I’ve crawled out of my hole, blog-wise, today to write about conversation analysis. I’ve been calling myself a social media analyst for a long time, but the specific area that most interests me, around which I’ve started a couple of companies and received several patents, is better described as conversation analysis.
There are a bunch of companies that offer social media monitoring services that claim to “monitor the conversation” that social media has begun to enable among organizations and their clients, customers and other constituents. A huge shortcoming of them, as I see it, is that they really are little more than digital clipping services. All they find is what is indexed by search engines and when they find it, they are looking at web pages, not conversations. They miss a lot.
They completely miss the conversations that take place on mailing lists and in web forums that aren’t friendly to search engine crawlers. When they do find digital conversations, they don’t analyze them structurally to identify individual posts, replies, timestamps and so forth. This means that they aren’t really getting a valid picture of how many people are talking (perhaps the most important metric), how many topics there are, how many people are engaged in each topic and other metrics that are only possible when analysis looks beyond the single web page. Of course, they don’t do this because it is hard!
It is probably an impossible task to keep up with all of the digital conversation platforms so that this type of analysis could be as thorough as Google’s web page indexing. There are dozens of open-source and commercial web community forum platforms. Some are easier to analyze than others; some are probably totally impractical to automatically analyze. Some are deliberately quite private and invisible. Still, I have no doubt that conversation analysis is worthwhile, if only to solve the fundamental problem of listening to an enormous group of people whose concerns and viewpoints matter to you – your customers, if no one else.
Most of my work over the last nine years, aside from detours into necessary infrastructure building, has been focused on this challenge – how do you summarize millions of postings in digital communities? How do you summarize the topics under discussion, how those topics are changing over time, how many people care about those topics and so forth. Automatic summarization has never made an intuitive leap, so part of the summarization challenge is to automatically select a tractable number of typical phrases, sentences or postings for a human being to read, a selection that should enable the kind of leap of recognition that a computer cannot.
Anything “social” is about relationships. My goal continues to be to use technology to discover what I can about the relationships among people, brands, companies, events and so forth. Along those lines, I’ll be writing some more blog posts in the next few days. One will be on the difference between customer support and technical support. Another will take a look at how a conversation between a large organization and its constituents might be structured – how today’s (and yesterday’s) social media fit together.
Twitter: Web Analytics (#wa)