text analytics

Financial technology has sparked a global wave of startups who are rethinking how people make and receive payments. These fintech providers have a unique challenge when it comes to satisfying their target audiences. Not only do these companies have to satisfy the consumers who send and receive money through their application, but they are at the mercy of retailers, payment networks and financial institutions that touch the transaction throughout the journey. These dynamics make it difficult for payment companies to make holistic improvements to the user experience. There are just too many factors that they cannot monitor, much less control.

Within this increasingly crowded field, customer experience is more important than ever as a means of differentiation. This is true for both emerging players (think Square, Venmo, or Paypal) as well as larger competitors (like ApplePay or Google Wallet). They need to make the process of sending or receiving money as simple as possible. Simple wins.

This has payment companies reassessing the massive amounts of data that flow through their systems. Many have developed algorithms to mine transaction and customer data, with the aim of better understanding user behavior. These algorithms use structured data – data that is easily classified and quantified. Far fewer analyze unstructured text data.

Fintech payment providers can use text analytics to identify deep customer insights. Their findings can inform product development, uncover customer pain points, and reveal ways to stand out.

Think about all that text-based data available from customers’ social media comments, postings on support forums, call center notes, chat sessions, complaints, and in-app feedback. Gartner is generally credited with this projection: 80% of all data is unstructured.

While analyzing a seemingly endless sea of data may seem daunting, with the right strategy and tools, it becomes simpler and well worth the effort.

Four powerful strategies for using unstructured data

  1. Improve customer experience and marketing effectiveness.
    Unstructured data, such as social media comments, helps gain insight into what consumers like and don’t like about the brand, products and service. By measuring and tracking sentiment, executives can then target what to fix, what features to offer, and which prospects to engage for the best results.
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  3. Reduce response time and improve issue resolution.
    By closely analyzing customer comments, the business can quickly identify pain points and spot emerging issues for taking targeted action to improve customer experience.
  4. Accelerate productivity and innovation.
    Many companies have more “big data” than they realize, yet to reap the business value it takes a strategic, systematic program to ensure staff can easily access, manipulate, and learn from the data. According to a study by the University of Texas, Fortune 1000 companies could gain $2 billion a year in employee productivity by increasing usability of their data by 10%. Companies can accelerate innovation by mining unstructured data to better understand customer preferences and discover unmet needs and market gaps, important prerequisites for differentiating the brand.
  5. Enhance fraud detection.
    With every new payment platform comes new opportunities for fraudsters. How do you tell the difference between fraudulent transactions and legitimate ones? Text analytics and predictive analytics can be combined to help identify patterns of fraud. With advanced analytics, legitimate transactions can be handled seamlessly, while suspicious items can be flagged for closer scrutiny.

The more unstructured data a company can collect, the greater the opportunities for the business. With the right approach, fintech payment providers can leverage data science and text analytics to transform their customer experience, reduce fraudulent transactions, and use analytics to make their business more successful.

Text Analytics World June 2016 - Steven Ramirez Program Chair and Keynote Speaker