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78% of consumers have bailed on a transaction or not made an intended purchase because of a poor service experience. —American Express Survey, 2011 

It takes 12 positive experiences to make up for one unresolved negative experience. —“Understanding Customers” by Ruby Newell-Legner 

For every customer who bothers to complain, 26 other customers remain silent. —White House Office of Consumer Affairs 

Customer experience affects both top and bottom lines. According to Forrester Research, a modest shift in customer experience for a $10 billion company increases product sales by $64 million, reduces churn by $116 million, and improves revenue $103 million by word of mouth alone. 

In this brief, you will understand how to catch market trends early and act on them in time, by effectively analyzing what customers are saying. This entails: 

1. Adding social media to your list of monitored information sources (customer interactions, databases, tweets, support calls, etc.); 
2. Turning your business intelligence and numerical analyses into action by discovering root cause—answering the “why” something happens; 
3. Augmenting or supplanting broad mass-market analysis with deeper, focused analysis of customer behavior; 
4. Letting the words of your customers tell you what information to monitor and which product features matter most to them. 

Big data and machine learning technologies are enabling a new class of analytics that discover patterns in customer behavior to give you time to act before market disruption occurs—think Uber and taxis. 

Not so far in the future, using text analytics to mine the social web will be a business tool as common as a profit and loss statement. 

THE ANALYTICAL SHORTFALL 
Business analytics have come a long way. Mining structured data in databases, applications, and spreadsheets detects patterns and predicts performance. Mining unstructured data in documents, log files, multimedia, and social content determines reputation and makes recommendations. 

We know the who, what, where, and when for every data point, but we struggle with the “why” for three reasons, we’ll explore: 

1. We typically analyze numeric data separately from text. 
2. We incorrectly use mass market analysis when we should be focusing on deep analysis of individual customer data. 
3. We use pre-conceived ideas of what data to measure, rather than letting the words of customers tell us what is most important to them. 

Your proprietary customer data will have greater value if viewed in the context of the social web. By “social web” we mean Twitter, Facebook, LinkedIn, but also surveys, self-service data, and product reviews. The social web is the epicenter of customer thought and it has one salient characteristic: it is comprised of words, sentences, and paragraphs with little to no defined structure. Your key to unlocking that content is text analytics. 

Click here, to read the complete paper.

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