Reply to this post agree or disagree and explain why

Businesses get customer feedback through multiple channels both offline and online feedback. In fact, online feedback systems and platforms that, by design aggregate customer sentiment, are becoming dominant these days with social media platforms. Customers share their thoughts through Facebook likes, Twitter tweets, LinkedIn comments, Pinterest pins, and more.

The real challenge lays in aggregating usable data from these multiple sources, analyzing the data to identify problems, clustering similar problems together, and making them actionable for decision making or problem-solving. This process of converting unstructured voice of customer data into meaningful, coherent datasets to measure customer opinions, product reviews, feedback, or sentiment analysis, and/or subjecting the data to entity modeling in support of fact-based decision making is called Text Analytics or Text mining.

Hence, when dealing with the voice of customer data, details on customer journey problems are often detected through direct comments, open text responses to questions like What is the one thing that would have improved your visit today? When customers with similar specific problems (responses) are grouped together, product managers and development teams can use their relative volume to prioritize their repair backlog and release calendars.

These kinds of customer problems can be further analyzed, drilled down, classified, and ranked by their impact on businesses KPIs such as time to purchase, customer satisfaction, etc.

Obtaining the data can be a challenge since the data typically reside in source systems that were not created with analytics in mind electronic health records are a great example of this. Extracting the data can be a burden on the performance of the source system and can take a long time. Understanding the data can also be a challenge, since many interesting data sources are rife with jargon, abbreviations, and specialized language. You often need a subject matter expert to determine which terms are important, which are synonymous, and so on.