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

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.

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


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


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:


Accelerating the insight generation process across sources

Text analytics

Text

analytics

Complaints

Survey/

EFM verbs

EmailsQualitative

data

Web dataImage/

Voice


So, what’s the big deal?

Cost

Better insightsSpeed

Consistency

Scalability

Data integration

Response rates

Engagement


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

Case study

Airline

Merging the voices

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:


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)


And who is talking most about what

Total CustomerSocial

listeningStaff

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


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

Vo

lum

e

Priority improvement

Priority maintenance

- Sentiment +


And split sentiment across the voices

Flight experience Staff Flight punctuality Baggage Food and drink

Customer Social listening Staff

Po

siti

veN

ega

tive

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)

-

+


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


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

Case study

Therapy Listening

Understanding diabetes


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


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


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


Brand associations


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 diagnosisShock & 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

Challenges

Lessons learned


What‘s not the big deal?

Managing multiple languages

Systematic errors

Beware of the hype

Rubbish in / Rubbish out


The future is here!

Voice

Video

Image

Interactive survey

branching

Chat bots


“Don’t kill the

analyst just yet”


Thank you.

Deputy MD Global Client

Solutions, Ipsos Loyalty

[email protected]

Jean-Francois Damais

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

Presentations & Public Speaking