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.

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.

The post, written by YouTube product manager Shiva Rajaraman, explains that the majority of YouTube users who rank videos give them a perfect 5-star ranking. He continues:

Seems like when it comes to ratings it’s pretty much all or nothing. Great videos prompt action; anything less prompts indifference. Thus, the ratings system is primarily being used as a seal of approval, not as an editorial indicator of what the community thinks about a video. Rating a video joins favoriting and sharing as a way to tell the world that this is something you love.

Rajarman goes on to solicit the community for feedback on how useful the current ranking system is and what can be done to improve upon it.

We’re really glad to see that YouTube is finally examining its rating system with an eye on delivering more value to its community and look forward to seeing how the system evolves from here. Ratings and user generated reviews, though often misleading, have become an expected part of the online experience and encourage deeper engagement. I don’t think anyone would take away points from YouTube on their ability to engage an incredibly large, diverse and influential community of users. However, YouTube’s review system- and others like it –  must also find ways to inform ratings based on valuable sentiment and implicit feedback gathered from the vast majority of their site visitors. Not the loud minority.

With a truly integrated approach to recommendations that blends both implicit and explicit feedback, companies can expect to improve engagement and overall user experience by directing site visitors to the best content based on their intent.  I talk a lot about this concept in my paper, entitled “7 Deadly Biases”.

In the end, explicit versus implicit user feedback shouldn’t be viewed as an either/or scenario. Please let us know your thoughts on the matter and share examples of sites that are doing it right.

Is Relevancy and Privacy Inversely Related? No.
So here is the question, Can we target users effectively without diminishing their privacy? Does increased targeting inherently scale with lesser privacy? I would say no. Google has made their fortune on contextual targeting. Users provide Google with questions and Google matches those questions with two types of answers, those derived from their organic search algorithms and those offered by advertisers. The success of this solution marked a victory for contextual targeting over the profiling. Google effectively targets content to users by understanding the context and intent of users, exactly what Baynote does as well. No profiling or personal preferences were required.

This topic is a bit much to cover in one post, so expect a few more posts to follow up on why relevance and privacy are mutually exclusive.

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.”

Let your website do the targeting

The instance a user lands on your website, they begin communicating with you. Its not always loud, but if you are listening this will become clear. They speak with their bounce rates, exit pages, entrance pages, site search terms, mouse movement, and many other web actions. This communication is not necessarily a function of generation, but one of context. By trying to focus on who your users are, which is expensive and typically inaccurate, you can lose focus on what really matters, their context. A user’s context drives their intent, and being tuned to that allows you to unlock the Collective Intelligence of your website. No marketing team or focus group with demographic data will be able to match the insight gathered from the collective intelligence on your website.

A Collective Intelligence Powered Site

So what does a site that is powered by its collective intelligence look like? This site will bring the right content to the right users as soon as they land on a website. Each page will recommend the content or products that will most likely result in a successful conversion. Search results will be tuned into the context of the search and reflect usage of the results by monitoring successes and failures of searchers. The Search Marketing strategy of the website will pivot around the keywords used by the users that are driven organically from Google and used in the site search. Landing pages will be dynamically created with the content or products found most useful by users and will update real time to reflect these shifts. eMail marketing campaigns driven by site observations will include clusters of products and content within the same context to increase engagement. Marketers will utilize reporting of the website’s collective intelligence to drive mutli-channel strategies including offline and mobile channels.

Automatic, not Autonomous

Harnessing the collective intelligence is not about creating an autonomous website. It aids marketers in providing a complete user experience that will create customer loyalty and bring users back to the site for future transactions. Solutions that harness the collective intelligence of your site must provide reporting and management interfaces that allow you to apply business rules and push promotional content or products. This mechanism allows you to maintain control of your site while using the collective intelligence to automate laborious and intensive data-driven tasks.

Discover Context, not Identity

Leveraging collective intelligence on the web is about understanding the context of your users through observing their interactions with your website, not their identity. By not requiring demographic information, the act of gathering your site’s collective intelligence is not perceived by your customers as an invasive practice. Therefore, this contextual approach is not only an effective and powerful targeting technique, but it keeps your brand intact by maintaining the trust of your users.

Lets step back and imagine a world where Behavioral Targeting services and Contextual Targeting services lay down their arsenals and rejoice in consumer targeting brotherhood. That day is here. Check out Jack Jia’s interview over at Behavioral Insider where he discusses the power of combining behavioral and contextual targeting techniques to bring balance to the force. After all, two heads are better than one.

clipped from www.readwriteweb.com

Sometime today, according to BusinessWeek, social news site Digg will launch a slew of new social networking features that will put the site in closer competition with services like Facebook and MySpace. The features will enable easier communication between diggers and allow them to form groups based around their interests. “This is really the first time that we have enabled communications between users,” Digg founder Kevin Rose told BusinessWeek.

The Age of Profile Targeting

In the age of social networking giants like Facebook and MySpace, profile targeting is on the rise. Here I am using profile targeting as simply another flavor of behavioral marketing. ClickZ has a great definition of Behavioral marketing:

Behavioral marketing targets consumers based on their behavior on Web sites, rather than purely by the content of pages they visit. Behavioral marketers target consumers by serving ads to predefined segments or categories. These are built with data compiled from clickstream data and IP information.

On paper this seems logical given the wealth of user information found in these profiles, but delivering the right content and messages requires more than simply segmenting users into profiles and matching them with related information.

Targeting Behaviors out of Context

Users have profiles that span different segments and these different profiles are rarely, if ever, active all at once. Truly effective targeting must also take a user’s context into account before providing ads,content, or product recommendations. While behavioral targeting may often provide an improvement to marketing initiatives, ignoring a user’s context, as done by most behavioral targeting solutions, will continue to yield less than optimal results.

Nevertheless, these sites that facilitate social interactions amongst web communities do indeed possess a wealth of information, and deserve the hype surrounding the bunch. In Part 2 of this topic, I’ll dive into how this information can be used to more effectively target users.