Voice of the Customer: Is Text Analytics Finally Growing Up?

Text analytics (also called unstructured data analysis) involves making sense of all those words written on blogs, review sites, and social networks. Text analytics or text mining was always the ugly stepchild of marketing analytics. Often reduced to simple categories or sentiment analysis as the amount of available data exploded with the advent of social networks and other sources of text data, text analytics suffered neglect. Voice of the customer insights from these programs support optimization.

Enter “Voice of the Customer” (VOC). Here’s what resulted from the Wharton School’s Customer Analytics Initiative:

Every year, American companies spend more than $10 billion dollars seeking to learn what consumers think about their products or how they rate against their competitors. The techniques they typically use have guided the field of market research for the last three decades – methods such as focus groups or detailed customer surveys. These practices still dominate even as thousands of the most engaged and avid customers – larger than a focus group by a factor of thousands – have been taking to the Internet daily in growing numbers for nearly 15 years to hold, free-ranging and unsolicited discussions of what they like about consumer brands, what they dislike, or to compare similar products.

So, if so much data is available, why don’t more firms close up their marketing research efforts in favor of this richer pool of data? The answer, according to a report by Dr. Netzer published by MSI (Marketing Science Institute) lies in the complexity associated with teasing out insights from this voice of the customer:

Consumer-generated content on the Web is both a blessing and a curse.” The sheer size of the data pool can be overwhelming, and the freewheeling nature of the consumer chat raises problems with spelling and grammar, in addition to interpretation.

This is why firms reduce the complexity (and obscure interpretation) by placing consumer-generated content into general buckets or categorizing utterances as positive, negative, or neutral to score consumer sentiment.

Interpreting the voice of the customer

Increasingly, efforts to glean more nuanced insights involve collaborations between marketing practitioners and academics, and computer scientists. That’s because interpreting text material requires automatically scraping relevant conversations, then determining WHAT attributes consumers mention and HOW they frame their conversation about these attributes. Bradlow and Lee, both professors at Wharton, developed such a tool and found it much better at providing insights than traditional market research methods such as conjoint analysis. It remains to be seen whether the tool works equally well for other types of text analysis and whether it can work in commercial applications of text analysis.

Del Moro points to 5 important characteristics of a successful voice of the customer programs.

Listening

Many tools toute their listening ability when, in fact, they only HEAR what consumers say. Listening involves gathering insights from customer-generated content, then using those insights strategically to improve firm offerings.

For instance, Mark Eduljee, who heads social listening at Microsoft (and is my co-author on our upcoming social media analytics book) talks about problems customers voiced after the introduction of the Surface computer. Within days, Mark heard their conversations, identified issues garnering the most criticism, and sent the information to the design team responsible for improving the system.

Separate actionable insights from noise

Text data is everywhere — in blogs, on review sites, and on social networks like Twitter and Facebook, in chat rooms and forums, etc. Thus, an automated process is needed to decrease the noisiness of the digital environment. IBM, SAS, SAP,  and Attensity offer excellent solutions designed for the voice of the customer analysis. There are even some free text analytics solutions including 1 recently released by Stanford, which unfortunately classifies voice of the customer data rather than aiding interpretation.

Develop integrated solutions

Integrated solutions for the voice of the customer capture data across multiple sources including customer support conversations by phone and email and mail.

Make it mobile

Customers increasingly rely on mobile devices so your voice of the customer program must include unobtrusive measures of these mobile conversations — through apps, for instance.

Voice of the customer as part of your business process

Voice of the customer can’t be a stand-alone solution. You need to integrate it into other business processes, such as your sales and customer loyalty data. Moreover, the voice of the customer insights must inform ongoing strategy.

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