Qualitative Analytics Challenges

April 4, 2019 / by Joonas Linkola

Presis 16-9 taustakuvat v2

Even if your brand had no profiles on any social media channel, it is still out there and people talk about it. But how can you really know what they talk, post and share? And most importantly, what is the tone of voice?

From the user point of view, social media channels are the main sources of information, places to share experiences and feedback, and interact with brands. From the brand point of view, social media channels are valuable assets in marketing, branding, communication, product development, and customer relationship management. Platforms such as Facebook, Twitter and Instagram provide data from the users, trends and industry. Using social media analytics is fundamental because it helps you assess what is or isn’t working. What kind of analytics should you use then?

Quantitative analytics helps you to understand various key performance metrics. These are, for example, reach, engagement and share-of-voice. Qualitative analytics helps you to understand what people feel and how they speak about your brand. Both analytics are meaningful and can support you in making better decisions. Quantitative analytics is easy to utilise but the information is plain and reduced. In order to master the social media game, you need to conduct a qualitative analysis.

Qualitative social media analysis has three major challenges:

Challenge 1: If your analytics are based on only textual data, you lose majority of the information already before the analysis. Take into account also natural language, context, images and videos.

Challenge 2: If your data is not filtered, it will include large amounts of irrelevant information such as advertisements, neutral conversations and spam. Always filter the data, or the insights and analysis will be false.

Challenge 3: If your data is interpreted by Artificial Intelligence only, the results are going to be inevitably false. Artificial Intelligence cannot interpret complex data such as natural language, context, links, emojis, images, GIFs, audio or video. 

To resolve these challenges, you need a combination of human analysts and Artificial Intelligence. When a computer cannot interpret a piece of content, for example, a sarcastic joke, a photograph or a video, it passes the content for a professional analyst. This way it is possible to analyse all social media data with 100% accuracy.

Screenshot 2019-02-27 at 17.24.13

Tags: Social Analytics, Sentiment Analysis, Social Listening, Social Media Monitoring, Social media analytics, qualitative analysis

Share This!

Recent Posts

Subscribe to Our Blog!