Charting Emotions

One of the emerging themes from our research is the notion of the “highly-instrumented” enterprise environment. Data is everywhere – new types of data that we didn’t previously have access to. You can think of this as a virtual layer of information that adds a new level of understanding (and complexity) to the physical world. Of particular interest to me is the notion of sentiment analysis, where companies can use tools from vendors like Attensity, Scout Labs, Radian6, and a variety of others to listen in on customer conversations and measure sentiment towards products, services, brands, and specific experiences. Companies can now analyze every tweet, blog post, and comment to know what customers are feeling. This is definitely cool technology.

What’s equally impressive is some of the display technology being developed to display this type of data. All vendors have some form of executive dashboard, but these are highly utilitarian. From what I have seen, the bar for sentiment visualization is being set by other innovative thinkers. For example, ongoing projects like We Feel Fine from Jonathan Harris and Sep Kamvar as well as Bio Mapping from Christian Nold aim to visualize emotional data in new, interesting, and useful ways. We Feel Fine is more of an art project than a rigorous sentiment analysis tool, but it provides a useful example for how we might organize, display, and search for comments. Users on We Feel Fine can search by emotion (key word only), gender, age, location, weather, and date. It also connects emotions to associated images that are found within the document.

Bio Mapping (shown below) uses a lie detector connected to a GPS unit to measure location and physiological arousal at the same time. This is then plotted using Google Maps and other visualization software. Note, in this case the sentiment metric is intensity of emotion, not the specific emotion itself. The spikes shown on the map are locations of interest, but that is all we can determine from the data.

Although most of the complex visualization technology is still nascent you can imagine where this is type of analysis is going. Companies could segment customers based on emotional response, plot the spread of viral buzz, identify ideal test markets, and optimize local campaigns based on near-time feedback loops. Employees could gain access to a new lens on customer activity, behaviour, and satisfaction in a user-friendly display that makes analytics fun. Large retailers could use similar mapping technology powered by emotion data to optimize store layout and measure display/product appeal. The biggest challenge to wide adoption of these types of tools is the lack of valid emotional data in significant volumes. Currently, mining user comments online is the best available data source, but some early research suggests promising breakthroughs in the area of voice analysis and facial recognition as well – stay tuned.

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Naumi Haque has more than a decade of experience in the research and advisory industry. Naumi has been at the forefront of customer experience management, recently arguing that enterprises need an integrated customer experience strategy to meet customer expectations. He has conducted research and provided thought leadership on a wide variety of topics related to emerging technology and business innovation, including: social media strategy, customer experience, next generation marketing, enterprise collaboration, open innovation, digital identity, new sources of enterprise data, and disruptive web-enabled business models. He received his MBA and his Honors in Business Administration from the University of Western Ontario’s Richard Ivey School of Business.