Companies and brands have traditionally relied upon consumer surveys, focus groups, and research reports to figure out what people think of their products or services. Social listening provides an alternative that is enabling brands to tap into much richer consumer insights that are generated in real time.
Every day, hundreds of millions of people talk publicly on social media about where they have been, what they have bought, and their good and bad experiences. Or they just express their feelings and opinions. This information is a gold mine for consumer-facing industries, including retail, consumer goods, retail banking, insurance, and healthcare.
Recently, advances in machine learning have made smart analysis of natural language content possible, as well as the monitoring of pictures and videos. In our experience, the new techniques have been very effective in enabling companies to investigate a wider range of consumers’ feelings and follow different aspects of their lives. We have seen how their preferences can be mapped and updated immediately, as can the ways in which customers connect with and influence each other.
We are only beginning to scratch the surface of how these machine learning advances will enable social listening to eventually rewrite the rules for consumer product companies. But already, we are seeing social listening overturn how consumer product companies develop, market, and package their products.
Social listening can uncover hidden links between consumer markets that are heavily influenced by others, as consumers take cultural or fashion cues from places they feel an affinity for. Analysis of social media posts, for example, shows that the Indonesian cosmetics market is now more heavily influenced by trends in Thailand than from France or Japan. It also points to the products and brands concerned, as well as the reasons for the link. For example, one Instagram post about a face powder read: “Best seller in Thailand! Super fit for my skin colour.” Another referred to a product’s sun protection factor.
These comments indicate that Indonesian users of cosmetics see similarities with Thailand in climate and people’s skin colour, and that they feel that products popular in Thailand will be right for them. Makeup manufacturers might therefore want to consider taking products that work in Thailand and launching them in Indonesia, perhaps also using their popularity in Thailand as part of an Indonesian marketing campaign. Indeed, in makeup our work suggests that product manufacturers could increase their return on investment in Indonesia by up to 20% by identifying and partnering with the most-promising brands in Thailand.
We have seen how comments on social media can also help develop new versions of products. For example, a manufacturer can often double its rate of hit products from, say, one in 10 to one in five with better consumer information, halving the cost of developing also-ran products. Social listening thus brings manufacturers closer to consumers, enabling them to make customized, small-lot products for specific groups.
Analysis of social media interactions is also making tribal marketing much easier and more precise, by locating groups with tastes and needs in common on, say, Twitter or Weibo, its Chinese equivalent. By tracking and measuring the connections between a group of new mothers, for example, marketers can map their tribes and identify leaders – the members who are best-connected with the others. These will likely have significant influence on the tribe’s preferences in baby products and other categories they might be drawn to.
Consumers often talk on social media about their interactions and experiences with brands, products, and services – such as how bad the packaging is or how poor the delivery was. However, it is tricky to turn such comments into actions. Simple aggregations of the appearances of key words, such as “packaging,” are not useful, since they don’t indicate whether the specific complaint is leakage or unclear instructions on a bottle.
But techniques such as natural language processing, machine learning, and image recognition are now helping to identify more precisely what is being said. They can then suggest the right actions. For example, if one social media post complains that a bottle is hard to carry because it is large and made of glass, that information could prompt a manufacturer to study the potential of smaller bottles made of different materials.
Though social listening techniques have been around for over 10 years, very few companies are making the most of them. That’s not because of a lack of material to work with. Instead, the main barrier to making social listening useful has been the quality of the data. This requires natural language recognition in different languages and advanced techniques to analyse social media accounts and their contents automatically and effectively.
Soon, however, these techniques will be common in consumer-facing industries, and those companies that neglect them will find it hard to catch up. By contrast, product manufacturers that learn how to understand consumers in actionable ways – and to test their reactions in real time – will have a huge advantage.
