Can the combination of machine learning and adaptive research radically reshape mobile market research as we know it?
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Can the combination of machine learning and adaptive research radically reshape mobile market research as we know it?
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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.
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Can the combination of machine learning and adaptive research radically reshape mobile market research as we know it?
- 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? © 2018 Borderless Access 3 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? © 2018 Borderless Access 4 Email Invitations Avg LOI 22minutes Average LOI (Mins) 2014 2015 2016 2017 28 27 22 17
- 5. What is ‘Adaptive’? © 2018 Borderless Access 5 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 © 2018 Borderless Access 8 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? © 2018 Borderless Access 10 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 © 2018 Borderless Access 11 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 © 2018 Borderless Access 12 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 © 2018 Borderless Access 13 ? 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? © 2018 Borderless Access 14 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 © 2018 Borderless Access 16 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 © 2018 Borderless Access Dynamic Invites Learn from Behavior Q Blocks
- 18. Results – Field Metrics Comparison © 2018 Borderless Access 18 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 © 2018 Borderless Access 19 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 © 2018 Borderless Access 20 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 © 2018 Borderless Access 21 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 Connect with us on Jason TifferPrasanth Reddy