Big Data

Better listeners get more accurate customer analytics

Find out how listening apps can help your organization collect analytics that really let you know what customers think.

Image: iStock/shironosov

Jill Lapore, a writer for The New Yorker and a professor of American History at Harvard University, recently reported during an interview on National Public Radio that polling may never have been less reliable or more influential than it is now.

This is an interesting observation since from the late 1990s to 2012, 1,200 polling organizations conducted nearly 37,000 polls by making more than three billion phone calls. "Most Americans refused to speak with them," said Lapore, who indicated that many US households now let calls ring through to the answering machine before deciding whether to answer them. Lapore also indicated that a majority of those participating in polls tended to be persons who were definitely going to vote, but who also tended to be more conservative.

SEE: Election Tech: How to make yourself a DIY data scientist

When companies try to use big data and analytics to determine customer sentiment about their products, the challenge is equally daunting. How do you really know what your customers are thinking if you can't be sure whether you are getting a truly representative sample?

"There are two types of listening applications that companies have used since the early 2000s," said Errol Apostolopoulos, senior vice president product at Crimson Hexagon, which assists companies in using insights derived from social data. "One communications method is to listen to the voice of the customer through call centers or surveys. The other popular mode of customer listening is through social media."

SEE: Sentiment analysis: Understanding customers who don't mean what they say

Apostolopoulos says that companies encounter challenges when it comes to deriving insights from this data, and that one of the problems that social media managers are still trying to get a handle on is how to harvest insights from customer sentiment that flows through social media channels. His company makes a business out of using a set of proprietary algorithms that address the variety of media channels and uses machine learning to assist companies in achieving meaningful insights into the sentiment of their customers.

"The first thing that we tell our clients is that to arrive at meaningful and constructive analytics about customers, they don't necessarily have to just look at social media — and they don't have to just limit the information that they derive from this customer sentiment analysis to marketing," said Apostolopoulos. Any form of structured or unstructured data can be aggregated into a data pool for purposes of analyzing customer sentiment.

And while marketing stands to gain the most immediate benefit from what is known about how customers feel about a company and its brands, there could also be other benefits, such as the HR department being able to spot someone who already has a favorable impression of the company and its products from what was said in social media. "An HR manager might ask himself or herself, what if I were able to recruit people who already have an affinity with the company or its products?" said Apostolopoulos. "Where there already is a common thread of affinity, the possibility of retaining a new hire for the long term might be likelier."

What does this mean for companies?

Listening to customer sentiment might not only be a 360-degree exercise in terms of culling data from a diversity of incoming data channels, but it may also be a 360-degree exercise when it comes to leveraging the insights gleaned from the data throughout the company so that everyone can listen and benefit.

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About Mary Shacklett

Mary E. Shacklett is president of Transworld Data, a technology research and market development firm. Prior to founding the company, Mary was Senior Vice President of Marketing and Technology at TCCU, Inc., a financial services firm; Vice President o...

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