6Sense developed out of a 2008 acquisition of search technology company Trovix.  According to a Monster press release, “6Sense patented semantic search technology utilizes intuitive, concept based searching, with a human-like understanding of the recruiting process and hiring needs.”^[1]

In case you’re not familiar, semantic search utilizes a concept called “disambiguation” to determine the probable meaning of sentences by building conceptual relationships between words, syllables, and related text.  However, the system cannot function in absence of what’s called the “conceptual basis” or primary logic required to begin building the conceptual relationships which govern the system.

But who or what determines the logic of the conceptual basis for a given semantic search system?

Intuitively, most people respond with an answer they find very simple, “The task at hand determines the logic of the conceptual basis”.  However, this simple and intuitive leap of the designer (the human mind) is an exceptionally difficult leap for the designed (computational system).

Unlike its designer, the semantic search system does not have the luxury of thinking outside of its own box.

Because the system relies on the wisdom of its designers to set the most appropriate logic, would it make sense to open up this process to a larger audience?

Given the presumably enormous amount of data and users, how many possible sub-contexts exist within the contextual premise of the initial system?  What is the probability that a conceptual basis created by a few experts will account for all of these possible combinations?  And most importantly, would differentiation at this level of granularity deliver measurable improvements to the user experience?

Well, I assume some level of diminishing marginal returns would arise; however, I’m confident this level of contextual granularity has probably not been reached in the case of 6Sense.

At the very least, I think this ad clearly communicates that successful companies are coming to realize the importance of their web channel, and therefore, the importance of relevant search and navigation.

And when it comes to relevance, we’re all beginning to realize that context is king.

I think Monster said it best. “The intelligence of the 6Sense technology contextually interprets the meaning behind words and concepts rather than relying on the narrow, literal meaning of keywords.”^[2]

I couldn’t agree more.

http://www.businesswire.com/portal/site/home/permalink/?ndmViewId=news_view&newsId=20100201006672&newsLang=en

Over the past few weeks, we’ve explored how there are three key principles that explain why site search just doesn’t perform like we expect it to and what we can consider to help mitigate this.  So far, we’ve learned that:

1. The critical information we need to make search great isn’t in the document – it’s in the users’ heads.
2. Asking users to explicitly provie us information that would improve search, while a seemingly good approach, is inherently flawed.

Today, we explore the third principle that shows that if we want to improve search, we need to focus on all of the things users are doing online.  What I mean here is that we need to look beyond search and at the entire site experience to truly understand what’s valuable, why it’s valuable, and in what context it’s found to be valuable.

Reason #3:  Search does not exist in a vacuum.

In order to improve search, we need to observe more than just search behavior.  Search and navigation have traditionally been seen as two separate paradigms: separate interfaces and separate systems driving them.  But in reality what’s happening?  A user is coming to your site and expressing an interest or intent through their actions.  They might have first expressed that intent through a Yahoo! or Google search that brought them to your site.  They might then express it in the pages they visit and engage with, the navigation they use, the links they click, and maybe the site searches they perform.  This expression of interest may span multiple searches and clicks.  And, finding documents that hold true value for that interest and intent may also take multiple steps.

Let’s think back to the “Insight/Incite” example once more.  Had we only looked at what search results users clicked on, the problem might never have been solved.  Why?  Because the valuable content was never in the results – it wasn’t there to be clicked on in the first place!  To learn what users really meant by “insight”, we had to watch their subsequent navigation, paying particular attention to the patterns of behavior that indicated engagement or that they had discovered content that was of value – even if it happened several steps after the initial search.  Observing search behavior alone is not enough!

What about users who don’t search at all?  What can we learn from them?  Users are actually giving us continual clues to their intent and interest with every link they click and every category they choose.  The documents that users engage with and the order in which they engage also tell us not only about relationships between documents, but intent.  If we take this valuable, implicit insight into account, then we really begin to see how this insight could be used to fix search.

What’s really remarkable is that once we take a step back and think of the entire online experience as a single unified expression of intent and value, we can do a lot more than fix search.  We can start to make recommendations and optimize the user experience with every interaction they make with your site; from the moment they arrive, every step they take through the site, as well as every search they perform. The true goal is to understand the user’s intent and then automatically surface documents that other like-minded peers have found valuable in that same context.  That’s the true wisdom of the crowd, and what Baynote’s Collective Intelligence Platform (CIP) is all about.

There are three key principles that explain why site search just doesn’t perform like we expect it to. Over the next few weeks, I’ll dive deeper into each issue surrounding traditional search and offer my insights and experiences to help you understand why your search sucks, and how you can improve it.

Reason #2:  Actions speak louder than words.

Okay, so we’ve figured out that the critical information is not in the document.  Where is it?  Well, it’s in the users’ heads of course.  Let’s look at an example.  If we look at a work by Shakespeare—or any great work of literature—the meaning cannot be identified simply by looking at the words within it.  It’s synthesized in the reader’s mind, and different readers may derive different meanings based on their own unique makeup and experiences.  The same applies to any document.  We must look beyond the words within the document to truly understand the value.  The key question to ask is this: when and why is this document valuable to users?  Only the users themselves know the answer.

Now that’s all well and good, but how do we extract that knowledge from the users?  We could ask them directly, but while that might seem like a good strategy, it’s actually not.  Asking users to explicitly rank, rate, or tag documents is doomed to failure.  The core problem is one of participation.  Think back to how many times you’ve provided feedback on the web.  Most of us never have; others may have on occasion, but almost certainly not on every page visit or search result.  This participation problem leads to a few key challenges:

A. Low coverage. A small subset of the population rates content, and when they do, the ratings only tend to  cover the most popular content.  Where does that leave the majority of our content?—the long tail.  Unranked and therefore undiscovered.  And with search, it’s not just about knowing that a document has value; it’s about whether it’s valuable for that specific search topic and that user.

Let’s take a deeper look at this.  Let’s say we have a bunch of ratings on a particular camera.  Sounds great, right?  Well, not really.  People like or dislike a camera for a variety of reasons.  Someone looking for a “lightweight camera” might think it stinks, while someone looking for a “cheap camera” might love it.  You can’t ignore the context of what a person is looking for, and getting explicit coverage across every topic of interest is even harder than just getting an overall rating.

And, when you factor in staleness of content — be it the article or the rating — then even the ratings you do have become less meaningful.  Add to that the mountains of content that are being created every day, and the problem really gets out of hand.  There’s just no way to keep up with it if we’re relying on people to go out of their way to explicitly rank, rate, and tag content.

B. Biased. In general, the people who do participate in explicitly rating something online represent a very small subset of the population.  That means that, even for those documents and topics that do have coverage, there’s no guarantee that the knowledge imparted by users even represents the majority opinion.  In fact, it almost always represents fringe opinions that are either extremely positive or negative because those are the people motivated to be heard.

To read more about the bias inherent in explicit methods of capturing community wisdom, check out my “7 Deadly Biases” whitepaper.

C. Inaccurate/Incomplete. Even when an individual decides to provide some form of explicit feedback, it often is not fully representative of even their own experience.  Let’s go back to the insight/incite example from my previous post.  Let’s say that a user decided to tag the Incite product page that was so useful.  What tag do you think they would use?  Probably “incite” or “incite phone,” right?  They would almost certainly not tag it with “insight” once they realized their mistake; but this is actually the tag that would be of most value to the community!  It might surprise you to know that social scientists are generally distrustful of people’s own accounts of their feelings and behavior!  Meta-cognition and emotional self-awareness are far more complex, and less intuitive, than people think.

So, if asking people to tell us what documents are valuable and why they’re of value doesn’t work, then what is the right way?  The key is to observe what people do, not what they say.  It’s both more accurate and more comprehensive.  The wisdom we are looking to tap is present in every single search–whether the searchers were successful or not.  By watching what people do, we can understand which documents are valuable and when.

But we need to be careful here too, because watching which search results users click on is not enough.  Clicks are a very weak indicator: just because people clicked on a result does not mean it’s valuable.  Perhaps the title was intriguing, confusing, or even misleading.  It is critical to follow the user all the way from query to success or ultimate failure—even if several steps later—and not get distracted by what they click on.  At Baynote, we track 24 different behavioral heuristics to ensure that we accurately capture where users are engaging given a particular context and intent.  And it’s not just about search; it’s about the entire online experience.  More on this in the next post.

Next week: Part 4 in the 4-part series, My Search Sucks! where we’ll explore how search does not exist in a vacuum.

My Search Sucks! Part 2

Part 2 in the 4-part series, “My Search Sucks,” discussing why search, well, sucks.

There are three key principles that explain why site search just doesn’t perform like we expect it to. Over the next few weeks, I’ll dive deeper into each issue surrounding traditional search and offer my insights and experiences to help you understand why your search sucks, and how you can improve it.

Reason #1:  The critical information is not in the document.

All full-text search technologies basically work the same way: they look for a match between the words in a user’s query and the words in the text of the documents searched.  That said, there are lots of fancy layers that can be added from simple stemming to complex natural language processing (NLP), but the fundamental assumption is that the engine can figure out which documents best meet a user’s needs by looking inside the document.

While this is a start, it’s just not enough. The critical information isn’t in the document; it’s in someone’s head.  But whose head is it in? Let’s look at some examples.

A favorite example comes to mind involving one of our customers, a large online appliance retailer.  Users were coming to their website looking for a “stove” over and over again, and the search results had “stove-top safe” kettles and pots, but no stoves.  Turns out the reason for this was that this retailer’s website was using the manufacturer terminology, “cooktops” and “ranges.”  The word “stove” was nowhere to be found.  The community was using a different vocabulary than the site.

Sounds like a simple fix, right?  All you need to do is to create a synonym to tell the search engine that a “stove” is the same thing as a “range.”  And sure, once you’ve found and addressed the discrepancy, customers searching for “stoves” will find the “ranges” they’re really looking for.  But what about all of those long-tail terms and content—and what about when things change?

Sam Mefford, an expert in the deployment of enterprise search technologies, commented on last week’s blog.  In his search practice, he sees this challenge surface on a regular basis and provided an example from one of his clients.  The company re-branded one of its products, and made the appropriate changes in its marketing materials and documentation.   Afterwards, field agents and customers could no longer find the products and information they needed, because they continued to search using the old name.  This problem took months to discover.

Another great example is from a customer that’s a well-known wireless provider.  They launched a new LG phone called the “Incite.”  Suddenly, one of the most popular search queries on their site became “insight.”  The search results included lots of business-type documents about how to achieve great “insight” into your business operations, but nothing that matched what users wanted – information on this exciting new phone. Sure, searching for “insight” while the product is called “incite” was technically the user’s mistake, but does that matter when you’re losing opportunities?

Let’s say the words do exist in the document.  It’s often not enough.  There may be 1000s of documents that contain the search terms, but which documents are the best?  A traditional search engine will assume that the one with the most occurrences of the keywords is the most valuable, but this is very often not the case.  Obviously, the technology is more sophisticated than this, but the fundamental basis is along these lines.  The most useful document may only have one instance of the keyword and therefore may be buried on page 10 of the results.  So, how do you get the most useful document to the top of the search results?

Manual tuning is the traditional “solution” to all of these site search issues, but as we discussed earlier, it’s nearly impossible to catch all discrepancies and adapt rapidly—not to mention the time and effort involved.  I’ve even mentioned the spirit of the solution: it’s fundamentally a recognition that the needed information is not in the document, it’s in someone’s head.  But whose head is it really in?

Many companies have experts that manually tune and tweak search.  But that’s a labor-intensive way to temporarily solve the problem and certainly doesn’t guarantee that the expert’s view on what’s right matches with users’.  Why take that chance?  Better to go straight to the source of the information: the user!

Next week: Part 3 in the 4-part series, My Search Sucks! where we’ll explore how actions speak louder than words.

1. The critical information is not in the document

While documents — whether webpages, pdfs, or Word docs — seem like the best place to discover a match to a user’s search term, they’re not. Processing documents is a good start, but the words within a document do not necessarily match the way a user understands the topic and phrases their question. And even if the search term is in there, it doesn’t mean that particular document is useful. The critical information is in the heads of users, not the documents. The key is to understand how, when, and why people use each document. At Baynote, we call this UseRank.

2. Actions speak louder than words

To get information from users you might think the best approach is to ask them. Seems simple and straightforward, right? Wrong. Turns out that there are a number of problems with explicit means of collecting information stemming from who participates, when, and why. As social science has taught us all along, if you really want to understand people, watch what they do, not what they say.

3. Search does not exist in a vacuum

Any time someone comes to your website, they are looking for something and they give you clues to what that is through both their search and navigation behaviors — and not just what they ask for and where they go, but what they do when they get there. Often they got to your site through an external link such as a search on the web — that’s your first clue. Although the goal might be to solve the site search problem, observing search behavior alone is not enough.

I’ll expand on each of these in more detail in upcoming posts.

Our Social Search solution works by observing the successful search and navigation paths of users in order to guide successive site visitors to their goals. Relying entirely on keyword search solutions is insufficient. An effective enterprise search solution will be able to detect the different search contexts that exist on an intranet. While this number is large, it is certainly finite. By employing a solution that can learn these contexts and detect them when clues are present, findability on the intranet can be greatly increased.

Check out our white paper on social search on exactly how Baynote can replace or supercharge an existing search solution with inadequate results.

The blog post correctly identifies two types of Social Search:
1. Explicit-based – Solutions where search results are powered by “simple [influencers like] shared bookmarks or tagging of content with descriptive labels.”
2. Implicit-based – A solution that is powered by the uncovering social intelligence with complex computer algorithms.

Baynote Social Search is closely aligned to implicit-based Social Search description given, as we do not utilize explicit information like bookmarking or tagging to derive our search results. And according to the post this turns out to be the differentiator that immunizes us from the pitfalls of social search mentioned:

Social Search Cons

Despite the obvious benefits social search has some glaring defects too. As said earlier social search hinges heavily on human judgment. But the web today is growing at a pace which humans simply cannot match and this means there will be a lot of content that would remain unnoticed and hidden from the user. Also the tagging method through which the search works is not the ideal way of organizing web data. There is also the risk of spam because users have the freedom to directly add results to a social search engine which can be misused. We all know the way some SEO’s behave!

However, Baynote Social Search does offer the same benefits that are mentioned by the author:

Social Search Pros

Social search engines have potential benefits deriving from the human input qualities of social search. Some of these include:

• Relatively free from link spam as there is less reliance on link structure of webpages
• More relevant search results as each result has been selected by users
• The user gets more current results
• The user gets his precise perspective reflected in the results
• The human judgment that social search uses is more accurate than computer’s ability to analyze a webpage

It was great to see Baynote standing out as the only company mentioned that is both bringing social search to websites rather than just a general web search engine and avoiding the negative pitfalls of explicit-based social search.

For any additional information on the Baynote Social Search Methodologies, check out our whitepaper “In Search for the Human Element.”

One of the interesting things that happened at Juniper after overlaying Baynote on top of their existing search engine  (you all know this right, no rip and replace, we’re SAAS, we’re very nice that way) is that the mix of search results shifted significantly in two main ways:

First of all, the Mob of Juniper site users found documents relevant to queries that the search engine couldn’t.  Humans just get things, make connections that machines can’t.  Period.

Second, the items that percolated to the top were more often the more technical documents that the majority of the site viewers were interested in, not necessarily all the various product pages that marketing felt they needed to create.  The Mob, of course, knew what it needed and put these items on top.  Simple.  Voilà.

So sometimes what the Mob, via Baynote, delivers is more results (i.e. stuff an algorithm can’t find like pdfs) or sometimes fewer results (just the stuff people want) either way, the Mob delivers better results.

As we worked with Suresh Vittal of Forrester Research to put together the content, Jack, Suresh and I frequently hearkened back to a similar “sea change” that happened in the early days of content management.  In 1998, we spent much of our time talking to journalist, analysts and customers about unlocking the so-called webmaster bottleneck.  If you could only put web publishing in the hands of the internal content experts, then you could eliminate this delay created by having a “webmaster.”  By 1999 when we went public, everyone got it.

But unlocking this bottleneck created a new bottleneck – people finding the information they needed.  Now we suffered from content overload — because organizations kept producing more and more content and there was no efficient means to make sure that the content you showed was the content people needed or the products people wanted.  Organizations tried to fix it by investing in complex search engines or analytics or profile-based personalization.  None of this fixed the fundamental problem.

It’s time to get rid of the expert bottleneck.  In this economy, the timing is perfect.  There are some task that will always be manual but there are others that can and should be automated.   Automate for a better user experience.  Automate for efficiency. Turn what people see on your site over to the Mob.  It might make you feel a little uncomfortble now, but so was the idea of eliminating the webmaster bottleneck in 1999.  I say FUGETABOUTIT.

Yesterday, one of the conference attendees that visited our booth created a blog post explaining her take on targeting technologies, but on a personal level.

My favorite part of Baynote’s technology: it ignores demographics. Yes! When are people going to figure out that this is an individualistic age? Advertisers, you are wasting your dollars on serving me with endless weight-loss and dating ads. Not all 27-year old females are the same!

I’d have to agree with this point, not all 27 year old females are the same. Not simply because I’ve seen our technology benchmarked against demographic or profile based targeting technologies(which I have), but also because my 27-year old wife would have me sleeping on the couch if I said otherwise.

If you are a new reader, or would like more background on our contextual targeting approach, check out our whitepaper “In Search of The Human Element.”