because the index is generated using a web crawl (and not indexing a database directly for example), some targets (especially web applications) still end up not showing particular well because it might not be possible to have the home page of the application modified to include better keywords or descriptions or (in the face of our single sign-on solution), sometimes a complex set of redirects results in the crawler not indexing the “right” target.

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?

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:

• Review data related to your search solution and understand its implications
• Formulate new questions to continually improve upon the insights gained from the data
• Formulate action plans from insights gained from monitoring that data in order to continually improve your search solution – this requires that the search analyst understand your search solution at a deep enough level of understand to be able to translate analytic insights into specific changes or actions
• Follow through on those action plans and work with others as necessary to effect the necessary changes

Measuring Success as a Search Analyst

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:

• Execute a regular survey of users of your search (perhaps annually?) – this can be a very direct way of measuring increased quality, though ensuring you get good coverage of the target audience (and reflect appropriate demographics) may be a challenge. We have used this and results do reflect increases in satisfaction.
• Provide ability to rate search results – a more direct way than a survey to measure satisfaction with search, though implementing it and integrating it with the search experience in a way that invites users to provide feedback can be a challenge.
• Measure overall increase in search usage – No need to directly work with users of your search but also begs the question about whether increasing search usage is really a measure of quality.
• Measure increase in search usage relative to visits to your site (assuming your search solution is integrated with your intranet, for example) – I mentioned this in post on advanced metrics as a metric to monitor. I think this can be more useful than just measuring increases in usage, however, it might also reflect changes (good or bad) in navigation as much as changes in search.
• Measure overall coverage of search (total potential targets) – How much content does your search solution make available as potential search results? By itself, increases in this do not equate to an improvement in search but if combined with other metrics that more directly measure quality of results, increases in coverage do translate to a user being more likely to get what they need from search. In other words, if you can assure users that they can gain direct access to more potential results in search while also ensuring that the quality of results returned is at least as good as before, that’s a good thing. On the other hand, if adding in new content pollutes the experience with many less-relevant search results, you are not doing anyone any favors by including them.
• Measure number of specific enhancements / changes made to improve the quality of results – especially for highly sought content. Assuming you track the specific changes made, a measure of effectiveness could be to track how many changes a search analyst has made over a given time period. Did the search analyst effect 5 changes in a month? 50? Again, the number itself doesn’t directly reflect improvements (some of those changes could have been deleterious to search quality) but it can be an indicator of value.

Time Commitment for a Search Analyst

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).

What Skills does a Search Analyst Need

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:

• First, I would expect that a search analyst is a capable business analyst. I would expect that anyone who I would consider a capable search analyst would be able to also work with business users to elicit, structure and document requirements in general. I would also expect a search analyst to be able to understand and document business processes in general. Some other insights on a business analyst’s skills can be found here and here.
• I would also expect that a search analyst should be naturally curious and knows how to ask the right questions. Especially with regard to the exploratory nature of dealing with a lot of analytical data (as seen in my recent posts about search analytics).
• A search analyst must be very capable of analyzing data sets. Specifically, I would expect a search analyst to be very proficient in using spreadsheets to view large data collections – filtering, sorting, formulae, pivot tables, etc. – in order to understand the data they’re looking at. Depending on your search solution, I would also expect a search analyst to be proficient with building SQL queries; ideally they would use reports built in a reporting system (and so not have to directly manipulate data using SQL) but I find that the ad hoc / exploratory nature of looking at data makes that hard.
• I would expect a search analyst to have an understanding of taxonomy in general and, specifically, understands your organization’s taxonomy and its management processes. This is important because the taxonomy needs to be an input into their analysis of search data and also (as highlighted in the potential actions taken from insights from search analytics), many insights can be gained from a search analyst that can influence your taxonomy.
• I would also look for a search analyst to understand information architecture and how it influences navigation on your organization’s web sites. As with the taxonomy, I find that the search analyst will often discover insights that can influence your navigation.
• I would expect a search analyst to have some understanding in basic web technologies. Most especially HTML and the use of meta tags within it. Also, XML is important (perhaps moreso, depending on your search engine). Some understanding of JavaScript (at least in so far as how / if your engine deals with it) can be useful.
• I would expect that a search analyst should be able to quickly learn details of computer systems – specifically, how to manage and administer your search solution. I would not be hung up on whether your search analyst already knows the specific engine you might be using but that can obviously be useful.
• This is not a skill, but another important piece of knowledge your search analyst should have is a good understanding of your major content sources and content types. In general, what kinds of things should be expected to be found in what places? What formats? What kinds of processes are behind their maintenance?
• This is also not a skill per se, but it is important for your search analyst to be connected to content managers and application teams. The connection might be relatively tight (working in a group with them) or loose (association via a community of practitioners in your organization). The reasons for this suggestion include:
  • The ability to easily have two way communication with content managers enables your search analyst to provide continuous education to content managers about the importance of their impact on findability (education about good content tagging, how content will show in search, etc.) and also enables content managers to reach out to a search analyst when they are trying to proactively improve search of their content (something which does not seem to be as likely as I’d like to see within an enterprise setting!).
  • The ability to communicate with development teams can help in similar ways: The search analyst can use that as a way to continually reinforce the need for developers to consider findability when applications are deployed. Also, connectivity with development teams can provide insights to the search analyst so that they can proactively inject themselves in the testing of the applications (or hopefully even in the requirements definition process!) to ensure findability is actually considered.
• Given that last recommendation, it is also important that a search analyst be able to communicate effectively and also be comfortable in teaching others (formally or informally). I find that education of others about findability is a constant need for a search analyst.

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.

Organizational location of search analyst

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:

• Originally, we have this responsibility falling on the heads of our search engine engineers. Despite their best efforts, this was destined to not be effective because their focus was primarily on the engine and they didn’t have enough background in things like the content sources, applications or repositories to include, connectivity to content managers or application developers. They primarily just ensured that the engine was running and would make changes reactively when someone contacted them about an issue.
• We moved this responsibility into our knowledge management group – I was a trigger for this move as I could see that no one else in the organization was going to “step up”.
• Due to subsequent organizational changes, this responsibility then fell into the IT group.
• At this point, I would suggest that the best fit in our organization was within the KM group.
  • A search analyst is not a technical resource (developer or system admin, for example) though the job is very similar to business analysts that your IT group might have on staff.
  • The real issue I have found with having this responsibility fall into the IT organization is that within many organizations, IT is an organization that is responsive to the business and not an organization that drives business processes or decisions. Much of what the search analyst needs to accomplish will result in IT driving its own priorities, which can present challenges – the voice of the search analyst is not listened to within IT because it’s not coming “from the business”.
  • Also, it can be a challenge for an IT group to position a search analyst within it in order to support success. The internal organization of IT groups will vary so widely I can’t make any specific suggestions here, but I do believe that if your search analyst is located within your IT group, a search analyst could be closely aligned to a group focused on either architecture or business intelligence and be successful.
• If your organization is structured to have a specific group with primary responsibility for your web properties (internal or external), that group would also be a potential candidate for positioning this responsibility. If that group primarily focuses externally, you would likely find that a search analyst really plays more of an SEO role than being able to focus on your enterprise search solution.

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.

Background

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:

• Top searches per reporting period
• Top indexes used and the top templates used
• Searches per hour (or day) for the reporting period (primarily useful to know how much hardware your solution needs)
• Breakdowns of searches by “type”: “successful” searches, “not found” searches, “error” searches, “redirected” searches, etc.
• A breakdown of which page of results a user (allegedly) found the desired item

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

• 95% of hits found on results page 1
• 4% of hits found on results page 2
• 1% of hits found on results page 3
• 0% of hits found on results page 4
• Users performed searches up to results page 21

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.

A Solution to Capture Search Results Usage

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:

• URL of the target
• search criteria used for the search
• Location of the result (which page of results, which result number)
• The relevance of the result
• The index that contained the result and whether it was in the ‘best bets’ section
• The date / time of the click

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.

Understanding Search Usage

Given the type of data described above, here are some of the questions and actions you can take as a search analyst:

• You know the most common searches being performed (reported by your search engine) – what are the most common searches for search result clicks?
  • If you do not end up with basically the same list, that would indicate a problem, for sure!
  • Action: Understanding any significant differences, though, would be very useful – perhaps there is key content missing in your search (so users don’t have anything useful to click on).
• For common searches (really, for whatever subset you want to examine but I’m assuming you have a limited amount of time so I would generally recommend focusing on the most common searches), what are the most commonly clicked on results (by URL)?
  • Do these match your expectations? Are there URLs you would expect to see but don’t?
  • Action: As mentioned in the basic analytics article, you can identify items that perhaps are not showing properly in search that should and work on getting them included (or improved if your content is having an identity issue).
• Independent of the search terms used, what are the most commonly accessed URLs from search?
  • For each of the most commonly used URLs, what keywords do users use to find them?
  • Does the most common URL clicked on change over time? Seasonally? As mentioned in the basic analytics article, you can use this insight to more proactively help users through updates to your navigation.
  • Action: Items that are common targets from search might present navigation challenges for your users. Investigate that.
  • Action: Items that are common targets but which have a very broad spectrum of keywords that lead a user to it might indicate a landing page that could be split out into more refined targets. That being said, it is very possible that users prefer the common landing page and following the navigation from there instead of diving deeper into the site directly from search. Some usability testing would be appropriate for this type of change.
• A very important metric – What is the percentage of “fall outs” (my own term – is there a common one)? Meaning, what percentage of searches that are performed do not result in the user selecting any result? For me, this static provides one of the best pieces of insight you can automatically gather on the quality of results.
  • More specifically, measure the percentage fall out for specific searches and monitor that. Focus on the most common searches or searches that show up as common over longer durations of time.
  • Action: Searches that have high fall out would definitely indicate poor-performing searches and you should work to identify the content that should be showing and why it doesn’t. Is the content missing? Does it show poorly?
• What percentage of results come from best bets?
  • Looking at this both as an overall average and also for individual searches or URLs can be useful to track over time.
  • Action: At the high level (overall average) a move down in this percentage over time would indicate that the Best Bets are likely not being maintained.
    • Look for items that are commonly clicked on that are not coming from Best Bets and consider if they should be added!
    • Are the keywords associated with the best bets items kept up to date?
  • Action: Review the best bets and confirm if there are items that should be added. Also, does your search results UI present the best bets in an obvious way?
• What is the percentage of search results usage that comes from each page of results (how many people really click on an item on page 2, page 3, etc.)?
  • Are there search terms or search targets that show up most commonly not on page 1 of the results?
  • Action: If there are searches were the percentage of results clicked is higher on pages after page 1, you should review what is showing up on the first page. It would seem that the desired target is not showing up on the first page (at least at a higher rate than for other searches).
  • Action: If there are URLs where the percentage of times they are clicked on in pages beyond the first page of results is higher than for other URLs, look at those URLs – why are they not showing up higher in the results?
• Depending on the structure of the URLs in use within your content, it might also be possible to do some aggregation across URLs to provide insight on search results usage across larger pieces of your site. For example, if you use paths in your URLs you could do aggregation on this data on patterns of the URLs – How many search results are to an item whose URL looks like “http://site.domain.com/path1/path2”.
  • Assuming you can do this with your data, you can then analyze common keywords used to access a whole area instead of focusing on specific URLs
  • If your site is dynamic (using query strings) it might be possible to do some aggregation based on the patterns in the query strings of the URLs instead to achieve the same results.
  • This type of analysis can actually be very useful to find cases where a user is “getting close” to a desired item but they’re not getting the most desirable target because the most desirable target does not show up well in search. (So a user might make their way to the benefits area but might not be directly accessing the particular PDF describing a particular benefit.)
    • Action: You can then identify items for improvement.
  • All of the above detailed questions about URLs can be asked about aggregations of URLs, so keep that in mind.

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!

• For URLs commonly clicked on from search results, what percentage of their access is through search?
  • Action: If a page has a high percentage of its access via search, this identifies a navigation issue to address.
  • One case I have not yet worked out is a page that is very commonly accessed from search results (high compared to other results) but for which those accesses represent a low percentage of use of that page – do you care? What action (if any) might be driven from this? It seems like from the perspective of search, it’s important but there does not seem to be a navigational issue (users are getting to the target OK for the most part). Any thoughts?
• Turning around the above, for commonly accessed pages (as reported by your web analytics tool), what percentage of their access comes via search? In my experience, it’s likely that the percentage via search would be low if the pages themselves are highly used already, but this is good to validate for those pages.
  • Action: As above, a high percentage of accesses via search would seem to indicate a navigation issue.
• You can also use your web analytics package to get a sense of the “fall outs” mentioned above at a high level of detail – using the path functionality of your web analytics package, what percentage of accesses to your search results page have a “next page” where the user leaves the site? What percentage leads to a page that is known to not be a relevant target (in our data, I see a large percentage of users return to the home page, for example – it is possible the user clicked on a result that is the home page, but it seems unlikely).
  • However, you will likely not have any insight about what the searches were that led to this and not know what the variance is across different searches.

Summing Up

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).

Combining Search Analytics and Web Analytics

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.

• Are the top targets the most used content for that topic?
• Assuming you can manipulate relevancy at an individual target level, you might bump up the relevancy for items that are commonly used but which show below other items in the search results (or you might at least review the titles and tags for the more-commonly-used items and see if they can be improved).
• Are there targets you would expect to see for those top searches that your web analytics tool reports as highly utilized but which don’t even show in the search results for the searches? Perhaps you have a coverage issue and those targets are not even being indexed.
• It might be possible to integrate data from your web analytics solution reflecting usage directly into your search to provide a boost in relevance for items in search that reflects usage.
• [Update 26 Jan 2009] One item I forgot to include here originally is to use your web analytics tool to track the page someone is on when they perform a search (assuming you provide persistently available access to your search tool – say in a persistently available search box on your site). Knowing this can help tune your navigational experience. Pages that commonly lead users to use search would seem like pages that do not provide good access to the information users expect and they fall back to using search. (Of course, it might be that leading the user to search is part of the point of the page so keep that in mind.)
• [Update 26 Jan 2009] Another metric to monitor – measure the ratio of searches performed each reporting period (week) to the number of visits for that same time period.  This will give you a sense of how much the search is used (in relation to navigation).  I find that the absolute number is not as useful as tracking this over time and that monitoring changes in this value can give you indicators of general issues with navigation (if the ratio goes up) or search (if the ratio goes down).  Does anyone know of any benchmarks in this area? I do not but am interested in understand if there’s a generally-accepted range for this that is judged “acceptable”.  In the case of our solution, when I first started tracking this, it was just under .2 and has seen a pretty steady increase over the years to a pretty steady value of about 0.33 now.

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.

• Do they show as good results? If not, ensure that the targets are actually included in your search and review the content, titles and tags. You might need to also tweak synonyms to ensure good coverage.
• You should also review the most highly used content as reported by your web analytics tool against your “best bets” (if you use that). Is the most popularly accessed content show up in best bets?

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.

Categorizing your searches

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.

• Should we review the results from each of those 100 searches and try to make sure they looked good? That seemed like a very time intensive process.
• Should we define a cut off (say the top 20)? Should we define a cutoff in terms of usage (any search that was performed more than N times)?
• What if one of these top searches was repeated? How often should we re-review those?
• How to recognize when a new search has appeared that’s worth paying attention to?

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:

• What are the most-used categories of content for your search users?
  • How does this correlate with usage (as reported in your web analytics solution) for that same category?
  • If they don’t correlate well, you may have a navigational issue to address (perhaps raising the prominence of a category that’s overly visible in navigation or lowering it).
  • Review the freshness of content in those categories and work with content owners to ensure that content is kept up to date. I’ve found it very useful to be able to talk with content owners in terms like “Did you know that searches for your content constitute 20% of all searches?” If nothing else, it helps them understand the value of their content and why they should care about how well it shows up in search results! Motivate them to keep it up to date!
• Assuming you categorize your searches based on your taxonomy, this can also feed back into your taxonomy management process as well! Perhaps you can identify taxonomic terms that should be retired or collapsed or split using insights from predominance of use in search.
• Within the categorization of search terms, can you correlate the words used to identify what are the most common “secondary” words in the searches. An example – GroupWise is a product made and sold by my employer. It is also a common search target. So a lot of searches will include the word groupwise in them (I use that as a way to pseudo-automatically categorizes searches with a category – by the presence of a single keyword). Most of those searches, though, include other words. What are the most common words (other than groupwise) among searches that are assigned to the GroupWise category?
  • This insight can help you tune your navigation – common secondary words represent content that a user should have access to when they are looking at a main page (assuming one exists) for that particular category. If the most common secondary word for GroupWise were documentation, say, providing direct access to product documentation would be appropriate.
  • You can also use that insight to feed back into your taxonomy (specifically, you might be able to find ways to identify new sub-terms in your taxonomy).

Analytics on the search terms / words

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.

• How many words, on average, are in a search? What is the standard deviation? This insight can help you understand how complex the searches your users are performing. I don’t know what a benchmark is, but I find in our search solution, it averages just over 2 words / search. This indicates to me that the average search is very simple, so expectations are high on the search engine’s ability to take those 2 words and provide a good result.
  • You can also monitor this over time and try to understand if it changes much and, if so, analyze what has changed.
• While not directly actionable, another good view of this data is to build a chart of the # of searches performed for each count of words. The chart below shows this for a long period of use on our engine. You can see that searches with more than 10 words are vanishingly small. After the jump from 1 word to 2 words, it’s almost a steady decline, though there are some anomalies in the data where certain longer lengths jump up from the previous count (for example, 25 word searches are more than twice as common as 24 word searches). The absolute numbers of these is very small, though, so I don’t think it indicates much about those particular lengths.

Chart of Searches per Word Count

• You can also look at the absolute length of the search terms (effectively, the number of characters). This is useful to review against your search UI (primarily, the ever-present search box you have on your site, right?). Your search box should be large enough to ensure that a high percentage (90+%) of searches will be visible in the box without scrolling.
  • I did this analysis and found that our search UI did exactly that.
  • I also generated a chart like the one above where the X axis was the length of the search and found some obvious anomalies in our search – you can see them in the chart below.
  • I tried to understand the unexpected spike in searches of length 3 and 4 compared to the more regular curve and found that it was caused by a high level of usage of (corporate-specific) acronyms in our search! This insight led me to realize that we needed to expand our synonyms in search to provide more coverage for those acronyms, which were commonly the acronyms for internal application names.

Chart of Search Length to number of searches

Network Analysis of Search Words

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”.

Closing thoughts

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).