We are in a new world of public relations management where missing out on a story or tweet from an influencer can potentially cause your brand a disaster. As communicators, it is essential to perform ongoing media monitoring and social listening for mentions of your brand and key topics that you’re tracking. This can be especially daunting when your brand has a name that is cited thousands of times a day in social and traditional media. Picture how difficult it is to sift through all that information.
On average, we’ve found that less than 20% of all social media data analyzed for clients is relevant to their brand and key messages. A lot of the irrelevant data is generated because brands have common names.
Take Chipotle, for instance, whose brand name is also a common dictionary word for a type of sauce or pepper. How can Chipotle’s communications team monitor thousands of tweets and news articles that use the word ‘chipotle’ and isolate when they are referring only to the brand? Merely using keywords to track online mentions is not enough.
Most media monitoring solutions encourage users to use Boolean logic to pare down the volume of data collected, but applying too many filters puts brands at risk of missing valuable information. For instance: what if a tweet discusses a bad batch of chipotle peppers used at a Chipotle location? If you are filtering out “pepper”, you would have likely omitted this result.
Tide is another example of a brand that deals with common name issues. How can Tide’s communications team focus on articles about the Tide brand and not changes in sea levels or Roll Tide Bama or any number of uses? A typical media monitoring solution would suggest that you solve this problem by filtering out articles that contain keywords like ‘tide’ and ‘ocean’ together. But, that could still cause you to miss essential coverage such as an article claiming that Tide detergent is causing water pollution.
The bottom line is that simply automating keyword tracking and pairing it with Boolean logic is not a strong enough media monitoring solution. You need to pair automated media monitoring with high-quality human analysis to ensure that you are working with data that you can trust.
