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Don’t kill the analyst just yet – the new world of text analytics

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Jean-Francois Damais
Global director and text analytics lead, Ipsos Loyalty

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Don’t kill the analyst just yet – the new world of text analytics

  1. 1. Accelerating Insight Generation | November 2016 | Presentation | © 2016 Ipsos. All rights reserved. Contains Ipsos' Confidential and Proprietary information and may not be disclosed or reproduced without the prior written consent of Ipsos. 1 Ipsos Loyalty November 2016 Jean-Francois Damais with Text Analytics Accelerating insight generation © 2016 Ipsos. All rights reserved. Contains Ipsos' Confidential and Proprietary information and may not be disclosed or reproduced without the prior written consent of Ipsos.
  2. 2. 2Accelerating Insight Generation | November 2016 | Presentation | In a nutshell… Text analytics is a method for extracting usable knowledge from unstructured text data through identification of core concepts, sentiments, and trends, and then using this knowledge to support decision making
  3. 3. 3Accelerating Insight Generation | November 2016 | Presentation |  More and more unstructured data  Increasing number of channels of interaction  Shorter MR questionnaires  Faster decision making process Big data or big headache? The new normal
  4. 4. 4Accelerating Insight Generation | November 2016 | Presentation | A well-balanced research ecosystem that facilitates data integration Analytical capabilities to make sense of that data and turn it into actionable insights in timely fashion. Organisations need:
  5. 5. 5Accelerating Insight Generation | November 2016 | Presentation | Accelerating the insight generation process across sources Text analytics Text analytics Complaints Survey/ EFM verbs Emails Qualitative data Web data Image/ Voice
  6. 6. 6Accelerating Insight Generation | November 2016 | Presentation | So, what’s the big deal? Cost Better insightsSpeed Consistency Scalability Data integration Response rates Engagement
  7. 7. 7Accelerating Insight Generation | November 2016 | Presentation |  Detecting new issues  Concept and mind clouds  Robust quantification of content into topics and sentiment  Text based driver analysis Explore and quantify Pattern detection Tracking and action planning1. 2.
  8. 8. 8 Case study Airline Merging the voices
  9. 9. Accelerating Insight Generation | November 2016 | Presentation | 9 Social listening Customer comment Staff comment Three data sources + One flexible text analytics model = One holistic feedback voice Opportunity: Global airline receives a lot of text feedback about the customer experience from three different sources. The background Solution: Using text analytics, data sources were merged to create a single holistic feedback and provide compelling evidence for change. Approach:
  10. 10. 10Accelerating Insight Generation | November 2016 | Presentation | WHAT people are saying We can identify 32% 26% 24% 18% 8% 5% 5% 5% 4% 2% 2% 1% 1% Flight experience Staff Flight punctuality Food and drink Non-flight experience Baggage Check-in Landing and arrivals Communication General satisfaction Product offering Boarding Entertainment • Customer comment: 500K verbatim • Staff comment: 10K verbatim • Social comment: 500K verbatim % MENTIONS BY TOPIC (Averaged by voice)
  11. 11. 11Accelerating Insight Generation | November 2016 | Presentation | And who is talking most about what Total Customer Social listening Staff Flight experience 32% Staff 26% Flight punctuality 24% Food and drink 18% Non-flight experience 8% Check-in 5% Baggage 5% Landing and arrivals 5% Communication 4% General satisfaction 2% Product offering 2% Boarding 1% Entertainment 1% • Customer comment: 500K verbatim • Staff comment: 10K verbatim • Social comment: 500K verbatim VOLUME OF COMMENT Dark colours indicate a higher volume of comment, lighter colours a lower volume
  12. 12. 12Accelerating Insight Generation | November 2016 | Presentation | We can set sentiment against volume StaffFlight punctuality Flight experience Food/drink Non-flight experience Check-in Boarding Baggage Entertainment Landing/ arrivals Product offering Communication General satisfaction Volume Priority improvement Priority maintenance - Sentiment +
  13. 13. 13Accelerating Insight Generation | November 2016 | Presentation | And split sentiment across the voices Flight experience Staff Flight punctuality Baggage Food and drink Customer Social listening Staff PositiveNegative Strength of sentiment is shown here not volume The bigger the bar upwards, the more positive it is; the bigger the bar downwards, the more negative it is (only top five categories shown) - +
  14. 14. Accelerating Insight Generation | November 2016 | Presentation | 14 WE FOUND AREAS OF Ipsos MORI – Your WSBL Only 4% of customers said the food was bad Only 1% online said the food was bad BUT 22% of staff think the food should be improved Base: 29,770 comments; 27,510 customer comments, 1,072 web comments, 1,188 staff comments DISSONANCE … … AND AREAS OF RESONANCE … 26% of customers mentioned delays 21% report delays online AND 17% of staff want to better manage disruption
  15. 15. 15Accelerating Insight Generation | November 2016 | Presentation | Text analytics allow organisations to explore: Who is talking What is being said The sentiment Action planning Resonance identification Understanding trend drivers + + + + + + And provide one voice of the customer experience =
  16. 16. 16 Case study Therapy Listening Understanding diabetes
  17. 17. 17Accelerating Insight Generation | November 2016 | Presentation | 17 Understanding what individuals are saying about diabetes online Focus on ‘new kid on the block’ – SGLT2 In-depth analysis from 390,000 posts on 19,000 topics, sourced from 3 diabetes patient forum websites in the UK and US. Goal: to derive insight from comments in order to: 1. Build a picture around SGLT2 2. Understand the impact of the disease 3. Identify unmet needs and life-style implications
  18. 18. 18Accelerating Insight Generation | November 2016 | Presentation | Analytical steps Starting with raw text divided into posts TRADITIONAL ANALYSIS (frequency/ crosstab tables/ multivariate), DATA VISUALIZATION Data Cleaning Grammatical Annotation Concept Extraction Topic Categorization & sentiment
  19. 19. 19Accelerating Insight Generation | November 2016 | Presentation | About the SGLT2 Class? What is Good & Bad Positives  Convenience  Weight loss  Effectiveness - patients share success stories  Low risk of hypoglycaemia  Positive blood pressure lowering effect Negatives  Side effects such as UTIs & increased urinary frequency  Limited long term safety data  Patients are aware that FDA warnings exist around ketoacidosis and CV risk  FDA required additional studies for liver toxicity
  20. 20. 20Accelerating Insight Generation | November 2016 | Presentation | Brand associations
  21. 21. 21Accelerating Insight Generation | November 2016 | Presentation | Patient Journey Developing a better understanding of impact of disease on lifestyle DIAGNOSIS LIFESTYLE CHANGES FIRST SYMPTOMS Nausea /dizziness Anxiety Patients recommended to: ►Monitor bg levels after meals each day ►Balance treatment with exercise and low carbohydrate diet ►Reduce weight, if necessary Potenital weight loss Social & Emotional support EMERGENCY ROOM VISIT Blood Glucose Test indicates possible diabetes diagnosis Shock & surprise of BG results HCP VISIT BG monitoring at home starts Blood Glucose/A1c test confirms diganosis Treatment Initiation Disappointment Understanding and acceptance DIAGNOSIS 4x daily BG monitoring around meal times Regular Exercise Adjust Diet Plan DIETICIAN HCP VISIT Possible Treatment Adjustments Social support DIAGNOSTIC AND MONITORING SYMPTOMS DIABETES MANAGEMENT STATE OF MIND
  22. 22. 22 Challenges Lessons learned
  23. 23. 23Accelerating Insight Generation | November 2016 | Presentation | What‘s not the big deal? Managing multiple languages Systematic errors Beware of the hype Rubbish in / Rubbish out
  24. 24. 24Accelerating Insight Generation | November 2016 | Presentation | The future is here! Voice Video Image Interactive survey branching Chat bots
  25. 25. Accelerating Insight Generation | November 2016 | Presentation | 25 “Don’t kill the analyst just yet”
  26. 26. Accelerating Insight Generation | November 2016 | Presentation | 26 Thank you. Deputy MD Global Client Solutions, Ipsos Loyalty Jean-Francois.Damais@ipsos.com Jean-Francois Damais

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