What are the main challenges and limitations of social media analytics?

One of the biggest is data understanding. For example knowing what the labels mean with video data and consistency across platforms. Watched up to 25%, 50%, 75% which is available on many platforms doesn’t mean the same thing nor what you may think. On LinkedIn for example it means the audience watched in that quartile range. Meaning someone will be attributed to the 75% range but could have fast forwarded there. Which is a different metric to someone who watched 75% of video.

A key challenge in social media analytics is data quality. Social media data can be noisy, incomplete, or inaccurate due to factors like spam, bots, trolls, fake accounts, duplicate posts, or measurement errors. These can lead to misleading insights. Improving data quality requires thorough data cleaning, filtering, validation, and verification techniques, along with reliable and credible sources and tools.

Remember that just because the data exists, doesn’t mean it’s something that is important to you. Data can be a great reference, but you have to use some critical thinking when analyzing it. It’s not the end all and be all of social media, so don’t overthink it.

With recent advancements in technology and all data features. All social media platforms share tons of data with us. Are all these metrics relevant to us? Probably not. Identify the top metrics that are important for you and your business goals and focus on achieving those specific numbers.

One of the biggest challenges I found in a multi channel strategy, is that every network has their own dashboards and sometimes it’s not as easy as it sounds to feed them into one dashboard.

Social media analytics also grapple with the massive volume of data generated daily. With billions of active social media users worldwide, analysts must process and analyze vast amounts of diverse data, which can be overwhelming. Coping with this volume necessitates scalable, efficient data storage, processing, and analysis tools and techniques like data sampling, aggregation, and segmentation.

Managing the variety of data available on social media platforms presents another challenge. Social media data can include text, images, videos, audio, emojis, hashtags, links, etc., each carrying different meanings and messages. This requires appropriate data formats, standards, and models, as well as advanced techniques such as natural language processing, computer vision, and machine learning.

Lastly, respecting the ethics and privacy of collected and analyzed data is a crucial challenge. Social media data can contain sensitive user information. Analysts must comply with relevant laws and regulations, like the EU's General Data Protection Regulation (GDPR), and adhere to ethical principles such as transparency, consent, accountability, and fairness.

Having a deep understanding of the data the platforms display is critical when analyzing performance - and can be a huge challenge as things can change over time. For instance, when looking at paid campaigns in LinkedIn, how campaigns are optimized need to be taken into consideration. In engagement campaigns, engagements count as clicks so if I have 50 engagements = 50 clicks and CTR is based off of those numbers. If I'm running the same ads as click campaigns, because driving landing page traffic is a KPI, when analyzing the data you have to do some additional investigation to be able to compare apples to apples. This can be different across platforms so having true understanding of platform performance criteria is imperative.

I think it's also worth remembering that time plays a big part in analytics. I find that so many people fail to look at their social media analytics because they simply don't make time or they are not sure how to make changes based on the findings. That's why simple tools really help - there are a lot of different expensive providers out there - and all too many companies buy them and never use them. I find @shieldapp is a really useful tool for LinkedIn as it makes it all so simple to understand and action

Choosing appropriate metrics to measure sentiment is crucial. While the company may track metrics such as positive and negative sentiment scores, these metrics may need to capture the complexity of user opinions. Some posts may express neutral sentiments or offer constructive feedback, which requires a nuanced approach to measurement.

Data bias: Social media data can be biased, reflecting the opinions and interests of the users who are sharing it. This can make it difficult to draw accurate conclusions about the wider population.