Baselining--Your Key to Measuring Successful Engagement in Social Media

Baselining--Your Key to Measuring Successful Engagement in Social Media

So, now you have your social media strategies set.  You have a playbook developed and an editorial calendar established for the next four weeks.  Your social media manager knows to stay on message and post at specific times throughout the day. 

How do you look at your data to begin to build the knowledge base to really identify what is engaging your followers?

This question is far from trivial.

Your have two goals.  The first is to attract followers or friends:

• Fans on Facebook
• Followers on Twitter and Pinterest
• Connections on LinkedIn and Google+

While you get new followers and fans, you want to share content that will keep them engaged.  This is your second goal.  When you’re measuring the success of individual posts, one key challenge is to continually grow the current fan base.  Success of individual posts will be impossible to read in raw numbers because of the continuing growth in the fan or follower base.

It does make sense to think about creating metrics that give you the ability to understand the strength of individual posts in the context of your fan base.  The fan base is variable and growing, thus making total likes, comments, and shares numbers difficult to interpret, especially if your fans are growing at a fast pace. 

As a marketer, however, you would like to distinguish stand-out campaigns both from the perspective of getting new fans or Facebook page “likes”, as well as particularly engaging individual posts or tweets.  Baselining your data and measuring the contribution of current campaigns to historical levels, is key to identifying what campaigns or individual posts are truly exceptional.

These two practices – (i) comparing engagement to baselines and (ii) maintaining a history of posts and attributing characteristics – will  help you   develop increasingly engaging content for your followers and fans.  (See the graph below to understand the concept of Baselining.  The data shows weekly Facebook ‘likes’ for a brand page.  The magenta line shows the incremental ‘likes’  by week.  The two dotted lines represent the baseline and two standard deviations above the baseline. The higher one is two standard deviations above the baseline.)

Let’s consider a baseline in the context of brand likes or followers.  This case is easier to consider first.  The baseline is computed by looking at all of the weeks when you don’t have an active campaign underway to increase likes.   This represents the base increase of the consumers to engage with your brand socially when you’re not promoting your social presence.  The actual week-to-week ‘likes’ may fluctuate over time and the baseline will reflect metrics based on a rolling number of weeks (as many as you can, but preferably 30 or more).   We arrive at the baseline by computing the average number of brand likes over the non-promotion weeks ; that is where non promotion is defined as a week when no particular campaign is in place.  Overtime, the baseline may increase or decrease.  Increases mean there is a social momentum about your brand and it is a sign that your brand has good equity or a good reputation with consumers.  Your current fans could be commenting favorably about your brand and some ongoing stream of consumers ‘likes’ your page.  If your baseline begins to decline over time, it could be a sign that your competitors in the market place are attracting fans or followers who might follow your brand but that competitor’s voices or profile has overshadowed yours.  

Next, let’s consider the two standard deviation benchmark.  The two standard deviations rule is a statistical concept.  If we look at all of  weeks when we don’t have an active campaign going to increase likes, we will still see variation in the weekly number of page likes.  Any week’s likes on a brand page is 95% likely to be within two standard deviations of the mean.  So, the probability that the number of likes on any given week is more than two standard deviations from the average number of likes is only 5%.  If the number of likes rises to this level or higher, it could be considered as a direct result of a campaign (or some other influence) that has caused the number of likes to increase to such a level relative to the mean.  The trend below illustrates the two standard deviation rule.  The highest spikes occurred when a ‘like’ campaign was launched but, as you can see from the data, the campaign’s impact lasted longer than a single week.  The activity trended down at the end of April and, then, spiked again in September.

Now, let’s look at Baselining in the context of individual posts.  The process for baselining for individual posts from a particular brand is similar.  Because posts are likely to have various attributes, the baseline can be approached in two ways:

• Develop a baseline of ‘likes’ across all posts
• Develop a second baseline of ‘likes’ for posts with similar attributes.

The first will provide insights into the strength of a post relative to all posts to your brand page.  The second will provide insights into the strength of a post relative to others with the same attributes (pictures, surveys, videos, etc.)

For individual post ‘likes’, consider using a metric that takes into account the proportion of current fans responding or ‘liking’ a post. You might do this because as you are building your fan base, the proportion of fans liking any given post will change.  The proportion either liking or commenting or sharing should all be taken into consideration since each delivers different returns for your brand.

The key to assessing the success of individual posts is to baseline the proportion of ‘likes’, comments, and shares relative to the overall fan base.  When you find posts that are associated with a proportion of your followers “liking” in numbers more than two standard deviations above the average proportion of fans, ‘liking’, commenting, or sharing a post, then you will know you have discovered content that has risen above your norm, and has really engaged a higher than average proportion of your followers.  Over time, you will find what truly engages your followers.

The marketing data scientists at Drake Direct would be happy  to help you establish baselining for evaluating the success of your brand's social media programs.  Please contact us if you need any assistance in your evaluation efforts.

Rhonda Knehans Drake
Rhonda@DrakeDirect.com