Twitter Analytics

Twitter Dominates … Does Your Brand Dominate?

Twitter represents about 50% of all messages and verbatims that are currently occurring in the Social Media space. Because of this, it is important that you don’t let your social media analytics be dominated by Twitter verbatims.

If you are just implementing basic monitoring of social media, and reporting out ‘pure volume’ numbers, it can be quite biased by the Twitter volume. And then your analysis can also be biased by the type of people who are using Twitter.

It is important to balance your analytics and ensure that the sample is representative of the full breadth and depth of social media interactions.  It is not just Twitter Analytics that you are interested in, it is ALL analytics!

The most intelligent, effective and efficient social media Market Research and analytics (not just monitoring) are those that let you see what’s happening to your company and your brand with a complete picture – basically, a 360-degree view of the social media landscape. Because you are looking for the whole story, i.e., the big picture, and not just bits and pieces, it is critical to be comprehensive in your approach.

You don’t need answers to just one question, but many questions – this will help you to act and react to important events and to better plan ahead.

A few other things to think about if you have a monitoring program which is heavy on Twitter & Twitter Analytics:

  • Only about 7 to 8% of all people are on Twitter
  • Given how important having a ‘following’ is to Twitter and its users, many people are using Twitter just to ‘feel like they have an audience’. So, the validity and the strength of their comments – positive or negative – should not be overly-represented in your sample.
    • If there are a lot of Twitter comments in your sample, it is important to consider whether or not your sample should be weighted, to better reflect usage within your target population.  Balancing the Twitter analytics with other analytics is imperative.
  • Some people use Twitter to just ‘feel like they have something to do’. If 10 minutes go by and there is not a new email in their “in box” or a new message on their mobile phone, it can feel like ‘nobody cares’ and ‘no one is listening’ to them!
    • For those people, just ‘checking in’ can be satisfying.
  • Some people consider their Twitter communications to be an integral part of their daily activities. For those people, communicating on Twitter is actually part of their daily “to do” list.
    • Anything that presents itself as a “to do list” is often masquerading as being “something that needs to be done”, even though it doesn’t really need to be “done”, at all. Therefore, comments on Twitter should not be given a weighting in the analytics that they do not deserve.

Some people can ‘write’ about things … just to have something to say. Basically, this is the problem of “thinking about what you are thinking about” – or not actually living your life – but lining up things you can Twitter about later – so, you have ‘something’ to say! 🙂

  • Some people are using Twitter, and other forms of social media, just to sell – not to tell.  It is important to ensure that the commentary you are collecting is not just ‘full’ of self-promoters.

 It is important to have a ‘data sampling plan’ for the internet that covers the depth and breadth of tools being used today. It is important that you are gathering data from the entire Internet, not just relying on Twitter analytics.

So, the key question to ask, in your quest to do better Market Research around Social Media, and not just to do monitoring (which collect reams and reams of non-actionable data) the key question is … “IS MY DATA SET BALANCED AND REPRESENTATIVE?”

We trust this has given you a few things to consider … in your quest to understand and dissect Social Media “Monitoring” vs. “Market Research”.

We look forward to getting your thoughts on Twitter Analytics vs. Comprehensive Analytics ….

Please CLICK the BLOG ICON to add a comment to our blog related to Twitter Analytics.  Share some of your own, personal thoughts on ‘mistakes’ to avoid or ‘lessons learned’.

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