According to a recent Social Media Examiner study about the social media usage of both B2B and B2C organisations, resulting in over 3000 respondents; the overarching majority of marketers (86%) claim an importance of social media to their business. Whilst this result comes as no surprise and seems to correlate with other surveys, it is interesting to note that a mere 26% of marketers claim a proficiency in measuring the social media engagement’s impact on their business. This numbers becomes even more staggering if the social media marketing experience is taking into consideration. 45% of responding marketers have been using social media tactics for 2 -5 years, 5% of this group even claims a social media experience of over 5 years.
With agencies pushing hard to have their clients engage in Social Media Campaigns (particularly in B2C), it is interesting that despite big data and ongoing digitalisation, the majority of organisations is still willing to invest money without being able to measure its ROI. To proof Henry Ford’s famous quote about misguided marketing spending wrong, it is inevitable to not only be brave enough to try new things (e.g. social media efforts are largely trial and error based) but to continuously work on defining bottom line relations to increase attributable marketing spendings to business results.
Some efforts to define the social media marketing ROI exist, such as MDG’s ROI of social media or the famous HBR blog about the calculation of a “like value” in Facebook but most efforts seem to lack consistency in measurement and a lack of bottom line integration. Engagement and conversation measures are being frequently reported and often lead to wild claims about social media campaign success with high reported engagement numbers, yet they too lack to correlate social media engagement to to bottom line results or business objectives. This is were the smart, engaged and eager marketer needs to start to employ digital metrics to track multivariate correlations and subsequently develop smart social media cause effect models.