7 NLP Must Havesfor Customer Feedback Analysis

Alyona Medelyanalyona@getthematic.com

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!

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

Alyona Medelyanalyona@getthematic.com

Need to make senseof customer comments?Get in touch!