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7 NLP Must Haves for Customer Feedback Analysis


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The best way of improving customer experience is by listening to customer feedback. But reading customer comments, such as complaints, answers to open-ended survey questions, and forum posts, is prone to bias. NLP, and text analytics in particular, can automate the analysis of customer comments. But how does one choose the right approach?

Published in: Data & Analytics

7 NLP Must Haves for Customer Feedback Analysis

  1. 1. 7 NLP Must Haves for Customer Feedback Analysis Alyona Medelyan
  2. 2. Current Customer Feedback Analysis suck because they focus on scores, not reasons!
  3. 3. consumers: scores > comments businesses: comments > scores
  4. 4. How do customer insight professionals use people’s comments?
  5. 5. Price increase New product feature Marketing campaign What happened? Comments = Reasons behind scores & richer insights
  6. 6. Comments = Answers to who should follow up
  7. 7. Comments = Answers to strategic questions
  8. 8. So, which functionality is crucial when you need to understand customer comments?
  9. 9. Capture many ways people talk about the same thing 1
  10. 10. How many ways are there to complain about a wet delivered news paper?
  11. 11. paper papers newspaper news paper newspapers news papers wet dripping soaking soaked damp drenched + Failure to capture dozens of ways issues can be expressed leads to misrepresentations and poor decisions
  12. 12. vs Synonyms can be dataset-specific Autocomplete can mess up the meaning of a word! People typed “airpoint” but were auto-completed to “airport”!
  13. 13. One size will not fit all! The ideal solution should learn data-set specific synonyms!
  14. 14. Capture positive & negative attributes separately2
  15. 15. teaching not helpful teachers bad learning style good learning stylehelpful teachers The lecturers aren’t particularly helpful and the learning style is far from perfect. I have always found the lecturers to be very helpful and the learning style is perfect. Same nouns & adjectives, but different feedback!
  16. 16. Purposes of Negation • Reversing polarity I did not like the learning style → dislike it • Emphasising negativeness or positiveness There is nothing I did not like about the learning style → love it • Make weaker claims The learning style is not bad → it’s ok
  17. 17. The ideal solution should handle negation!
  18. 18. Capture emerging themes3
  19. 19. ✘ ✓ Supervised categorisation fails as customer comments change over time 54% Other 8% Other
  20. 20. The ideal solution should allow for themes to emerge from data, instead of be pre-defined!
  21. 21. Link to original for verification & action4
  22. 22. 1. Pull out all comments on a specific theme 2. Verify 3. Action
  23. 23. Ensure transparency and ability to edit5
  24. 24. rugby world cup soccer world cupfootball world cup Two themes? Or one theme? Often there is no right or wrong. Themes must be customisable.
  25. 25. Work well on small dataset6
  26. 26. How can an NLP solution work on a small dataset? • Industry-specific dictionaries & rules But: How to avoid ambiguity errors? • Pre-defined static categories But: How to capture emerging themes? • Creative data gathering • Re-purpose survey data from related companies • Re-purpose company-own resources
  27. 27. Example of a related dataset used to model specifics of word meanings
  28. 28. Provide actionable insight7
  29. 29. Immediately actionable theme Repeated but has no meaning Trivial, Already knew Insightful, new knowledge Aspect or general category of business Ideal output from NLP analysis Most NLP Solutions 1h Prototype with open-source tool Suspected, Data verified
  30. 30. Price increase New product feature Marketing campaign What happened? ✓ Themes changing over time explain the reasons behind drops!
  31. 31. 1 2 3 4 5 6 7 Capture ways people talk about the same thing Capture positive & negative attributes separately Capture emerging themes Link to original for verification & action Ensure transparency and ability to edit Work well on small datasets Provide actionable insights
  32. 32. Alyona Medelyan Need to make sense of customer comments? Get in touch!