Developing Customer Insights Through Social Listening

Every business wants an answer to certain questions:

• What do people say about our product?
• Who is talking about – and who likes or dislikes – our product?

However, businesses have a few options for how to develop insight into their customers. They could run focus groups, but those are logistically difficult, expensive, and the data is inaccurate because the study subjects are psychologically “primed”.

They could dig into owned data they have on their current customers, which is a great idea. But this can only go so far: what about customers who chose not to purchase? Or who switched products?

The Social Web and Its Vast Data

There is a world of customer data out there, ready to be used to drive real business results. This is the social web (or web 2.0, or more commonly: “The Internet”).

Ever since the dawn of social networks, forums, blogs, comments, and all such 2-way conversation channels, people have been talking about what they do, like, eat, wear, hate, love, think, and feel. This massive set of unstructured data can actually be mined and analyzed. More often than that, the results produced can be used to define new assumptions, or modify existing ones.

Social Listening Provides Quick, Inexpensive and Accurate Customer Intelligence

Unlike its more traditional counterparts such as focus groups, surveys, and 1st party data analysis, social listening insights can be generated in a matter of hours. Some tools have the data stored and ready to be accessed within seconds.

And when you compare the aggregate costs for focus groups with enterprise-level social listening technology, the $3000-$10000 monthly fees for most social listening tools begin to look inexpensive.

Finally, the people you’re monitoring don’t know you’re listening (which would only be creepy if the data wasn’t as well-regulated and often anonymized as it is). This preserves the integrity of the results.

With the right Natural Language Processing (NLP) algorithms on top of the raw text data, businesses come away with all sorts of insights.

Telecom Example: How to use social listening tools to make business decisions

Let’s say, for example, that Verizon wants to learn about the perceptions of its brand vs. perceptions of its competitors’ brands. They are asking these questions:

• What do people think about Verizon and its products?
• Who is talking about (and who likes or dislikes) Verizon and its products?
• What do people think about AT&T, Sprint, T-Mobile and other competitors, and their products?
• Who is talking about (and who likes or dislikes) these competitors and their products?
• What do people think about mobile phones and carriers in general?
• Who is talking about (and who likes or dislikes) mobile phones and carriers in general?

With best-in-class social listening technology, Verizon would be able to answer all these questions in great detail. The results would include the following data sets:

What customers say:

• Positive and negative sentiment
• Emotions and thematic elements
• Major words in context (most mentioned topics)
• Strength of feeling (passion)

Who are the customers:

• Demographics (gender, age, financial demographics)
• Geographics (country, state/province, city)

This data would be available as trends over time or as aggregate numbers. For example, we could glean from the sentiment over time (past 3 years) that Verizon customers feel mostly positive about the brand. There are a couple hiccups, however, especially in June 2014, and a sustained period of increased negative sentiment in the Summer of 2013.