We know how easy it is to confuse a ‘like’ or a ‘share’ with meaningful content engagement or capturing a new fan. However, looking beyond the numbers often paints a different picture.
That’s why we have long advocated human analysis to get behind the social media buzz, especially when assessing the success of a campaign.
Beware of social media bots bearing gifts
As you may know, ‘bots’ are programs or algorithms that post automatically on social media, but appear to the world as real human users.
There are also ‘human’ bots – people paid to post on social media sites either manually or with automatic posting tools.
These users and their posts often appear genuine. A recent article in Forbes Magazine revealed that 30% of users are deceived by bot posts.
So with machines pretending to be real people, and real people acting like machines, it is difficult to measure your organic engagement.
Loved or Targeted? The Truth will set you Free
On a recent project, we could not tell if certain social media users represented actual organic engagement.
We noticed that some Twitter supporters were sharing a YouTube video posted by another well-known brand. These users looked ‘real’ in that they had an online presence beyond Twitter. No traceable presence outside of Twitter is often a sign that you’re dealing with a bot. They also had a reasonable number of followers who also looked genuine. On the face of it, these seemed like actual users engaging with the Brand’s content, but we needed to be sure.
We then noticed that most of their posts featured other well-known brand campaigns with no product category preference, i.e. they were posting about mouthwash one day and motor oil the next. Their followers and those they were following had similar Twitter behaviors. However, the activity was sporadic enough not to point to a paid scheme. Why?
On average they posted one or two tweets daily and tended to post about each campaign just once. Repeated posts about individual campaigns is another sign of paid activity or bots.
Whilst we believed these users to be real people, we were unable to decipher their motivations for posting. One theory, was they were attempting to build their followers and thought that posting about well-known brands might help.
We may never know the true motivation behind these posts. But what matters, is that these users did not engage with the campaign content and did not seem interested in the brand running the campaign.
Without delving into the data, we would not have reached the proper conclusion. This has clear implications for the success of the campaign, while the topline numbers were suggesting a different outlook.
The lesson here is not to completely distrust your numbers, but to take the time to fully understand them before you make any strategic decisions. You may not get the initial results you hoped for, but you’ll get the info you need.