Widening the use of unstructured data

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Analyzing text is like a treasure hunt. It is hard to tell Tuba1what you will end up with before you start digging and the things you find out can be quite unique, invaluable and in many cases full of surprises. It requires a good blend of instruments like business knowledge, language processing and advanced analytics capabilities. This variety and complexity reveals the hidden value for the organizations.

90 percent of the digital information is unstructured according to this IDC report. With the further development of the speech-to-text technologies, the actionable volumes of data will get even bigger.

The good news is, the growing diversity of the unstructured data triggers innovative and interesting use cases for business.

Discovering personality types

tuba2At SAS Global Forum in Las Vegas this year, one of the presentations was on the topic of discovering personality type through Text Mining from Deloitte & Touche LLP. They presented the usage of text mining to develop, attract and retain the right mix of consultants for their teams. Apparently career oriented people tend to be more neurotic than open and having a good mixture of personalities in your team makes success more likely.

Can Text Analytics help you to reveal what your customers really mean when they talk about your organization?

Net Promoter Score (NPS) has become one of the key metrics for the measure of the customer satisfaction. Your customer gives you a score out of 10 and then you know how happy that person is with your organization. However, it is always good to cross check whether what your customer says or thinks about you is in line with how he or she scores you. A number could easily be perceived differently by people with different perspectives.

In our analysis of NPS data with scores and also comments of the customers, we could see examples of comments like I cannot say that I am not pleased with the attitude of the lady at the branch but this was my third visit to make a simple money transfer. I will switch to another bank. They are much better at internet banking”. This feedback resulted in an NPS score of 8!? But is this really correct? Because of the nice lady serving? Or because the person was too polite to give a lower score?! Or maybe he or she just doesn’t care? Extracting the topics that describe the real experience of the customer could help to see the bigger picture which could look completely different.

More case studies on text analytics at A2015 in Rome

Text can help you in many different ways and I will be able to share some more customer stories next week at A2015 in Rome:

  • Royal Brompton & Harefield NHS Trust are using text analytics to support clinicians to discover unusual sequence clusters and future diagnosis of new cases.tuba3
  • A US regulating agency is analyzing documents from banks to detect the risk factors that would potentially impact the future trends of the economy.
  • The World Bank, which has one of world’s largest electronic libraries, is categorising thousands of documents in minutes.
  • A major insurer in the UK is integrating their claims advisory notes into their existing fraud models and improving the correct detection rate by 20% and decreasing false alerts by 60 percent.
  • Alberta Parks are analyzing customer feedback data to detect major topics and sentiments and prioritize actions to improve customer satisfaction.

I will also highlight some potential use cases on speech mining to:

  • Detect the sentiment journey of the customers on the phone and improve tuba4the scripts accordingly for call center agents.
  • Use police interview records for cross referencing.
  • Analyze real-time streams of trader conversations in capital markets to detect rogue trading.

Also, A2015 will host another presentation on text analytics from British Airways. They will share their journey and the very interesting outcomes of their project on the Classification of passenger complaints.

I think it is fair to say that with the improvement and penetration of the technology, the insight extracted from unstructured data gets more sophisticated and rewarding each year.

I am looking forward to Rome! And I cannot wait to hear more of these use cases in future especially on real-time text streaming and speech mining.

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About Author

Tuba Islam

Tuba Islam is an analytics expert in SAS UK, designing analytical solutions to solve complex business issues in creative and effective ways. She is specialised in predictive modelling, segmentation, text mining, forecasting and analytical life cycle management. Tuba has implemented many successful projects across different industries including rogue trader fraud detection, smart metering analytics, credit risk, churn prediction, rate making and demand forecasting. She has been at SAS for almost 10 years. Before joining SAS she worked in a research institute and developed projects on speech recognition and language identification. She has an engineering background and holds a master’s degree in digital signal processing.

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