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 most vocal and misleading group of contributors is what I like to call the “squeaky wheels”. This could be those people who simply like to complain. But it can also be any one of us when we have a negative experience. Negative experiences tend to stand out more than positive ones and also encourage us to take action.

After the last design, users reacted similarly. Despite this, the network grew pages viewed per visit by 50% and its user base to 175 million users to be the largest social network. Justin Smith of the Inside Facebook blog said “Even though there are a lot of negative votes in that voting app, I think it’s a pretty self-selecting group of people and not necessarily representative of the entire population.” Justin is spot on with his analysis. 850,000 people voted in this survey, which is roughly .5% of the Facebook population. This backs up the squeaky wheel bias theory which states that “At the end of the day, 99 percent of the population remains unspoken for.”

Personally, I think the new redesign is a great update(although I haven’t voted), so I am glad to hear that Facebook is taking the feedback with a grain of salt.

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.

While many marketers, website owners, and other business owners already know this, you’d be surprised how many of them use tools that rely on this overused and inaccurate metric. Many analytics, and even recommendation vendors, are relying on clicks as a KPI to either display to their customers or in the case of recommendations, to power their algorithms. The quality of recommendations or any kind of targeting based on user observation is no stronger than its ability to understand when and how a user has succeeded. Bounce rate, as Avinash points out, is just one of those metrics. At Baynote we track dozens of them, and clicks are by far the least utilized when validating success or failure.

Bottom line: When you are talking to analytics vendors, ad networks, or recommendations vendors ask them what KPIs their algorithms are relying on. If they say hits or clicks, they are idiots by Avinash’s definition, and he’s a smart guy.

Without further adieu here is the list of 7 criteria from GigaOm:

• Adaptability: How easily can you modify the application? This can be as simple as adding fields or building dashboards, or as advanced as a programming platform.
• Reliability: How much can you depend on the system to function well? This boils down to four things: Performance, availability, scalability and security.
• Task productivity: How effectively can your users accomplish their goals? How many cases-per-minute or entries-per-day can workers do, and how many errors do they make?
• Price: How much will it cost — really? Because SaaS offerings are so varied in pricing, it’s hard to compare them. A better model is to create several benchmark subscribers (a 10-, 100-, and 1,000-person organization) and compare upfront and ongoing costs for them.
• Back-end integration: Can you plug it in to other things? Any enterprise SaaS offering will have to work with other systems, for everything from authentication to data sharing.
• Longevity: How long will the SaaS company be around, and what’s your exit strategy? With ISVs, you could ask for software in escrow. But as the sudden disappearance of Coghead shows, when a SaaS provider closes down, your entire IT systems can vanish with the flick of an “off” switch. Offers from Intuit and others to help stranded customers notwithstanding, this is a big problem.
• Ecosystem: How many third-party developers and integrators surround a particular platform with plug-ins and add-ons, and how active are they? A vibrant ecosystem means a more extensible, flexible solution.

Fortunately, I can say with confidence that Baynote satisfies each of these criteria. I’m not going to go through each item line by line, since these 7 criteria have different implications for each business. However, our sales team will be happy to do just that. Contact them here.

The Era of Experts, Influencers, and Mavens
Guy noted that the old way to put a product in front of the masses was a trickle down approach. The goal was to suck up to the influencers, experts, and mavens that had an abundance of social capital and a platform to distribute your message to the masses. To make his point, he mentioned about a dozen of these “mavens,” including tech bloggers, popular media personalities and himself.
Bottom Line: Suck up to the important people, and that will effectively trickle down to the masses.

The New Age of the Masses and Crowds
Because of the advent of new technologies like Twitter, Guy argues that the most effective way to connect to users is from the ground up. Connect with users that are relevant within the context you are trying to reach. Influencers and experts will never be as valuable as the users themselves if you can reach them.
Bottom Line: Don’t ignore the experts, but make your audience and your users a source of collective intelligence that drives your business decisions.

Technologies like Twitter and Baynote’s Affinity Engine allows marketers to leverage their users in ways previously impossible. While Twitter allows marketers to speak to and gather feedback directly from their audiences, Baynote allows marketers to poll users through implicit observation. Ultimately, its all about making sure that your audience finds your products in a timely manner. Your audience may be on Twitter, but its definitely on your website and that is where Baynote comes in.

Gaming Bias

Another type of reviewer is someone who is “gaming” the system. Sometimes such gaming is malicious, but often it’s altruistic. While writing this article I went onto Amazon (Nasdaq: AMZN) to look at the reviews of a book I co-authored called Wired for Speech. The first one was very positive; perhaps someone my coauthor knows. But I have no doubts about the second 5-star review, titled “Amazing Insight.” To my surprise, it was from my dad! Enough said.

Gaming such as this is actually the rule, rather than the exception on Amazon and other media sites where products have authors or artists and personal connections abound. I admit to having given 5 stars to articles on my company … heck, if I can do it for this one, I will. Go ahead, try it out, give this five stars if you can!

For more of this whitepaper on The Seven Deadly Consumer Biases click here.

The irony is that the guy used Amazon’s Mechanical Turk to game Amazon.com. Here’s a link to the full article.

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.”

Click Here to take survey!

I’m not exactly sure went wrong here, but there are a few problems with recommendations driven by previous user purchases that are likely culprits in this disaster. Often multiple users on the same account that purchase different products. This creates a pretty useless source for data. Here is a link to the full image.