Smartphones today have the power to enable its users to manage a wide variety of tasks within short durations. With a plethora of apps, services and communication channels continuously beaming data, an average smartphone user is bombarded with many tasks/messages in the form of emails, texts, app notifications, digital transactions etc. and many of these include feedback surveys. How wise is it to ask the same consumer to answer a market research questionnaire setting aside 10 or more minutes of his/her time at a stretch? Can't we do away with the static questionnaire approach and employ an adaptive style of questioning? This presentation explores the topic of adaptive questioning powered by machine learning and its impact on respondent engagement, field times and data quality.

1. 1© 2018 Borderless Access
Can the combination of machine learning and adaptive research
radically reshape mobile market research as we know it?

2. Mobile MR landscape today
© 2018 Borderless Access 2
40%
Smartphone
Penetration
by 2020
50% Survey
emails are
opened on
Smartphones
Dropout rate
TWICE on
Smartphones
Response Rate
18%
6%
0%
5%
10%
15%
20%
Mobile PC
Share of Completes
88%
12%
PC
MOBILE
23% Survey
responses from
Smartphones

3. Avg number of
taps per minute
- 18
Did we really adapt to mobile?
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Email
Invitations
Avg LOI
22minutes
Average LOI (Mins)
2014 2015 2016 2017
28
27
22
17

4. Avg number of
taps per minute
- 18
Did we really adapt to mobile?
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Email
Invitations
Avg LOI
22minutes
Average LOI (Mins)
2014 2015 2016 2017
28
27
22
17

5. What is ‘Adaptive’?
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Dynamic Decision making based on existing conditions and
past history driven by analyzing a large set of parameters.
What is landscape I am
operating in?
What are the expectations from
the consumer?
What is the nature of the
ecosystem?
What are the constraints
of the ecosystem?
What are the generic
characteristics?
What are the specific
characteristics?

6. © 2018 Borderless Access 6
Adaptive Example
Source/ Destination Locality
Public Transport Network
Traffic
Weather
Holidays
User
Location
Travel history
Average fare
Destination history
Driver
Location
Trip History
Home Location
Revenue per week
Rating
Driving Dynamics
Pricing
Offers
Rider-Driver Match making
Adaptive
Areas
Impact
65% reduction in wait times
Two-fold increase in avg revenue
per user
Rapid growth in locality coverage

7. © 2018 Borderless Access 7
Adaptive Example
Source/ Destination Locality
Public Transport Network
Traffic
Weather
Holidays
User
Location
Travel history
Average fare
Destination history
Driver
Location
Trip History
Home Location
Revenue per week
Rating
Driving Dynamics
Pricing
Offers
Rider-Driver Match making
Adaptive
Areas
Impact
65% reduction in wait times
Two-fold increase in avg revenue
per user
Rapid growth in locality coverage
Analyses 80+ data points before making a
match!!

8. Proximity
Road Conditions
Traffic Conditions
Speed control
Collision Avoidance
Lesser Stress for
Driver
Adaptive Example - Automotive
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Purely based on user
input
Simple convenience
of not using one foot
Adaptive Cruise Control
Normal Cruise
Control
Adaptive
Cruise
Control

9. Proximity
Road Conditions
Traffic Conditions
Speed control
Collision Avoidance
Lesser Stress for
Driver
Adaptive Example - Automotive
© 2018 Borderless Access 9
Purely based on user
input
Simple convenience
of not using one foot
Adaptive Cruise Control
Normal Cruise
Control
Adaptive
Cruise
Control
Proximity
Road Conditions
Traffic Conditions
Speed control
Collision Avoidance
Lesser Stress for
Driver
Purely based on user
input
Simple convenience
of not using one foot
Adaptive Cruise Control
Normal Cruise
Control
Adaptive
Cruise
Control
Precursor to fully
autonomous driving!

10. Are we ready to become as smart as Uber?
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Important questions for researchers
What impact will ML have on survey design?
Can we leverage the adaptive way in research?

11. Stats on traditional research
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17.9 19 20
2017.8
18.2
20 20
28 27
22 17
2014 2015 2016 2017
Avg LOI
NonMobile
Mobile
Observed LOI Trend
17%
15%
14%
10%
16%
17% 14% 14%
2014 2015 2016 2017
RR CR
RR & CR Trend - Online Research
17% 15%
14%
10%
15%
15%
14% 14%
2014 2015 2016 2017
RR CR
RR & CR Trend – Non-Mobile
26%
22%
26%
28%
1% 3%
7% 7%
2014 2015 2016 2017
RR CR
RR & CR Trend - Mobile

12. Mobile Field Stats – Splits by B2B & B2C
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26% 24% 25% 24%
2%
2%
3% 3%
2014 2015 2016 2017
RR CR
29 29
24 25
2014 2015 2016 2017
LOI Trend 4 years Trend graph of RR & CR for 4 years
14%
14% 23% 22%
2%
4% 7% 7%
2014 2015 2016 2017
RR CR
18
19
20
18
2014 2015 2016 2017
LOI Trend 4 years Trend graph of RR & CR for 4 years
B2C Surveys
B2B Surveys

13. Experimenting with machine learning & adaptive
questionnaire
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?
Machine Learning Adaptive Questioning

14. - Activity patterns
- Response patterns
- Avg session time
- Day of the week
- Time of the day
- Response History
Learning models
Where can ML come in?
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An average smartphone user uses 39 apps in a day!
Knowing your respondents is very important!
Respondent Behavior
Questionnaire
Blocks
QNR 1 QNR 2 QNR 3
QNR 4 QNR 5 QNR 6
QNR 1 QNR 2 QNR 3
QNR 4 QNR 5 QNR 6
QNR 1 QNR 2 QNR 3
QNR 4 QNR 5 QNR 6

15. © 2018 Borderless Access 15
Case Study

16. Comparative Study – Traditional Vs Adaptive Questioning
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Traditional Questionnaire Adaptive Questionnaire
LOI: 15 minutes LOI: Dynamic LOI
Markets
Mexico
Platform: Mobile
Study Type:
Brand Tacker
Waves: 2
Sample Size:
200 each wave

17. Research Methodology - Adaptive
17
Questionnaire Setup & Learning Sampling Invitations
Divide Questionnaire in to smaller
blocks
More context based
Nano Samples
Continuous Sampling
Use all channels on mobile
Adaptive Invites
3 tier data
collection
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Dynamic
Invites
Learn from
Behavior
Q Blocks

18. Results – Field Metrics Comparison
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Traditional
Questionnaire
14 minutes
Adaptive
Questionnaire
Better
engagement
30% cost
reduction
Avg LOI
10 days
Study TAT
6%
Quality rejects
$3 per complete
Cost of Incentives
20 minutes
Avg LOI
7 days
Study TAT
1%
Quality rejects
$2 per complete
Cost of Incentives
More LOI but
lesser TAT

19. Impact on Respondent Engagement
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Market Activity
Time Spent by
Respondents 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Market Activity
Time Spent by
Respondents
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours of the day
Traditional
Adaptive

20. Impact on Respondent Engagement
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Market Activity
Time Spent by
Respondents 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Market Activity
Time Spent by
Respondents
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours of the day
Traditional
Adaptive

21. Answers to the important questions
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Better respondent engagement
leading to reduction on TAT &
Cost
What impact will ML have on
survey design?
Yes! Changing the way we
write questionnaires and
bite-sizing surveys
Can we leverage the adaptive
way in research?
Yes! We have all the data.
Are we ready to become as
smart as Uber?

22. Answers to the important questions
© 2018 Borderless Access 22
Better respondent engagement
leading to reduction on TAT &
Cost
What impact will ML have on
survey design?
Yes! Changing the way we
write questionnaires and
bite-sizing surveys
Can we leverage the adaptive
way in research?
Yes! We have all the data.
Are we ready to become as
smart as Uber?

23. Closing Thoughts
23© 2018 Borderless Access
Leverage Behavioral
Data
Try Adaptive Invites
Nano Sampling

24. Thank You!
24
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Jason TifferPrasanth Reddy