You already know that prior to joining Forrester, I worked in the information retrieval industry and will forevermore be fascinated and frustrated by search. B2B marketers face unique search challenges, not only to select the best keywords and improve on organic rankings, but also to direct buyers to the information they need to make decisions and move closer to a purchase. In recent research, we found business marketers don’t use microsites and landing pages to guide paid clickers to the relevant information. Of the 86 unique ads we reviewed, only about a quarter took buyers to pages custom-designed for a paid search campaign and neither dedicated pages nor general ones provided keyword-related content consistently. Why do B2B marketers struggle here and can technology help?
Landing pages and microsites are hard to design and even harder to maintain. Consider the matrix of possibilities a B2B marketer faces when they buy several hundred – if not thousands – of keywords and then try to associate them with the right Web page, campaign, or offer. It gets overwhelming quickly, so landing pages fall short as a best practice. What if marketers could figure out which pages match keywords best and add offers to those pages instead of creating campaign specific content?
Matt Brown, another analyst at Forrester, and I got to meet a company called Baynote, who may have a solution here. Baynote’s social search technology – which is more impressive, in our opinion, than their AdGuide or navigation interfaces – uses implicit, community-based information, instead of just words and formatting, to rank pages. Their technology can observe buyer behavior – clicks, scrolling, how long visitors stay on a page, where they come from, and where they go next – to learn how much users “like” a certain page. They use this implicit voting to boost or demote pages in search results and – based on their demonstration – do a better job than statistical or algorithmic ranking alone. The best part? Once you embed their html tags on a Web page, the data gathering and ranking happens automatically – no human tagging or tinkering with keywords. Instead, marketers can focus on improving the content, message copy, and offers on popular pages, while fixing or getting rid of the pages to which no one pays serious attention.
What’s the downside? The system must observe users over some period of time to learn which pages have likable content. Since this depends on traffic, Baynote was reluctant to give us a firm number, but said as little as a day or as much as a month’s worth of log files can work. Also, there are all those html tags to enter, as task best automated through scripting. Which leads to the real question: what is the real potential of social search to help B2B marketers – and online sellers in general – deliver the freshest, most relevant contant to searchers? I’d like to hear more about other social search approaches you’ve seen – or built into marketing programs – and how you think Baynote stacks up.