This document summarizes Elka Mao's research portfolio, which includes experience in user research methodologies, field research, and relevant industry exposure. It provides three case studies as examples of her work. The first case study describes segmentation research on mobile payment app users. The second outlines conjoint analysis research to determine optimal features for a video streaming service's VIP membership. The third presents development of a credit scoring model for rural lending in China. Across her portfolio, Elka applies mixed research methods and data analysis skills to provide user insights and solve business problems.

1. RESEARCH
PORTFOLIO
ELKA MAO

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

5. MOBILE PAY USER
SEGMENTATION
CASE STUDY 1

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?

7. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
Survey
▸ Mobile Pay Tracker (longitudinal quarterly survey)
▸ Sample: 2000 mobile pay users
▸ Online survey with screener
K-means Clustering
▸ 94 variables input
▸ Cluster plot
▸ Determining clusters

8. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
Shiny AppPlot each each variable and explore differences 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

10. VIDEO WEBSITE VIP
MEMBERSHIP DESIGN
CASE STUDY 2

11. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ Determine benefits included in the VIP membership package
▸ Determine pricing of VIP subscription

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?
1080P high
definition video
Ads-Free
VIP-only video
resources
Price: ¥20/month
720P definition
video
Ads-Free
VIP-only video
resources
Price: ¥15/month
1080P high
definition video
Ads-Free
Cross platform
display
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

13. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
37%
14%
5%
22%
21%
Ads-free VIP-only vedios VIP marker Price Video definition
Attribute Importance

14. Research Process
1
Project Goal
Methodology
Analysis
Insights
2
3
4
▸ Ads-free, access to high definition video are the most highly valued
features
▸ Come up with the VIP package that has the highest possible
market share

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

16. CREATING A CREDIT
SCORING MODEL FOR
RURAL CHINA
CASE STUDY 3

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%.

21. Data Visualization Project
‣ NYC Yellow Cab Analysis
‣ NYC Real Estate Price Prediction