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P 02 ta_in_uw_transformation_2017_06_13_v5

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Text Analytics can be fun, useful and distracting. It is not just about the tools, but about how to use tools to drive business outcome. In this deck, you will get a sneak peak into some uses of text analytics in Life Insurance Transformation

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P 02 ta_in_uw_transformation_2017_06_13_v5

  1. 1. Life Insurance Transformation (Using Text Analytics) Vishwa Kolla Head of Advanced Analytics, John Hancock Insurance
  2. 2. 2  Advanced Analytics CoE, Maturity Model  Customer Analytics (entire value chain)  Machine Learning  Scoring Engine  Optimization  Simulations  Forecasting & Time Series • 16+ Years • John Hancock Insurance • Deloitte Consulting (Industries – Insurance, Retail, Financial, Technology, Telecom, Healthcare, Data) • IBM • Sun Microsystems Business Analytical (Math, Stats) Technical (Programming) Expertise Experience Vishwa Kolla Head of Advanced Analytics John Hancock Insurance, Boston MBA Carnegie Mellon University MS University of Denver BS BITS Pilani, India
  3. 3. 3 Where How So What? Say we want to analyze what are folks about in various conferences …
  4. 4. 4 Where How So What? Unigram Unigram analysis is a stat. Be cautious – can lead to a wrong conclusion if close attention is not paid
  5. 5. 5 Where How So What? Unigram Bigram A bi-gram analysis seems more like in the right direction
  6. 6. 6 Where How So What? Unigram Trigram and aboveBigram A tri-gram analysis seems more like an over-kill for this particular problem.
  7. 7. 7 Prospect Acquire Nurture Always think simple in 3s. When thinking about adding value, we can broadly think of 3 areas.
  8. 8. 8 Prospect Acquire Nurture • Document Clustering / Classification • Word Cloud • Concept Extraction • NLP Social Listening When looking to see who to target, social listening seems to be a good direction
  9. 9. 9 Prospect Acquire Nurture • Info Retrieval / OCR • Document Clustering / Classification • Concept Extraction • NLP • Web Mining Information Organization When looking to improve operations, information extraction and structuring is more important
  10. 10. 10 Prospect Acquire Nurture • Document Clustering / Classification • Concept Extraction • NLP • Web Mining Information Understanding When looking to nurture existing relationships, text analytics of interactions help a ton
  11. 11. 11 Where How So What? Benchmark #PASANDIEGO #ODSC+TDWI+MLSUBMIT+DS AUSTIN An insight is more powerful when we benchmark
  12. 12. 12 #PASANDIEGO #DATA SCIENCE CON AUSTIN #ODSCEAST #TDWI #Machine Learning Summit Where How So What? Benchmark 1 vs. all and 1 vs. other
  13. 13. 13 https://www.youtube.com/watch?feature=player_embedded&v=XswX1TSQwfQ So many stories. IoT led disruption. Shared Value. Business Model Disruption. Breathe new life into Life Insurance
  14. 14. 14 • Where should I talk? • What topics should I talk about? • Who should I talk to? Word Cloud Classification Alignment Inventory EDA Relevance Monitor Influencer Pool Relevance Partner
  15. 15. 15 What are people talking about? Why do we have a spike in positive tweets? Has our NPS increased since we launched ? NPS 18.52%
  16. 16. • How do I extract value from Dark data? • What APSes are similar? • What is the insight? • Is there a misrep? • What does my customer want? • Is my customer a Net Promoter? • How can I nudge?
  17. 17. 17 • How do I extract value from Dark data? • What APSes are similar? • Is there a misrep? • What does my customer want? • Is my customer a Net Promoter? • How can I nudge?
  18. 18. 18 NLP Plotly Networx Dplyr TM LDA Zygnet Caret ReshapeSentR Shiny R App
  19. 19. Shiny R App Web scrapping Sentiment Analysis Word clouds Topic Modelling Word Frequency
  20. 20. 20 Where How So What? What is being talked about in each of the industries
  21. 21. Increased CSAT 21 Increased NPS Reduced Software Spending Reduced Services Spending Always quantify value
  22. 22. 22 Means to an End Can be very distracting Automate Mundane Tasks into Packages Text Analytics = more than R&D Use tools to your advantage
  23. 23. 23 Appendix
  24. 24. 24 vs An example of distraction
  25. 25. 25 Jai Singh Sr. Data Science Consultant Agota Sakalauskaite Data Science Co-op Always give credit where credit is due

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