After attending the event for four years, I’ve had the opportunity to see first-hand how the market has shifted over time. The discussions at eTail West in the earlier years were primarily about how to manage, measure, and increase transactions. But most retailers have this figured out by now, so their focus has shifted to maximizing their revenues in new ways.

Naturally as the market has matured, retailers have grown to be more concerned with customer experience and service, so it’s no wonder that the three consistent themes at this year’s event were multi-channel customer experience, personalization, and recommendations. Online retailers are looking for new ways to increase their revenues and to achieve better personalization and customer targeting. They are also looking for new ways to maximize revenues by deploying the recommendations they already have in place in a much broader sense. Now that retailers have reaped the benefits of traditional product recommendations, they understand the capabilities these technologies have to tackle the newer issues, such as navigating social elements of their sites and the ongoing quest to improve search.

Just as the market has matured, so has Baynote. Each year at eTail, we’ve seen interest in our approach grow. Listening to the conversations of the attendees and the other speakers this past week, it was clear to me that Baynote’s time has come as retailers hone in on providing a superior customer experience and ultimately how to create happier, more loyal customers.

Perhaps the best evidence of this was a survey I conducted during my presentation. I asked the crowd: “Raise your hand if you think your best salesperson can outsell your best customer?” The result? Not a single person in the room raised their hands. When I asked another question: if the happiest customers are the most important to increasing their revenues; almost every single person in the room agreed. This demonstrates just how powerful peer-to-peer brand advocacy has become to retailers and the bottom line.

I left Palm Desert thinking about what an exciting time it is to be involved in the e-commerce industry, and how proud I am that Baynote’s customers are setting the standard with more sophisticated personalization approaches that are transforming the customer experience as well as their online businesses.

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.

According to Gartner, a pattern-based strategy “provides a framework to proactively seek, model and adapt to leading indicators, often-termed ‘weak’ signals that form patterns in the marketplace.”  For the past several years Baynote has been committed to helping companies identify these patterns with technology that lets them tap into the collective intelligence of customers visiting their websites. This is something that transactional based systems such as business intelligence (BI) and complex event processing (CEP) simply haven’t been able to deliver. Here’s why:

1) For years BI, CEP (more recently) and other related technologies have helped organizations become much more efficient by automating their interactions with customers. However, in the process of creating huge economies of scale, they forced companies to lose the “mom and pop” touch that consumers expect when they walk into a local hardware store or restaurant. In failing to create digital mom and pop experiences, online retailers and publishers have placed unnecessary emphasis on promoting popular products and content, thereby losing out on profits to be gained from merchandising their long tail products.

2) In addition, these so-called “predictive” applications have historically prioritized the wrong set of indicators, often identifying consumer trends weeks, if not months, after the fact. For example, e-commerce transactions lag other more relevant indicators, such as online comparison shopping, by months. Only by tapping into the power of the collective is it possible to see early signals, spot trends and develop strategies around them before your competitors catch on. This holds particularly true for long tail products. Our customer US-Appliance tapped into the implicit behaviors of its website visitors to merchandise colored washers/dryers months before Home Depot and Best Buy began promoting similar products in their stores.

In Gartner’s recent report, entitled “Introducing Pattern-Based Strategy”, they view “the collective” as being critical to developing a pattern-based strategy. We couldn’t agree more with their position:

The collective comprises individuals, groups, communities, mobs, markets and firms that shape the direction of society and business. The collective is not new but technology has made the collective more powerful — and enabled change to happen more rapidly. The explosion of social software has enabled groups and individuals to rapidly form and rally to a cause — often resulting in significant societal changes.

The result for business is a cacophony of rapidly evolving demands, expectations, inputs and transactions, as well as an opportunity to not only react, but to seek signals of change from the collective. Market trends, some subtle, others strong, are masked by noise, and many enterprises are failing to proactively detect the patterns they rely on to direct future strategy and support investment decisions. In addition to failing to detect these patterns, enterprises are not utilizing new resources to proactively seek signals of change nor do they understand their power to influence individuals and communities.

Val Sribar, group vice president of Research at Gartner, sites Amazon’s and Netflix’s use of recommendation engines as good examples of organizations leveraging collective intelligence to support their pattern-based strategies. Sribar agrees with Baynote that recommendation engines identify new patterns in behavior as customers browse and purchase. While Amazon and Netflix are highly popularized cases, we’ve helped hundreds of other well known brands tap into their collective customer networks to significantly increase revenue through cross-selling and upselling, and higher customer loyalty.

We’re excited to see Gartner take a leadership position on this important issue and look forward to working with them and our customers to bring best practices related to collective intelligence to the forefront of modern business strategy.

The report, Americans Reject Tailored Advertising shows that 66 percent of adults said no to tailored ads. Not only that, when informed of specific behavioral targeting techniques that marketers employ to create the ads, even higher percentages — between 73 percent and 86 percent — oppose tailored advertising. Those techniques include tracking behavior on websites and in retail stores.

For a more detailed analysis of the findings, you should check out Stefanie Clifford’s coverage of the report in the New York Times.

We’ve been talking about the pitfalls of behavioral targeting for years, so it’s nice to finally see some national research that tells marketers what consumers actually think about this shady technique. In this age of identity theft and mounting concerns over privacy in general, a practice that proactively profiles a user — perhaps over the scope of many Web sites and over a period of several months — will sound alarms even among the least conservative of us.

Beyond privacy concerns, there are bigger issues with behavioral targeting related to accuracy and quality, that many marketers still don’t understand. Traditional behavioral targeting struggles precisely because it tries to discern what I want now based on my past behaviors. Consider the impact of focusing on historical interests instead of current intent: If I bought a gift for my niece on Amazon.com last week, I certainly don’t want to be bombarded by ads for similar products that probably aren’t relevant during my next visit.

Another way to think of this problem is to consider the idea of roles or what personalization systems might call “profiles”. Humans have far too many roles in life for a profile to possibly predict what a user wants on any given day. A woman shopping for baby clothes, a tie for her husband, and a gift for her sister may appear schizophrenic because she is acting in three different roles — mother, wife and sister. What do you show her next? Tossing strollers ads at her isn’t going to be effective now that she’s shopping for a new cocktail dress for herself.

This is the pitfall of profiles. In a given month, an individual will have thousands of roles. Knowing my past is not necessarily a better way to predict my future. In fact, this phenomenon has been known by psychologists and other scientists for years — humans are animals of context and situations, much less than of historical profiles or roles.

Enter Intent-driven Targeting

An alternative that solves the issues with both privacy and effectiveness is one centered on understanding the users’ intentions, instead of their clickpaths or profiles, and pairing that knowledge with specific content, product and advertising recommendations. This approach relies exclusively on the collective wisdom of like-minded peers who have demonstrated interests or engagement with similar content and contexts.

The concept of profiles is completely removed in this case. Instead, through understanding expressed or implied intent, content appropriate to the user’s current mindset can be delivered.

Most importantly, it kills two birds with one stone: Users get useful, accurate recommendations and ads, while still avoiding the whole privacy mess.

Let’s start with a little history: years ago, before the internet, we went to the store to buy everything. Shelf space was limited so stocking popular items was the best way to make money. Today however, online vendors have an unlimited amount of “shelf space” allowing them to stock not only the popular items, but also the not-so-popular items whose individual sales taper off to create that eponymous Long Tail. These items might not sell like the latest Justin Timberlake album, but the theory behind the Long Tail tells us that you’ll always sell at least one- which means that if you sell one of enough of these items, you could make more revenue than if you DID just sell the latest Justin Timberlake album. Plus, your profit margin and customer satisfaction is much higher with these unpopular products than the popular ones. Take a look at your own site; you’ve got your own long tail too- but are you making the most of it?

The key to tapping into your own Long Tail an achieving a double-digit lift in revenue is to make on-target product recommendations to shoppers and identify your up-sell and cross-sell opportunities. This is done most accurately and easily by understanding like-minded peers and the Wisdom of Crowds. This Wisdom of the Crowds is garnered by observing a site’s invisible crowds or silent groups of like-minded peers whose intent is determined by their implicit behavior instead of their explicit feedback. Just think of it like taking those annoying magazine surveys…do you really exercise daily or watch less than 2 hours of TV a week? We didn’t think so. But by harnessing the implicit wisdom of these invisible crowds and essentially letting your customers sell for you, retailers can understand true buyer preferences and intent rather than using misleading clicks to inaccurately determine their interest. This way, vendors can tap into their own Long Tail.

We’ll be speaking more on this topic as well as giving some helpful tips you can use right away to tap into your own long tail at our session, Profit from the Long Tail: Let your Customers Sell for You, at the Web 2.0 Expo in San Francisco. Please stop by to listen or visit us at booth #5 in the expo’s LongTail Pavilion.