What are the limitations of relying on social listening tools?

Social listening tools are great for quickly gathering and making sense of the vast amounts of unstructured social data online. However, there are some dangers of relying too heavily on them, particularly when it comes to analysing the data they gather. Here are some of the major limitations of social listening tools that you have to consider:

Failure to consider context

Technology doesn’t always consider context. For example, a social listening tool may translate a high volume of mentions about a particular topic or brand as popular and therefore positive. But that’s not the case if those mentions are negative. Yes, many social listening tools include sentiment analysis. But, they aren’t sophisticated enough to be able to de-code the nuances of language as well as a human (yet, at least).

Similarly, the tool will not consider the cultural context of what is being said and therefore, may come up with a meaningless interpretation. You need a human analyst to interpret the results in the context they were meant – whether that is the cultural context or immediate context (i.e. what’s being said around the highlighted mention). For example, the word “thirsty” may not literally talk about someone who needs some hydration. And “fixin,” in the South of the US, doesn’t actually refer to repairing something.

Netnography is a vital solution to this challenge, allowing you to dive deeper into each conversation and interaction to find context. And in the case of global social listening projects, local analysts can provide you with much-needed cultural context.

Limited access to some platforms

With each platform having different privacy and data collection policies, social listening tools have limited access to data from some of them. Several major social networks also limit data sharing. So, for those platforms, the tools can only show you a sample of the data they collect. These results are based on an incomplete data set.

On top of this, there are a number of private chat channels such as Discord, Clubhouse, and WhatsApp, which social listening tools aren’t able to gather any data from. The only way to know what’s going on on these platforms is the old fashioned way: join them as an individual and observe.

Requires extensive cleanup

Whilst technology can quickly sort through lots of data to bring results on specific search queries, it’s not very precise. There’s still a lot of cleaning that is required before analysis can begin. There will be a lot of search results that are irrelevant because of the context or because words can have multiple meanings.

For example, someone talking about “apple” may be referring to the fruit; not the brand. Or searches for “Chelsea” the football club may bring up results for women named Chelsea. Without including exclusions (often multiple ones) within the search queries, social listening tools can’t differentiate between these different uses. This often results in a lot of noise in the search results.

Inability to interpret data into meaningful insights

On its own, data is just information. What’s important is how you interpret the information and turn it into insights you can use and action that has an impact. And social listening tools can’t do that for you.

The interpretation can be specific to your organisation’s unique needs and goals, with different departments using the same data set for completely different purposes. Social listening tools can provide the data, but each department will have to interpret it for their own needs.

Diving deeper into social listening data

With the ability to quickly gather and analyse millions of data from multiple sources, social listening tools are a helpful way to gain insight into your audience. But considering all these limitations, it’s also crucial that you leverage them in tandem with human analysis for a more comprehensive, in-depth interpretation of the data.

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