7 nlp must haves for customer feedback analysis
TRANSCRIPT
quora.com/What-are-the-best-customer-feedback-analysis-tools
Current Customer Feedback Analysis suck because they focus on scores, not reasons!
consumers: scores > commentsbusinesses: comments > scores
How do customer insight professionals
use people’s comments?
Price increase
New product feature
Marketing campaign
What happened?
Comments = Reasons behind scores & richer insights
Comments = Answers to who should follow up
Comments = Answers to strategic questions
So, which functionality is crucial when you need to
understand customer comments?
Capture many ways people talk about the same thing
1
How many ways are there to complain about a wet delivered news paper?
paperpapers
newspapernews papernewspapersnews papers
wetdrippingsoakingsoakeddamp
drenched
+
Failure to capture dozens of ways issues can be expressed leads to misrepresentations and poor decisions
vs
Synonyms can be dataset-specific
Autocomplete can mess up the meaning of a word!
People typed “airpoint” but were auto-completed to “airport”!
One size will not fit all!The ideal solution should learn
data-set specific synonyms!
Capture positive & negative attributes separately
2
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!
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
The ideal solution shouldhandle negation!
Captureemerging themes3
✘ ✓
Supervised categorisation fails as customer comments change over time
54%Other
8%Other
The ideal solution should allow for themes to emerge from data,
instead of be pre-defined!
Link to originalfor verification & action
4
1. Pull out all comments on a specific theme 2. Verify 3. Action
Ensure transparencyand ability to edit5
rugby world cup soccer world cupfootball world cup
Two themes?
Or one theme?
Often there is no right or wrong. Themes must be customisable.
Work well on small dataset6
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
Example of a related dataset used to model specifics of word meanings
Provide actionable insight7
Immediatelyactionable theme
Repeatedbut has no meaning
Trivial,Already knew
Insightful,new knowledge
Aspect or generalcategory of business
Ideal output from NLP analysis
Most NLP Solutions
1h Prototypewith open-source tool
Suspected,Data verified
Price increase
New product feature
Marketing campaign
What happened?
✓
Themes changing over time explain the reasons behind drops!
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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
