Learn practical techniques to improve research efficiency and effectivity to ensure your efforts truly pay off.
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Course overview
This course is designed to transform your research habits, enhancing research quality and impact without increasing time and effort. Ultimately it helps you deliver a better return on investment for customer research.
Efficiency is essential for achieving a higher ROI in research, as it goes beyond mere speed. True efficiency combines speed with quality and impact, ensuring that research efforts are both timely and meaningful. By optimizing each stage of research process with an Efficient Research Framework, you'll gather valuable insights faster, without compromising depth of understanding, which leads to actionable outcomes and informed decisions.
Thus, efficient research directly supports business goals and maximizes the return on every research dollar spent.
In this course, I’ll share 160 proven techniques from my 10 years of experience in both academia and UX research to elevate your research efficiency.
If you're looking to improve research quality and impact without increasing time cost, this course will transform your approach in a self-paced manner.
This is an asynchronous course designed to overcome time zone differences. There are no live sessions. Lessons are delivered through pre-recorded videos (with AI voiceover). Each chapter includes two assignments, which you’ll complete and submit. We’ll then discuss your work asynchronously through in-system chat.
Check the syllabus below for more details.
01
People with 1-5 years of research experience looking to refine their skills and advance their careers.
02
People striving to balance speed, impact, and quality in their research projects.
03
Professionals aiming to train their teams to improve efficiency and maximize the ROI of customer research.
Gain a clear understanding of the principles behind the ROI for customer research
You'll walk away with a practical framework for immediate use.
Transform your research habits for greater efficiency
Accelerate each stage of the research workflow, from planning to reporting, to reduce time while enhancing quality and impact.
Manage stakeholders smarter and quicker
Implement smart tactics to enhance stakeholder alignment success while minimizing your time and effort investment.
Efficient synthesis - Less time, greater quality and impact
Build your skills to efficiently discover key findings from complex data. Engage in hands-on practice to generate high-impact insights through effective data analysis.
Implement effective communication throughout the research process
Master the art of quickly and effectively communicating research findings to facilitate decision-making.
Take-away templates
This course offers you a diverse range of templates to guide you through future research activities.

Live sessions
Learn directly from Weidan Li, Ph.D in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
66 lessons • 13 projects
Efficient Research Framework: Speed, Quality and Impact
Feel free to reach out to me if you have any questions!
Anticipation: Identify potential barriers ahead of time during research planning
Time allocation: Small time slots, not big time blocks
Prioritization: Begin by planning the most important sections first
Collaboration: Work with stakeholders in every step of research planning
AI Assistance: AI’s a supportive tool to enhance writing
Depth of understanding: Gain sufficient knowledge of the research context
Justification of methodology: Clearly explain how the research adds value
Business actions: Create inspiration with a compelling research plan
Individual engagement: Good questions and hypotheses keep people interested
[Template] Research Planning
[Template] Stakeholder Interviewing
Assignment 1
Assignment 2
Anticipation: Foresee future scenarios in later research stages
Time allocation: Use time blocks to maintain focus and stay in the zone
Prioritization: Start with the most essential and impactful questions
Collaboration: Keep reviewing as a team
AI Assistance: Use AI to refine and simplify questions
Depth of understanding: It's from the depth of exploratory questions
Justification of methodology: The rationale behind your interview or survey
Business actions: Drive purposeful actions post-research
Individual engagement: Immersion in the journey of designing questions
[Template 3] Interview Discussion Guide
Assignment 1
Assignment 2
Anticipation: Challenges in recruiting the right participants
Time allocation: Use different time blocks for different recruitment activities
Prioritization: Multiple ways of prioritization for efficient recruitment
Collaboration: Your team is as eager as you are to know the research target
AI Assistance: You still need manual selection for qualitative research
Depth of understanding: Understand both your participants and sampling strategy
Justification of methodology: Rationalize sampling strategy
Business actions: Identify and explore those "VIP" cohorts
Individual engagement: Consistent updates
[Template 4] Recruitment and Sampling Strategy: Tiered Approach
[Template 5] Team Recruitment Planning
Assignment 1
Assignment 2
Anticipation: Potential challenges in analysis and communication
Time allocation: Time management affects your data quality
Prioritization: Quick prioritization is key to effective communication
Collaboration: Data collection can be collaborative too
AI Assistance:AI's capability to collect quality data is still unclear
Depth of understanding: Don’t just 'collect' data, try to understand it
Justification of methodology: Justifying while collecting data
Business actions: Data collection reveals potential actions
Individual engagement: Greater engagement leads to a bigger data impact
[Template 6] Bias Reflection Checklist
[Template 7] Quick Data Review & Debrief
Assignment 1
Assignment 2
Anticipation: Foresee patterns and future report
Time allocation: Allocate time well to obtain both speed and quality
Prioritization: Prioritization starts before reporting
Collaboration: Collaborate to some extent
AI Assistance: Analysis is driven by your curiosity instead of any AI tools
Depth of understanding: Understand what data means, not just what it says
Justification of methodology: Bring stakeholders to your approaches of analysis
Business actions: Actions don’t naturally emerge during analysis
Individual engagement: Encourage stakeholder engagement through reflection
[Template 8] Insights Prioritization
[Template 9] Team Collaboration on Data Analysis and Thematic Review
Assignment 1
Assignment 2
Anticipation: Pause briefly before starting the report
Time allocation: Start by using time to write more, not better
Prioritization: Highlight the important information and show them early
Collaboration: Less applicable to report writing but...
AI Assistance: ChatGPT is our second brain for writing
Depth of understanding: Seek for meaningful insights
Justification of methodology: An insight is not just a title
Business actions: The last chance to inspire actions
Individual engagement: Thinking, writing and asking questions
[Template 10] Report Structure
[Template 11] Question Preparation for Research Presentation
Assignment 1
Assignment 2
Weidan believes that the more efficiently we conduct research and share insights, the greater the impact.
Because everyone’s curiosity has an "expiry date".
Over the past 10 years of his research career, Weidan has led over 70 UX research projects, designed and facilitated hundreds of surveys and interviews, and coached numerous cross-functional teams on conducting user research efficiently and effectively.
Weidan’s coaching approach is deeply influenced by his experience with Google rapid research. Now, he is keen to share what he has learned and practiced with the broader community. Before entering the UX industry, Weidan was an academic researcher specializing in "user experience and social theories".