„If you can’t make me love you, at least make me laugh”. Creating valuable KPI from Facebook reactions

What is a sentiment analysis and how to calculate it?

In general, it’s an application of quantitative scoring to text data. For example, if I say:

„I was satisfied with overall customer support, but the product lacked its features”,

then it’s really hard to say what I really feel. I’m positive about support (one category) and negative about product features (second category). I can also add a quantitative metric of +1 for customer support and -1 for features. In this case the sentiment is 0.

One sentence may have many sentiment expressions, e.g. Amazon reviews. In such situations, customer feedback will be shared by many discussion categories. I strongly recommend running such exercise once, either remotely or using natural language processing algorithms. There are many tools that allow you to do so as well. The links I’ve provided before also share methodological input if you’re interested.

However, in our example, we want to zero in on much simpler thing. We want to use Facebook reactions as an applicable KPI without further categorization (even though it’s possible to do later on).

It’s super simple. I came up on this idea, while I was scrolling through feed and realized no one has done it before.

In general, you need to download the list of your posts and their reactions. After that one just needs to apply this simple measurement:

Positive: (+1) for Love and Haha

Neutral: (0,0) for Like and Wow

Negative: (-1) for Sad and Angry

Once you have it, you deduct % of positive reactions from % of negative reactions. Net sentiment is achieved, which is our main KPI.

Connecting sentiment analysis with actual Facebook reactions

It’s best to show the concept of the analysis on the graphical presentations.

I have downloaded dummy data from here.

In the following section there will be two types of analysis presented: 1st one focusing on posts effectiveness and 2nd one presenting more high level view of overall sentiment trends.

Single posts analysis

In this analysis, you need to aggregate the reactions into one simple dataset. Here is how it should look like:

Calculate shares of your positive and negative comments, subtract them from each other and here you go. Now you better understand effectiveness of your posts as you have access to net sentiment scoring.

From the substantial volume of reactions, it’s much easier to understand if our post was liked or hated. At the same time, it’s now possible to double-click into the ones that resulted in poor customer feedback.

You can use this analysis to:

1. Better track your campaigns and communication results, one by one.
2. Identify the trajectory of your discussions. It’s easier to see if you’re moving into right direction or not.
3. Deep-dive into posts that drive the best sentiment or find the ones that are not liked at all. It’s easier to scale up good content or remove the wrong one.
4. Run qualitative analysis on actual comments and categorize them properly, so you generate richer customer insights.

Trend analysis

Facebook reactions data have many other use cases, but there is additional one that I personally like. You can easily use this to create a very simple, yet powerful managerial dashboard for this social media channel.

By plotting volume of positive, neutral and negative comments, marketers and community managers may quickly understand whether their posts generate higher discussion buzz and what is their sentiment. It’s also valuable to understand the engagement vs. emotional affinity.

As you can see on the chart above, audience reacts on posts, yet most of their reactions are neutral (most probably “likes”). In this case we can think of boosting the emotions. I think I don’t need to remind you that emotional reactions also increases the reach and visibility of your fan page.

Such analysis is valuable from various standpoints:

1. You can see the trends in a longer time frame
2. It can be used as a part of social media team dashboard
3. It presents the discussion spikes next to sentiment, so you can see the audience engagement
4. It’s easier to understand if our audience is emotionally attached.
5. Potential for deep-dives to identify most engaging and emotional posts

To conclude, Facebook reactions can be used to drive valuable customer insights. Despite their high business insights utility, I haven’t found out too many uses cases online, therefore I decided to publish my own.

We can drive customer experience knowledge from pretty much everything. It’s just good to have your eyes opened and be cautious as free knowledge is available for you everywhere.

By connecting Facebook reactions with actual emotions and quantifying them, you can quickly create easy-to-use metric, which may help you with improving your communication with potential audience. I suggest you to try it.

Let me know if you have any questions or shoot me an e-mail at: maciejwilczynski24@gmail.com if you want to discuss the details.

Good luck and enjoy your new KPI!