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Taking Brands Temperature on Twitter
Every marketer is professionally paranoid. We constantly worry about what’s being said about our brands. And with more than 25 million US adults regularly sharing advice, reviews and comments about products and services online posting 50 million tweets per day and nearly 1 in 5 site visits resulting from these conversations, who can blame us? We know that unbrokered conversations about our brands, our products, our services and our people are taking place among our customers and prospects. Not knowing what’s being said worries us.
We fear the online backlash that virally spins out of control. We’re conscious of the hapless PR guy who sent out a press release bragging about his Twitter account and who, 447 Diggs later, was crowned “the biggest douche in social media.” We watched JetBlue get slammed and we are witnessing Toyota getting pilloried.
Twitter is potentially an early-warning radar for brands. By monitoring the stream of 140 character missives, brands can ideally get a feel for what’s being said, understand competitive comparisons, potentially identify brand loyalists or opinion-makers worthy of extra care and attention and intervene before problems or negative comments cascade into real trouble.
Working for typical call center clients – credit cards, telco and cable companies, financial services firms, technology manufacturers and retailers – this is a unique and differentiating value proposition. PV and his guys create a snapshot of what’s going on based on 5 to 6 hours of data mining by rating sentiment on a -1-0-+1 scale and by creating a “net emotion score” using a combination of open source data mining tools and home made analytical algorithms. Since my Twitter account is linked to my blog, 85% of my tweets are professionally oriented. According to TweetPsych, the language in my tweets indicates I post about “learning” 73% more than average. But since I rarely write about school or teaching this is a curious result. Similarly I write consistently about media, yet this tool must be programmed for different words ophrases since it indicates I tweet 17% less than average about media.
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