Finding Order in Chaos
Brand managers typically draw feedback and information from their brand's own Facebook pages—top-brand specific, individual products, etc.—and also track:
- Major blogs that follow the company or brand
- Posts or comments from consumer-facing articles like news stories and business-facing articles in key journals and industry magazines
- Tweets that mention their brand or products
- Predictions and insights from analysts, and
- Their own feedback loops from websites and user comments.
Over the course of a month, this could be hundreds of thousands of individual items from different viewpoints, a range of "authors," and myriad topics.
Traditional search tools are virtually useless for these tasks. The data are simply delivered too quickly and the topics, comments, and opinions as framed by the writers may have little in common with key terms and phrases that brand managers think are important.
Content Analytics overcomes this issue by assessing the intelligence of what is being said, versus the words used or even the source of the information. It identifies concepts that are:
- Identical, even though expressed differently
- Closely related, and/or
- Different yet expressed similarly.
For example, a movie can be a "bomb" at the box office and it could "crash and burn" with audiences, while another movie's "crash scenes" provide box-office adrenaline and a heroine's performance "burns up the screen." So how do you craft keyword searches that identify each of these concepts without being overly inclusive and getting data about the others? The answer is, you don't have to.
CAAT's content analytics algorithms easily identify these examples as different concepts, and can automatically find or group only data that pertains to one of them. Honing-in on just those elements that are important helps turn the chaotic rush of data into a vital and quickly understood flow of information.
