Webinar Summary

Introduction to the Workshop

In this session, Lynne Thomson shared practical strategies for planning research that drives results. From refining business questions to validating participants, she covered techniques to enhance data accuracy and credibility. Attendees left with actionable insights to strengthen their research foundation and stakeholder alignment.

1. Planning Research for Impactful Results

  • Begin by negotiating the right business question—not necessarily the first one presented.
  • Translate broad questions into specific, testable hypotheses (e.g., from 'How do we grow?' to 'Will users pay more for this feature?').
  • Ensure research outcomes are actionable, credible, and directly tied to business decisions.

2. Gathering Diverse Opinions for Hypothesis Testing

  • Collect input across teams and transform them into hypotheses.
  • Use multi-modal research—conversations, surveys, big data—to triangulate on insights.
  • Build credibility using customer quotes and AI-powered open-end coding.
  • Tailor communication to skeptical audiences like engineers or finance teams.

3. Machine Learning Universe and Data Challenges

  • Identify the universe you're studying—know your internal and external audience.
  • Validate your sample using known databases and external verification (e.g., social media profiles).
  • Avoid quantity over quality—talk to the right people, not just more people.

4. Validating Samples in Market Research

  • Screen out bots and dishonest participants with low-incidence 'yes/no' honesty checks.
  • Use Eggers’ Honesty Detector to assess data reliability.
  • Present data clearly to stakeholders who may be unfamiliar with research, especially engineering.

5. Synthetic Data Limitations in Research

  • Be cautious with synthetic data—especially for concept testing or qualitative depth.
  • While synthetic data may assist with brand tracking, it often lacks emotional nuance and can mislead.
  • Use big data and secondary research for context, not as a replacement for primary insights.

6. Identifying Actionable Business Questions

  • Align stakeholders by uncovering tension points and disagreements, they often reveal testable hypotheses.
  • Ensure questions are framed to produce clear, actionable insights—not just opinions.
  • Use honesty detection techniques to build stronger, more representative samples.

7. Maintaining Valid Samples for Research

  • Fresh eyes are crucial—early adopters may inflate satisfaction scores due to bias.
  • Account for attrition bias—those dissatisfied may drop off early.
  • AI tools can help screen and qualify participants more effectively.
  • Use personality archetypes to ensure the right audience fit.

8. Conclusion

  • Mark from Protocol Theory shared efforts to build a double-opted-in panel for crypto, web3, and tech audiences.
  • Emphasized the importance of audience relevance and niche community alignment.
  • Melissa, a veteran freelancer, highlighted evolving research methodologies and appreciated the workshop’s depth.

Mastering Research Planning: Test Your Knowledge

Test how well you understand the foundation of effective research planning!

Meet the Speaker

Lynne Thomson is a seasoned customer and user researcher with over a decade of experience helping organizations like Microsoft and Amazon plan and execute high-impact research. Her expertise lies in transforming vague business challenges into testable hypotheses, selecting the right methodologies, and validating research samples.

She currently leads research initiatives at Decoding AI and is passionate about teaching others how to plan research that drives real business value. Learn more about the work of Lynne Thomson.

Frequently asked questions

Are panel providers a reliable source for research participants?

It is important to be realistic while selecting panel providers. Higher quality panels usually come at a higher cost. Vetting panel providers thoroughly is paramount to ensure data integrity & quality insights.

Can AI be used to build synthetic research audiences?

There is a little skepticism about the current value of synthetic research for qualitative insights or new concept testing. However, there is potential in synthetic research for testing survey mechanics or question formats.

Why is it important to involve 'fresh eyes' in research?

It is important to bring in new participants, especially when expanding from early adopters to a mainstream audience. Fresh perspectives help avoid biased or overly optimistic results that can come from repeatedly surveying the same group.

What are the challenges in maintaining a representative sample over time?

Maintaining sample validity is especially tough with long-term studies. People who dislike a product often drop out faster, skewing satisfaction scores. Controlling sample attrition is essential to keep findings accurate.