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The Impact of “Big Data” on Voice of Customer

With the growth of digital transactions, more consumer data is becoming available through smartphones, GPS, mobile banking, etc. The availability of “big data” means marketers and customer experience professionals have the potential to gain deeper insight into customer behavior.

For Voice of Customer data, the combination of data mining and text analytics provides the best analysis method. Text analytics turns comments on social media or the web into structured data that can be analyzed. Data mining can be applied to uncover the hidden value of the information or link it to other sources to compare trends and relationships.

Ideally, the variables culled from text analytics are used alongside structured and transactional data from many other databases (such as customer satisfaction scores, geographic data, demographics, purchase and usage histories, product-feature data, etc.)

Through data mining, we can identify and refine patterns and trends among the hundreds, even thousands of variables that often come with “big data.” We can then make predictions based on information obtained from analyzing and exploring this data.

For example, a successful customer experience software company gleaned some surprising insights after analyzing text and data from multiple sources, including NPS scores, structured survey data, demographics, customer experience data and freeform answers to open-ended survey questions. After completing the text analytics phase, all the data were merged into a data mining model. In addition to the identification of highly concentrated subgroups of customers, the company discovered:

  • Those customers who mentioned “feature set” within their freeform answers were six times more likely to be a “promoter” of the product and company.
  • Those customers who made a negative comment about “reliability” were most likely to be non-promoters.
  • Of the variables found to be most predictive of the NPS score, the least important predictor was the number of times a customer had used the software in the previous three months.

In short, text analytics/data mining solutions help us connect “big data” to business practices and obtain insights that will make a difference. The results are insights that can be acted upon to achieve specific business goals in the areas of operational efficiency, customer engagement, product innovation and more.

Al Nevarez is Vice President of Product Management for Allegiance.