Emotion Engineering: Root-Cause Analysis for Customer Satisfaction (Dan Somers, Warwick Analytics)

CX Emotion
CX EmotionCX Emotion
Emotion Engineering: Root cause analysis
for customer satisfaction
20 June 2019
The Problem
!
Source: SurveyMonkey; 5 ways to get the survey data you want
All ML Models are not created equally!
Keywords NLP/Basic ML Complex, Specific ML
milk soup@Tesco why have u added milk to a perfectly nice
soup???? Just to save a few pennies I am DISGUSTID.
Cheapskates u have really let me down.
@santanderukhelp cheers, thanks for withholding
my cheque for extra days without letting me know
you would. Love the clarity you have with your
bankers. Good to see I’m just a number and not
valued. I’m off.
clarity cheque
soup Ingredient change
very unhappy
lack of info withholding cheque
very unhappy churn
positive
positive
not picked up
wrong (sarcasm)
wrong (misattribution)
irrelevant
irrelevant
subtle, key intent
concepts, not keywords
100%
90%
80%
70%
60%
50%
40%
Accuracy
Optimized Learning: minimal effort, maximum performance
(represents amount of human training/classification)
Conventional ML
Optimized Learning
Case Study
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1. Topic model: increased accuracy with reduced ACW
All Level1 Cats
(Original)
All Level1 Cats
(New)
Incorrect and
“Bucket”
categories
AFTER
STRICTLY PRIVATE AND CONFIDENTIAL
BEFORE
2. Sentiment Model: understand how 100% of the conversations went
Sentiment at interaction Position (Actual) Sentiment at interaction Position (%)
STRICTLY PRIVATE AND CONFIDENTIAL
3. Emotional Intent Model: Actionable root causes
Percentages (of total conversations)
STRICTLY PRIVATE AND CONFIDENTIAL
Neutral Positive Negative Disappointed
No Intent
Resolved
Not Resolved
Clarification/Didn't Understand
Repeat Problem
Bad Previous Advice
Long Time Waiting
Last Agent Disappeared
Insecurities/Possible Error
Don't Recommend
Looking to Leave
Recommend
Broken Promise
Difficulty in Purchase
Difficulty in Self-Serve
Grand Total
anonymised
Non-FCR
Churn & Advocacy
Customer Effort
Resolution
Easy, rapid impact
KPI1
Initial
‘Booster Pack’
Phase II
CX Improvements
Phase III
CX Improvements
PrediCX software live
• 4 weeks
• Pick initial KPI(s)
• Quick wins/high impact
KPI2
Improvements: CSat 18%, AHT 8% … AHT 35%
Thank You!
How did we do?
1 of 11

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Emotion Engineering: Root-Cause Analysis for Customer Satisfaction (Dan Somers, Warwick Analytics)

  • 1. Emotion Engineering: Root cause analysis for customer satisfaction 20 June 2019
  • 3. ! Source: SurveyMonkey; 5 ways to get the survey data you want
  • 4. All ML Models are not created equally! Keywords NLP/Basic ML Complex, Specific ML milk soup@Tesco why have u added milk to a perfectly nice soup???? Just to save a few pennies I am DISGUSTID. Cheapskates u have really let me down. @santanderukhelp cheers, thanks for withholding my cheque for extra days without letting me know you would. Love the clarity you have with your bankers. Good to see I’m just a number and not valued. I’m off. clarity cheque soup Ingredient change very unhappy lack of info withholding cheque very unhappy churn positive positive not picked up wrong (sarcasm) wrong (misattribution) irrelevant irrelevant subtle, key intent concepts, not keywords
  • 5. 100% 90% 80% 70% 60% 50% 40% Accuracy Optimized Learning: minimal effort, maximum performance (represents amount of human training/classification) Conventional ML Optimized Learning
  • 7. 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 1. Topic model: increased accuracy with reduced ACW All Level1 Cats (Original) All Level1 Cats (New) Incorrect and “Bucket” categories AFTER STRICTLY PRIVATE AND CONFIDENTIAL BEFORE
  • 8. 2. Sentiment Model: understand how 100% of the conversations went Sentiment at interaction Position (Actual) Sentiment at interaction Position (%) STRICTLY PRIVATE AND CONFIDENTIAL
  • 9. 3. Emotional Intent Model: Actionable root causes Percentages (of total conversations) STRICTLY PRIVATE AND CONFIDENTIAL Neutral Positive Negative Disappointed No Intent Resolved Not Resolved Clarification/Didn't Understand Repeat Problem Bad Previous Advice Long Time Waiting Last Agent Disappeared Insecurities/Possible Error Don't Recommend Looking to Leave Recommend Broken Promise Difficulty in Purchase Difficulty in Self-Serve Grand Total anonymised Non-FCR Churn & Advocacy Customer Effort Resolution
  • 10. Easy, rapid impact KPI1 Initial ‘Booster Pack’ Phase II CX Improvements Phase III CX Improvements PrediCX software live • 4 weeks • Pick initial KPI(s) • Quick wins/high impact KPI2 Improvements: CSat 18%, AHT 8% … AHT 35%