Beyond Big Data: Big Text Analytics

Big Data is valuable for banks because it can identify trends, uncover emerging issues, and forecast business outcomes. Yet leading companies are thinking beyond Big Data, and using Big Text Analytics to create a more comprehensive, intelligent customer analysis. Businesses can enrich Big Data with Big Text to develop a better understanding of customers, and as a result, create better business outcomes.

What is Big Text Analytics?

Big Text is unstructured word-based data, and includes:

• Email and phone messages
• Online news and blogs
• Social media posts
• Call center notes and transcripts
• Surveys and feedback forms

Unstructured data is rich with potential insight, and companies collect a lot of this type of data. In fact, 80% of an organization’s data is unstructured, which makes manually sorting through volumes of text a daunting, inefficient task. Instead, companies use text analytics to mine their unstructured data, search for common themes and trends, and glean insights that can:

• Identify at-risk customers and ensure that complaints are routed to the right departments for timely resolution
• Uncover opportunities to improve business operations
• Measure feedback and reactions to new products and services to drive targeted improvements
• Identify customer experience pain points and product defects before they become PR nightmares

Text analytics benefit the whole business

Text analytics can be a powerful tool for departments and lines of business across the enterprise, including:

• Marketing – Voice of the Customer, social media analysis, churn analysis, market research, and survey analysis
• Business development and operations – competitive intelligence, document categorization, human resources, records retention
• Legal and compliance – fraud detection, risk analysis, e-discovery, warranty analysis, medical research
• Customer service – analysis of interactions and opinions to address product and service issues, improve quality, and manage the brand and its reputation

Text-based data often contains qualitative information that helps businesses understand the root causes of churn and attrition. Equipped with this insight, companies can more effectively respond to complaints and develop strategies to retain customers and attract new business.

For example, using a Voice of the Customer program, a top 5 bank analyzed its large collection of customer feedback to track customer pain points and identify emerging issues. The bank was able to build a course of action and engage C-level executives in the bank’s overall customer experience strategy, which resulted in increased customer retention and higher profits.

Social media analytics is another powerful tool that takes advantage of the unstructured data found on Facebook, Twitter, and other social networks. When Beyond the Arc analyzed social media comments about Bank of America, we discovered thousands of remarks about service breaks and purchase limit rumors that put the bank at risk of losing customers. If the bank had been closely monitoring and analyzing their social media data, they might have preempted an escalating negative rumor mill by providing effective communications to reassure customers before things spun out of control.

Getting the most from text analytics

To gain a more comprehensive picture of the customer experience, companies should leverage textual data from a wide range of sources. To do this most efficiently, many businesses choose to partner with a firm that specializes in text analytics. The business determines its objectives or the questions it wants to answer, and a data scientist creates algorithms and analytical models that identify meaningful trends related to those objectives or questions.

Text analytics can help you unlock the power in your data to:

• Find out what people are saying– scan text for names, places, dates, and other important words and phrases to learn what people are saying about your organization or your competitors.
• Identify themes and trends – group similar information or topics for nuanced segmentation or to spotlight emerging trends.
• Uncover opportunities for improving the customer lifecycle – Relationship mapping lets you connect events and entities to one another to find where and when you need to improve the customer experience.
• Understand public sentiment – with sentiment analysis you can link emotions implied in textual comments to outcomes.

When analyzing text data, it’s important to focus on the most significant patterns to reveal the bigger picture. Three key data points provide valuable business insights:

• A description of what happened
• The customer’s perception of what happened
• The business outcome

Even before you capture meaningful insights, you’ll want a smart strategy in place to make sure you can put them into action. For example, an e-commerce company can use sentiment analysis to gain insight into what online shoppers are feeling when they abandon carts. With an understanding of how emotions such as frustration, happiness, or anger influence customer behavior, the company can take steps to craft experiences to produce desired outcomes.

The takeaway

Big Data and Big Text are complementary strategies that can help companies improve customer experience, increase customer retention, and boost profitability. Emerging technology may eventually enable businesses to draw insights from non-textual data, such as video, images, and audio (speech), providing a wider array of data sources for highly nuanced customer insights. As the possibilities for Big Text expand, businesses will have even greater power to harness data and achieve key objectives.

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Steven is the CEO of Beyond the Arc. The agency helps companies solve key business problems and build stronger customer relationships — with customer experience strategy, data science and analytics, and effective communications.