Lee Romero

Archive for the ‘Enterprise Search’ Category

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).

In my first few posts (about a year ago now), I covered what I call the three principles of enterprise search – coverage, identity, and relevance. I have posted on enterprise search topics a few times in the meantime and wanted to return to the topic with some thoughts to share on search analytics and provide some ideas for actionable metrics related to search.

I’m planning 3 posts in this series – this first one will cover some of what I think of as the “basic” metrics, a second post on some more advanced ideas and a third post focusing more on metrics related to the usage of search results (instead of just the searching behavior itself).

Before getting into the details, I also wanted to say that I’ve found a lot of inspiration from the writings and speaking of Louis Rosenfeld and also Avi Rappoport and strongly recommend you look into their writings. A specific webinar to share with you, provided by Louis, is “Site Search Analytics for a Better User Experience“, which Louis presented in a Search CoP webcast last spring. Good stuff!

Now onto some basic metrics I’ve found useful. Most of these are pretty obvious, but I guess it’s good to start at the start.

• Total searches for a given time period – This is the most basic measure – how much is search even used? This can be useful to help you understand if people are using the search more or less over time.
  • In terms of actionable steps, if you pay attention to this metric over time, it can tell you, at a high level, whether users are finding navigation to be useful or not. Increasing search usage can point to the need to improve navigation – so perhaps might indicate the need for a better navigational taxonomy, so look at whether highly-sought content has clear navigation and labeling.
• Total distinct search terms for a given time period – Of all of the searches you are measuring with the first metric, how many are unique combinations of search criteria (note: criteria may include both user-entered keywords and also something like categories or taxonomy values selected from pick lists if your search supports that)? If you take the ratio of total searches to distinct searches, you can determine the average number of times any one search term is used.
  • In terms of taking action on this, there is not much new to this metric compared to total searches, but the value I find is that it seems to be a bit more stable from period to period.
  • Monitoring the ratio over time is interesting (in my experience, ours tends to run about 1.87 searches / distinct search and variations seem small over time). Not sure what a benchmark should be. Anyone? Understanding and comparing to benchmarks probably would provide some more distinct tasks.
• Total distinct words for a given time period and average words per search – take the previous metric and pull apart individual search terms (or user-selected taxonomic values) and get down to the individual words.
  • This view of the data helps you understand the variety of words in use throughout search. Often, I find that understanding the most common individual words is more useful than the top searches.
  • In terms of action, again, not much new here other than comparing to the total searches to find ways to understanding search usage.
  • I’m also interested in whatever benchmarks anyone else knows of in this area – again, I think comparing to benchmarks could be very useful. Just to share from my end, here are what I see (looking at these values week by week over a fairly long period):
    • Average words per search: 2.02. Maximum (of weekly averages) was 2.16 and minimum (of weekly averages) was 1.84. So pretty stable. So, on average, most searches use two words.
    • Average uses of each word (during any given week): 4.95. Maximum (of weekly averages) was 5.69 and minimum (of weekly averages) was 2.93. So a much wider variance than we see in words per search.
• (The most obvious?) Top N searches for a given time period – I typically look at weekly data and, for this metric, I most commonly look at the top 100 searches and focus on about the top 20. Actions to take:
  • Ensure that common searches return decent results. If it does not show good results, what’s causing it to show up as a common search (it would seem that users are unlikely to find what they need)? If it does show what appear to be good results, does this expose specific issues with navigation (as opposed to the general issues observable from the metrics listed above)?
  • If a search shows up that hasn’t been in the top of the list, does that represent something new in your users’ work that they need access to? Perhaps a some type of seasonal (annual or maybe monthly) change?
• Trending of all of the above – More useful than any of the above metrics as single snapshots for a given time period (which is what it seems like many engines will provide out of the box) is the ability to view trends over longer periods. Not just the ability to view the above metrics over longer periods but the ability to see what the metrics were, say, last week and compare those to the week before, and the week before that, etc.
  • I’ve mentioned a few of these, but comparing how the trend is changing of how many searches are performed each week (or month or quarter) is much more useful than just knowing that data point during any given time period.
  • One of the challenges I’ve had with any of the “Top N” type metrics (searches, words, etc.) is the ability to easily compare and contrast the top searches week to week – being able to compare in an easily-comprehended manner what searches have been popular each week (or month) over, say, a few month (quarter) period helps you know if any particular common search is likely a single spike (and likely not worth spending time on improving results for) or an indication of a real trend (and thus very worthwhile to act on). I have ended up doing a good bit of manual work with data to get this insight – anyone know of tools that make it easier?
• Top Searches over time – another type of metric I’ve spent time trying to tweak is to understand what makes a “top search over an extended period of time”. This is similar to understanding and reviewing trends over time but with a twist.
  • Let’s say that you gather weekly reports and you have access to the data week by week over a longer period of time (let’s say a year).
  • The question is – over a longer time period, what are the searches you should pay attention to and actively work to improve? What is a “top search”?
  • A first answer is to simply count the total searches over that year and whichever searches were most commonly used are the ones to pay attention to.
  • What I’ve found is that using that definition can lead to anomalous situations like a search that is very popular for one week (but otherwise perhaps doesn’t appear at all) could appear to be a “top search” simply because it was so popular that one week.
    • To address this, what I do is to impose a minimum threshold on the # of reporting periods (weeks in my case) that a search needs to be a top search in order for it to be considered a top search for the longer time period. The ratio I use is normally 25% – so a term needs to be a top search for 25% of the weeks being considered to be considered at all. Within that subset of popular searches, you can then count the total searches.
    • Alternately, if you can, massage your data to include the total searches (over the longer time period) and total reporting periods in which the search occurs as two distinct columns and you can sort / filter the data as you wish.
    • The important thing is to recognize that if you’re looking to actively work on improving specific searches, you need to focus your (limited, I’m sure!) time on those searches that warrant your time, not find yourself spending time on a search that only appears as a popular search in one reporting period.
  • On the other hand, a search that might not be a top N search any given week could, if you look at usage over time, be stable enough in its use that over the course of a longer period it would be a top search.
    • This is the inverse of the first issue. In this case, the key issue is that you will need access over longer periods of time to all of the search terms for each reporting period – not just the top searches. Depending on your engine, this data may or may not be available.
• Another important dimension you should pay attention to when interpreting behavior is seasonality. You should compare your data to the same period a year ago (or quarter ago or maybe month ago, depending on your situation) to see if there are terms that are popular only at particular times.
  • An example on our intranet is that each year you can see the week before and of the “Take your Kids to Work” program, searches on ‘kids to work’ goes through the roof and then disappears again for another year. Also, at the end of each year, you see searches on “holidays” go way up (users looking for information on what dates are company holidays and also about holiday policy).
  • This insight can help you anticipate information needs that are cyclical, which could mean ensuring that new content for the new cycle (say we had a new site for the Kids to Work program each year, though I’m not sure if we do) shows well for searches that users will use to find it.
  • It also helps you understand what might be useful temporary navigation to provide to users for this type of situation. Having a link from your intranet home page to your holiday policies might not be useful all of the time but if you know that people are looking for that in late November and December, placing a link to the policies for that period can help your users find the information they need.
• Another area of metrics you need to be attention to are not found searches and error searches.
  • What percentage of searches result in not found searches for your reporting periods? How is that changing? If it’s going up, you seem to have a problem. If it’s stable, is it higher than it should be?
  • What are the searches that users are most commonly doing that are resulting in no results being found? Focus on those and work to ensure whether it’s a content issue (not having the right content) or perhaps a tagging issue (the users are not using expected words to find the content).
  • The action you take will depend on the percentage of not found results and also on the value of losing users on those not found.
    • On an e-commerce site, each potential customer you lose because they couldn’t find what they were looking for represents hard dollars lost.
    • On an intranet, it is harder to directly tie a cost to the not found search but if your percentage is high, you need to address it (improving coverage or tagging or whatever is necessary).
    • A relatively low “not found” percentage might not indicate a good situation – it might also simply reflect very large corpus of content being included in which just about any words a user might use will get some kind of result even if it’s not a useful result. More about that in my next post.
      • I’m not sure what a benchmark is for high or low percentage of not found, exactly. Does anyone know of any resource that might provide that?
      • On our intranet search, this metric has been very stable at around 7-8% over a fairly extended time period. That is not high enough to warrant general concern, though I do look for whether there are any common searches in this and there actually does not seem to be – individual “not found” results are almost always related to obvious misspellings and our engine provides spelling correction suggestions so it’s likely that when a user gets this, they click on the (automatically provided) link to see results with the corrected spelling and they (likely) no longer get the “no results” result.
    • Customizing your search results page for not found searches can be useful and provide alternate searches (based on the user’s search criteria) is very useful though it might be a very challenging effort.
  • What types of things might trigger an “error search” will depend on your engine (some engines may be very good at handling errors and controlling resources so as to effectively never return an error unless the engine is totally offline (in which case, it’s not too likely you’ll capture metrics on searches). Also, whether these are reported on in a way that you can act on will depend on your engine. If so, I think of these as very similar to “not found” searches. You should understand their percentage (and whether it’s going up, down or is stable), what are the keywords that trigger errors, etc. Modify your engine configuration, content or results display as possible to deal with this.
    • An example: With the engine we use, the engine tries to ensure that single searches do not cause performance issues so if a search would return too many results (what is considered “too many” is configurable but it is ultimately limited), it triggers an “error” result being returned to the user. I was able to find the searches that trigger this response and ensure that (hand-picked) items show up in the search results page for any common search that triggers an error.

That’s all of the topics I have for “basic metrics”. Next up, some ideas (along with actions to take from them) on more complex search metrics. Hopefully, you find my recommendations for specific actions you can take on each metric useful (as they do tend to make the posts longer, I realize!).

In an exchange in comments on Stephen Arnold’s blog, Stephen states the line that is the title of this post:

“the future is search enabled applications, not enterprise search”

I’m somewhat familiar with Stephen (I’ve seen him speak at a couple of conferences and also have followed his writing on his blog for some time), but I had actually not seen this declaration in the past (though Stephen says he’s accused of saying it too much).

In any event – I find this an interesting claim and I think I would agree with the sentiment but I also think that it depends on how you look at it.  As I wrote previously in trying to lay out what I thought enterprise search is, I think that the key aspects of an enterprise search are that it’s available to all members of the enterprise and that it covers all relevant content.

Down in the details, if access to the enterprise search is through embedding that it in numerous locations or one location, I do not believe it matters.  In fact, as I wrote previously, embedding access through multiple points is probably ideal – let workers access it within the environment in which they work, regardless of what tool(s) they normally use to do their job.

On the other hand, if the expectation is that you can embed search in single applications and expect that search only within that application is sufficient, I do not think that is now or will in the future be sufficient.  The information needs for any organization are diverse enough that no one application can realistically handle all of them – email, document management, CRM, support knowledge bases, intranets, policies, etc.

Thoughts?

In my last post, I described the goals I have tried to achieve with my proof of concept people search function. Here I will describe the design and implementation of this proof of concept.

Designing the Solution

Given the goals above, here’s the general outline of the design for this solution:

• It would be built as a web application that generates a “profile page” for each worker – it is the set of all such profile pages that comprise the targets for a search engine to index.
• Combined with a search engine (probably any search engine capable of indexing web pages would be sufficient – I used QuickFinder), it becomes trivial to integrate the search of these profiles into your enterprise search to provide a fourth generation solution to people search.
• The core tenet of the data used is that I wanted to identify a set of activities for workers. The aggregation of keywords related to those activity is used to generate a profile for a worker.
• An activity could potentially be anything that represents an event, action, writing, task, assignment, etc., that is associated with the worker.
• Some examples of activities might include: edit of a wiki article, assignment of a task in an online workspace, posting of a message in a discussion form, membership in a project team, publishing a document in a corporate repository, posting an email to a mailing list, and so on.

Initially the web application directly queried the various systems used as sources when generating a profile for a worker. That is not scalable and also limits the amount of processing you can do, so I designed a simple SQL database to contain the data for this (implemented in MySQL). This database is essentially a data mart of worker data. The primary tables are:

• worker (one row for each worker); this table contains the basic administrative data for a worker (it’s effectively a mirror of the organization’s corporate directory)
• activity_source (each row describes a single source of activity which a worker might produce)
• activity (one row for each individual “activity” associated with a worker); an activity must have a “description” – typically the title of an item or the subject of an email, etc.
• From these tables, a few additional tables are generated by processing the data from the activity table
  • activity_keyword (contains a row for each keyword associated with an activity); a keyword is either any (individual) word from the description of the activity or a piece of metadata associated with the item (for systems which support such);
  • worker_top_keyword (aggregates the individual keywords associated with a worker [by association from activity_keyword through activity to the worker table]) so it’s easy to identify the top keywords for a worker without doing aggregation queries; each keyword in this table is weighted (see the description below of weights); I think of the set of keywords in this table for a worker to be that worker’s “attributes”
  • worker_connection (aggregates “linkage” between workers based on similarity of their keyword profiles); more on this later.

With the implementation of this database, I also implemented a synchronization tool that updates the data in the tables from the source systems for the various types of activities.

By automatically pulling data from these source systems (which workers use in their regular day-to-day work), you remove the need for the workers to maintain data.

• By simply doing their job and “leaving traces” of that worker, they generate the data necessary for generating this profile. This achieves goal #2.
• By restricting the set of data sources used to ones which anyone could examine for a worker’s activities (for example, I can view the history of a Wiki article and see who has edited it), I achieve goal #3.

Now, how should the profile page for a worker be presented?

Initially, I put together a design that did two things: 1) provided a typical employee directory style layout of my administrative details and 2) provided a list of all of the activities for a worker, grouped by activity source. In other words, you would see a list of all of the Wiki articles edited by the worker, a list of mailing list memberships, a list of community memberships, project team memberships, task assignments, etc. Each activity source’s list would be separately displayed (in a simple bulleted list). (Before this would go into production, I always have assumed I would ask for some design help from our electronic marketing group to give it a more professional look, but I thought the bulleted list worked perfectly well functionally.)

This proved simple and effective and also enabled the profile page to provide direct links to those activities that are addressable via a link (for example, the profile page could link directly to a Wiki article I’ve edited from my profile page, it could link to each discussion post, etc.)

However, this approach suffered from at least two problems: 1) it lacked an immediately obvious visual presentation of a worker’s attributes, and 2) it exposed every detailed activity of a worker to anyone who viewed the profile (I found when I demoed this to people, some had the immediate reaction of, “Wow – anyone can see all of these details? I’m not sure I like that!” – a reaction that surprised me given that any of the details are generally visible to anyone who wants to look, but go figure).

After looking for alternatives, I found that the keywords for a worker (when combined with their weights) provided good input for a tag cloud – which is what I ended up using as the default presentation of a worker’s keywords (visible to everyone). This helps to highlight what someone is “about”, presents a generally attractive visualization of the data, and, if the default view of a worker displays this tag cloud (and the worker’s administrative data) and does not show all of the details, it alleviates the concern mentioned above.

I have found the implementation of the tag cloud to be the trigger that pulls people into this tool – it helps satisfy my goal #5 because, for most people who have looked at this, it provides immediate validation when they see words they expect to see in their own tag cloud.

Here’s a shot of what part of my profile page looks like (partially obscured):

Lee Romero Profile

Additional Design Considerations

I wanted to keep the initial proof of concept simple in order to try to test different ways of using the data from the activity sources. With that in mind, here are some details on how I’ve done this so far:

• When parsing the text associated with an activity into “keywords”, I took the simplest approach I could: the words from an activity are split into separate words when any non-alphanumeric is found. So a string like “content-management infrastructure” would result in 3 keywords: content, management and infrastructure.
• I also removed any words that are stop words in our search engine.
• Each keyword for a worker is assigned a weight. Simplistically, the weight of a keyword is the number of times that keyword shows up in that worker’s stream of activities.
• However, the tool that maintains the keywords allows an administrator to assign a weight to each activity source – so some sources can be given an artificial boost just by assigning a weight for that activity source higher than 1. The only source whose weight I’ve really toyed with so far is the corporate directory itself – I have given that a weight of 20 instead of 1.
• The weights for keywords are used in two ways:
  • The top 50 keywords (by weight) for a worker are used in the tag cloud for that worker. The weight is then used to size the words in the tag cloud.
  • When the “keywords” <meta> tag is being computed for a worker’s profile, the keywords are sorted by weight and the keywords are included until the length of the keywords content attribute is greater than 250 characters. This means that the top keywords are the ones which will give the worker higher relevance for searches on those words.
• Because all workers will have, at absolute minimum, the same details in this profile as they would in the corporate directory, and because the keywords from that activity source are given extra weight, those keywords will almost certainly be in the “keywords” <meta> tag for their profile – this helps satisfy my goal #6 by ensuring good relevance when people search on worker’s administrative data (first name, last name, etc.)

Some additional functions I have layered on top of the basic profile / search mechanism that I believe will make this a valuable solution:

• The keywords in the tag cloud are links to pages that provide details about that keyword. When a user clicks on a keyword in a tag cloud, they are presented with a tag cloud of keywords related to their starting keyword (related by way of people who have the keywords in common). In other words, it provides a set of keywords that have a lot in common with their starting keyword. The “keyword profile” page also provides a list of workers who use the selected keyword (the list is sorted by keyword weight).
• When you view a worker, you are also presented with a list of workers who are “similar to” the worker you are looking at – the similarity measure is the percent of overlap of the current worker’s profile (weighted keywords) maps to the other workers. This provides a way to explore a neighborhood of similar people.
• In addition to the list of similar worker, a link is provided for each worker which, when clicked, displays a page explaining why the two workers are similar.
• Almost all of the data sources have a date threshold applied to the data pulled from the source – most of them take data from the last year. This ensures that the data used to build a profile is effectively self-maintaining.
• Each worker has control over whether others can see all of the details (the individual activities) in their profile. By default, only the tag cloud and administrative data is visible. A worker can opt in to allow others to see their entire profile.

Issues / Future Directions

The proof of concept has been very interesting to work through and has presented me with some (subjective) proof of the value of this approach, as simple as it is. That being said, there are some issues and additional areas I hope are explored in the future:

• This is a proof of concept built as basically a skunkworks project – I am hoping it will officially get some sponsorship and be launched into production.
• I would like to see it integrated with additional data sources – currently, it uses 12 data sources but some high value sources that are not included would be our CRM system and our HR system. With the sources currently in use, it tends to skew the people whose profiles look sufficiently detailed to be ones who use the sources. Integrating these is relatively easy – a single SQL query from the source system that provides a list of activities for workers (where the source system can define whatever it wants to represent activities) is all that’s needed. It is this ease of adding in sources that achieves my goal #4.
• I believe there is still a lot of work to do around tweaking the weights of activity sources to balance out the effects of various sources.
• I would like to see some exploration of workers directly tagging other workers (to add keywords) or possibly allowing workers to give a thumbs up / thumbs down to individual keywords in a profile for a worker. This would add a powerful way for people to influence their own and others’ profiles.
• This approach also needs to receive more testing from others to validate its effectiveness. I have had a few dozen people look at it and provide feedback but some more quantitative approach to this would be valuable.
• I think this profile for a worker could be presented in a FOAF format as well – I’m not sure if that provides additional value, but it is a path to explore.
• The algorithm for parsing out keywords from the activities could be improved beyond the very simplistic parsing applied now.
• And, finally, I think that the measurement of similarity between workers could be significantly improved and the data from the links between workers embedded in this could be used to do some research to find “invisible communities” within the company. This would be a kind of organizational network analysis through data mining, which

I have previously described what I termed the various generations of solutions to the common challenge of workers finding connecting with or finding co-workers within an enterprise.  My most recent post described the fourth generation solution – which enables users to search and connect using much more than simple administrative terms (name, email, address, etc.) for the search.

Over my next couple of posts, I will provide a write-up of a proof of concept implementation I’ve assembled that meets a lot of the need for this with what I believe to be relatively minimal investment.

The follow represent the goals I’ve set for myself in this proof of concept:

1. Demonstrate the usefulness of a people search based on attributes of workers other than purely administrative data – things like their skills, competencies, work, interests, etc.
2. Demonstrate the feasibility of discerning the skills, competencies, work and/or interests through a means that does not depend on maintenance of data by the worker (which, from my experience, is not long-term maintainable).
   1. More specifically, provide a test bed to explore different algorithms for discovering keywords important keywords for people.
3. Demonstrate the feasibility of discovering keywords using only data that is generally “publicly visible” within an enterprise.
4. Provide a path for integrating manually-maintained skills data (if that were to be collected), or any other data (possibly including tags applied by co-workers as seen in IBM’s Dog Ear project).
5. Provide a compelling user experience that draws people in and gives people a visual presentation of what another person is “about” (what describes them).
6. Provide a solution that provides, at minimum, the equivalent of a 3rd generation solution (in other words you can find that worker based on their administrative data).

Also, I wanted to say that part of the inspiration for this proof of concept came from a session I attended at Enterprise Search Summit 2007 as presented by Trent Parkhill.  In his session, he described a mechanism where submissions to a company’s repository would be tagged with the names of participants in the project that produced the document as a deliverable.  Then, when users were searching for content, there was a secondary search that produced a list of people associated with the terms and / or documents found by the user’s search.  I’ve kind of turned that around and treated the people as being tagged by the keywords of the items they produce.

In my next post, I will describe the overall design of my proof of concept.