2. ▸ Systematic training in research
methodologies
▸ Rich field research experiences
▸ Relevant industry exposure
FOR A SATISFYING USER EXPERIENCE
YOU NEED SOMEONE WHO HAS
Y
I AM THAT SOMEONE
3. FOR A SATISFYING USER EXPERIENCE
YOU NEED SOMEONE WHO HAS
Y
▸ Systematic training in research
methodologies
▸ Rich field research experiences
▸ Relevant industry exposure
4. WHY USER RESEARCHER?
▸ Perfect combination of my interest (UX design), my trainings (quantitative methodology) and my
past experiences (research)
▸ Fan of designers
6. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ Understand mobile pay user’s payment behaviors
▸ Improve user experiences by determining different service needs
Research questions
▸ Is there significant segmentation among mobile pay users?
If so,
▸ How are users similar within each group?
▸ How are users different among groups?
9. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ There are two key drivers of the segmentation: age
(demographical) and activeness (behavioral)
▸ The most active mobile pay user groups are relatively younger,
are enthusiastic about new technologies and any “cool” stuffs
▸ The least active mobile pay users are relatively older, more
conscious of budget and put more emphasis on security
▸ Benefits such as the convenience of no need to carry physical
wallet are universally appreciated among groups
Using the same dataset —- NPS score exploration
12. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
Choice Based Conjoint Analysis
If you were considering subscribe to the iQIY VIP membership and these were the
only alternatives, which would you choose?
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definition video
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720P definition
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1080P high
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Price: ¥20/month
None: I wouldn’t
choose any of
these
SELECT SELECTSELECTSELECT
My role:
‣ Help with the design of conjoint survey
‣ Test logical consistency of the online survey
‣ Summarize and visualize the conjoint data
15. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
Quantitative
▸ Help with survey design and evaluation
▸ Input and clean survey data
▸ Create charts and graphs based on data analysis results
Qualitative
‣ Notetaker during workshops and interviews
‣ Make posters and props for workshops
‣ Conduct telephone screening for interviewees
‣ Collect and analyze informations from dairy studies
My other roles during internship in the market research company
17. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ Enabling rural development with easier access to financial support
▸ Creating a special credit scoring model that fits into the financial
context of rural China
▸ Improving the efficiency and accuracy of loan decision making
18. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
Filed Research
▸ Case study of a rural financial cooperative in Hebei, China
▸ In-depth interview with 9 rural credit loan officers
▸ Delphi method: invite officers to fill out a survey concerning the
relative importance of credit indicators
Literature Review
▸ Relationship lending and credit scoring
▸ Method of building credit scoring model
▸ Selection of indicators: 21 indicators within 4 groups: family
background, willingness to repay, ability to repay, relationship with
the rural financial institution
19. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ Step1: Construct decision hierarchy (based on group discussion and
in-depth interview)
▸ Step2: Construct pairwise comparison matrix
▸ Step 3: Weight and recalculate the matrix
▸ Step 4: Evaluate the weights by calculating the consistency ratio
Finally, use the calculated weights to construct a formula, i.e. the credit
scoring model.
Analytical Hierarchy Process
20. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ For rural loan officers, relative importance of credit indicators
when assessing a loan application is as follow:
Ability to repay > Willingness to repay > Relationship with the
cooperative > Family background
▸ Using real loan application records as testing data set, the credit
scoring model reaches prediction accuracy of 92%.