Taking the stats that LinkedIn or Facebook provides, usually in nice to look at dashboards, can seem really easy. But after a while you will notice that these figures are not providing any insights into how well you are doing according to your business objectives. Augmenting the stats you measure does add extra work, but can provide more a compelling performance story.
For example if your goal is sales or signups, then all social media posts should have UTM tracking and the success can be measured in Google Analytics.
Or if your goal is awareness and front of mind consideration. You can report on impressions for awareness, but by manually stripping out the Paid and focusing only on ‘Organic Impressions’ you can see the true performance of your content.
Another way brands can augment their social media stats is to pull out all the important metrics to them, use a control column in excel to add these up, and hey-presto you have a bespoke business metric for social media.
Here is an example of how I have done this for some of my clients.
As you can see by only focusing on the positive tangible actions from the audience, you can create a wonderful metric to measure over time. You can compare this to other brands and start to learn from spikes and troughs in performance. Was this a certain announcement or using a new piece of media in your creative. Finally, now you can provide solid data back into the production workflow for creating assets that matter, and of course less of the ones that don’t.
Using automation or sampling to provide a qualitative sentiment can be full of errors and costly. Even with the most complex of sentiment rules the machine will not recognise irony or other nuances in how real people talk about your brands.
Stripping out the uncertainty within the written form you can always rely on data. By looking at only the positive actions someone can do on your channels and removing the negative actions you can create a simple NET Sentiment for each of your brands. This is not only easy, but also cost effective as all it takes is a simple ‘control column’ in your excel tables before you pivot them.
For example below is a model I have used in the past, which looks at quantitative sentiment for Facebook. By dividing the NET Sentiment over the SUM of all sentiment you can create a nice and simple to understand index score of between -1 and +1.
Harking back to my old traditional marketing days where in PR we would use ACE (advertising column equivalency), which basically meant the area of newspaper columns the PR created and what that would have cost to buy as an advert. The same principles can be applied in the digital world, where you look at what your social media content has achieved and work out how much it would have cost you to buy the same exposure.
Sounds simple, but there are a few pit-falls, namely making sure you only report against the ‘Organic or Earned Impressions’ you have received and also to set a realistic CPM (cost per thousand impressions) based on results from your existing ad activity.
This is relatively easy to do for Facebook, Twitter and LinkedIn, but does become more difficult for Instagram, Snapchat and TikTok where it is harder to separate the paid and organic data.
Here is an example of how that could look using Facebook data for a mock brand in total for the year and monthly.