There is a lot of debate about how to use social media content in market research and in which category it fits: Quantitative or Qualitative research?
The massive amount of content generated by social media and the proliferation of sentiment analysis tools attempting to quantify it, give the impression that it can be considered quantitative research. However, the issues of data accuracy and representativeness have not been solved yet.
Anybody who has done coding of open-ended questions in surveys knows how difficult this is. Finding categories to classify the answers is a very subjective process. To attain some level of objectivity and find categories or codes that accurately reflect the content, we often need several iterations and different coders to achieve consensus.
In social media, as in surveys, more often than not, people write incomplete and grammatically incorrect sentences. They also use irony, sarcasm or humor to convey their meaning.
Unfortunately, text analytic tools can’t interpret language nuances as well as a human coder. Progress in this field is coming rapidly, but we are not there yet.
To make things worse, it is even more difficult to discern what people are talking about when the often posted short messages are taken out of the context of a social media conversation. In these, there are no survey questions to filter the information by.
Sentiment analysis tools still need to be fed with codes and definitions of positive and negative content defined by someone in order to count and classify content.
Linked to data quality is the problem of representativeness of opinions and user segments. From customer satisfaction research, we know that people, who provide feedback, are either very happy or very unhappy with the issue they give feedback about. Those who are in the middle often don’t bother to comment.
The level of category involvement also affects representativeness. Not all products command enough attention to be a topic of conversation in social media unless there is something that surprises or annoys the public (e.g. banks imposing debit card fees).
Moreover, there is the issue of how we separate unpaid opinions from paid opinions, which are becoming more common.
Until we have better text analytic tools and ways to identify who is behind the opinions, we should consider social media as a tool in the qualitative research arsenal.
Among its applications, social media content can be used to explore:
- Likes and dislikes about a product, brand or company
- The language used to talk about needs, expectations, barriers
- Problems with products, customer services that need a quick response
- Topics of interest within a product category
- Perceptions about a brand and its competitors
Social media content can be a source of rich insights. However, we should be aware of its limitations and not equal access to large amounts of text to quantitative research.