Nobody has the definitive solution for how to measure the success of Social Media. At least not yet.
Sure, you can talk about online ‘buzz’ created by how many blog posts, tweets, videos, status updates etc. that occurred for a certain brand, person or topic, but what’s ‘buzz’?
Virtue, a company which does ‘technology solutions for social media marketing’ has just released its second annual list of the ‘most social’ companies in the world on this basis. I was interested in the result, which was, unsurprisingly, the iPhone, but took it with a pinch of salt. Of course, Apple and others on the list have built strong online reputations, but ‘buzz’ doesn’t necessarily mean success and it certainly doesn’t guarantee revenue as the mentions could as easily be negative as they could be positive.
Also, it doesn’t mean brands have done anything proactive in Social Media. For example, the NBA is fifth on the list, probably more to do with the prolific tweeting of this guy and the activities of certain teams than the association itself.
Some may say that no genuine Social Media campaign guarantees increased revenue in the short term; after all, it’s about building relationships through conversations and establishing long-term loyalty, not old-school sales and marketing. I would agree with this notion, but we do need to be able to define success in a better way when talking about Social Media. For me, ‘buzz’ simply isn’t enough.
We need a stronger grip on understanding whether a Social Media campaign changed people’s perceptions and whether they are now more likely to engage with the brand (e.g. buy the product or service). In addition to measuring ‘buzz’ from online mentions, maybe we should also incorporate offline and online surveys from a mixed socio-economic demographic.
To make the findings more compelling still, perhaps companies should hold a focus group of the key target market, not just at the beginning of a campaign, but at the end as well. People should not simply be asked if they had heard of or seen the campaign, but how they would rate different elements on a scale.
This would offer more data as it would be more varied, which in turn would enable better analysis and greater learning.