If there’s an exception to that rule, however, it lies in social media. Print, broadcast and e-mail marketers have had decades to hone their craft and perfect the way they process data. Social-marketing marketers are still the new kids in the class, and there are still a few kinks in their system.
Many people using social media for business purposes (yours truly included) aren’t professional marketers or publicists. There’s a temptation to rely on soft impressions — personal experiences and secondhand stories that create the rough impression that you know what’s going on. When you finally decide you need some data on how your social content is performing, there are a host of tools, both free and paid, for measuring basic social-media metrics such as click-throughs, re-tweets and the like. Those are fine for measuring your own performance — but how much do they actually tell you about how people see your brand?
The next step, for many people, is to use some kind of professional, automated sentiment-analysis program. These programs will scan social networks for mentions of your brand and create a more robust picture of how people are talking about you. They rely on keywords, syntax and other factors to figure out what each tweet means for your brand. Unfortunately, computers still struggle with the complex, idiomatic way humans express ideas.
In the lead item from today’s SmartBrief on Social Media, Tom Webster asks some very pointed questions about the limits of what machines can tell us about human sentiment. These questions aren’t purely academic, either. If you’re relying on brand-sentiment analysis to make marketing decision and your data are bad, it’s only a matter of time until you reach a disastrous conclusion.
But what is the alternative? Ignoring the problem isn’t an option. For large, well-known companies, measuring social sentiment by hand would be prohibitively expensive. Social data need to be automated to some degree to be scalable. Perhaps all social marketers can do for now is to take in their machine-harvested data — and then consume with a healthy dose of skepticism.
How are you measuring brand sentiment? How do you cope with flaws in automated sentiment-tracking programs?
Image credit, Korovin Vitaly Anatolevich, via iStock