I credit Malcolm Bastien for inspiring the headline who says in a recent blog post: “Just like the enemy of web analytics is measurement of page views and visitors, the enemy of social media listening is listening only for brand mentions.”

Such a great point. Counting brand mentions is only the tip of the iceberg yet it’s what so many of the current crop of social media monitoring tools are trading on. They count brand mentions, aggregrate content, and report on it. While they get you looking at social media, they are decidely slim on contextual information unless you have time to do a LOT of reading. Malcolm talks about the importance of framing social dialogue and I think that is key to the next step in the evolution of these tools.

Framing (Malcolm’s take may be slightly different so I won’t speak for him) is essentially a more holistic view of data. It’s putting data in context – relative to a market, a segment, a shared affinity or a goal. This is a shortcoming of many current monitoring tools as they focus on mentions, popularity, sentiment etc. but don’t do much to help you understand the implications of all this data.

Searching for brand mentions and verbatims is relatively cheap and easy to do (as evidenced by the price pressure in monitoring right now). Marketers need more context than brand mentions to make sound business decisions and large companies just coming to the social media table are reluctant to bet significant sums of money on such thin evidence when it comes to informing a large marketing campaign.

So what’s next? Malcolm mentions the new feature from Scout Labs called “Quotes”. It’s a good first step. They are attempting to push the envelope a bit in terms of giving their data more value to the end user. But what can you do to really push the envelope?

How about trend discovery and text analytics? Monitoring solutions rely on keywords – that means that you will find what you look for. What current monitoring tools lack is the ability identify trends or patterns in data that you didn’t know to look for. Text analytics opens this door.

Text analytics is basically a trend discovery engine of the best kind. It identifies trends or patterns in data that you didn’t know to look for. It is a very compelling and powerful technology. Combine deep text analytics with a segmented approach (something we do with SocialSense) and you have what we think is the next step in the evolution of social listening tools. I’m curious, what do you think is next?