In the past, I've written extensively on the topic of social media, from platform evaluation and optimization to management and measurement. Today I want to address a troubling trend in social media: meaningless metrics. There are a host of issues created by meaningless metrics, which include but are not limited to: wasting time, wasting money, and making bad decisions based on inaccurate, incomplete, or incomprehensible data.
The primary challenge is having the knowledge and discipline to provide context around data sources, collection, reporting, and analysis methodology. In this article, I will outline nine relatively meaningless metrics, from general to specific, and how to make them more meaningful.
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Nice article. I agree that fully automated sentiment analysis solutions only capture a % of the insight from the social reviews. In fact, you are overly generous by saying they capture 70%. That's why marketers need to look for more robust solutions: those that use a combination of technology and people to decipher the overall sentiment, apply the content into industry specific categories and pull out themes. Shameless plug: Check out newBrandAnalytics. We're the only ones that do this level of processing. As our customers say, "Other social media analytics solutions achieve 30% accuracy at best, but newBrandAnalytics captures 90% of the meaning accurately!"
Bravo! Finally someone said it! As a mid 20-something I've seen both the rises and shortcomings of social media progress (belonged to myspace & facebook alike way before there was open access). It is always interesting to see how businesses and consumers alike give worth to their brand or themselves by the amount of "impressions" or "likes" they receive. My motto: "Quantity versus quality still stands."
"Quantity of traffic rarely equates to quality of traffic,"Agreed. One site might be sending a lot of traffic to your site but is it the right kind of traffic? Is it traffic that converts in the end? Remember, numbers can be twisted to say just about anything you want so it's important to really understand the data, not just parrot it back.
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