Social listening has been the buzzword and Swiss army knife of digital marketing for quite a while now. You want to learn more about your consumers’ passions? Use social listening. Trying to identify the latest trend for your category? Figure this out by using social listening. Need to figure out what consumers really care about? Again, the answer can be found via social listening.
That’s all fine and good, but I believe we are dramatically exaggerating the value of social listening when it comes to its role in creating consumer insights. Don’t get me wrong, there is a tremendous amount of quantitative signals – such as likes and shares – we can extract from social conversations, in addition to the important work in the realm of influencer identification. However, social lacks the qualitative, in-depth insight that all this buzz leads us to believe is possible.
The reality is that most of the content of social conversations is actually just that: social chatter. And social chatter tends to result in a very low signal-to-noise ratio because of factors like the inherent limited length of posts or lack of rich, descriptive vocabulary. We recently analyzed 100,000 Tweets in a beauty category and found that overall, there was a very low signal-to-noise ratio. There was lots of noise, but not enough actual content with significant value.
For example, when we looked at Twitter and Instagram, 65 percent of the content was made up of interjections, prepositions, conjunctions, and pronouns, which can otherwise be categorized as noise. Meanwhile, only 35 percent was made up of nouns, verbs, adjectives, or adverbs – elements that contain the majority of the value. Social listening is still a great source for signal-based insights and testing, but when we are looking for deep consumer insights and opportunities, social networks are not always the right places for mining meaningful data.
Grammar Reminder Chart
So where do we go for valuable text-based consumer insights? This obviously depends on the types of brands and consumers we are trying to find insights for. In the case of beauty products, we have found that we get the best value by looking at the information contained in search, forums, ratings, and reviews.
Forums are a great source of insight because they contain a wealth of structured information in the typical question and answer format. They are tagged and dated, and they often contain scoring for the quality or usefulness of the responses. This type of rich, structured data allows for some great insights and provides a lot of clarity into urgency and frequency of common consumer questions and passions.
For example, we recently started planning the campaign for a cold-related over-the-counter product. In order to determine the right schedule and messaging sequencing, we used data from last year’s forum posts as an indicator of when consumers start thinking about preparing for the flu season. We then created a predictive communications calendar that addresses consumer concerns before they know they even have them.
Product Ratings and Reviews
Product ratings and reviews are another great source for consumer insight. When leveraging text analytics, we can easily extract common patterns like product issues, perceptions, problems, and feature requests.
As part of the beauty project, we discovered that a large segment of beauty consumers were less interested in the features we were promoting – product superiority, color selection, and so on – and had more interest in some of the less shiny product attributes like durability and ingredients. This allowed us to optimize our marketing messages and ad copy to drive higher engagement and ultimately greater ROI.
These are only a selected few examples. However, we have found that when trying to generate thorough research based on deep consumer insights and understanding, depending exclusively on social content is not enough. Be aware that social media is not the only platform that consumers are using to communicate with brands. Therefore, you may have to expand your search to other sources in order to obtain the rich collection of valuable data you seek.
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