Research Methodologies

November 25, 2019

The Last Frontier for Lean Methodology: Iterative Questions

Learn how to better utilize lean methodologies.

The Last Frontier for Lean Methodology: Iterative Questions
Athena Lam

by Athena Lam

Content Strategy Lead at Dalia Research

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Editor’s Note: This post is part of our Big Ideas series, a column highlighting the innovative thinking and thought leadership at IIeX events around the world. Athena Lam will be speaking at IIeX Europe 2020 in Amsterdam, Feb. 25-26. If you liked this article, you’ll LOVE IIeX Europe. Click here to learn more. 


Lean has been one of the guiding principles in the last decade. Using the Lean Startup methodology, popularized by Eric Ries, startups develop minimum viable products (MVPs) that are meant to be the most basic solution to a perceived problem. Using this methodology, teams move quickly to learn about customers and market demand and continuously build features with the intention to validate them with data. This iterative mindset has not only helped build products like DropBox and Product Hunt but been adopted by innovation teams at Fortune 500 companies.

Validation and iterations revolve around being able to ask questions and collect answers quickly. Technology has allowed for teams to build everything from web apps to landing pages quickly to begin collecting market data right away. Will people do pre-launch signup, download a trial, or pay? But how can teams that are not necessarily building a new web app apply the same principles to get public or market feedback with robust qua?

The simple solution is to ask.

Imagining Data Collection Possibilities

Asking consumers or the general public is not an intuitive step in the decision making process because online polls are assumed to be consumer engagement or casual polling on social media. Large-scale surveys are left to researchers at institutes such as Pew Research or enterprise insights teams.

But with automated and scalable platforms that can deliver surveys to the right audiences, whether they are niche consumer segments or the public in a specific country, new possibilities for research become possible. Asking questions and getting statistically significant results becomes accessible for teams and individuals.

Embed Data Collection into Decision Making

With global research platforms that can deliver insights in a matter of days, delivered to targeted respondents, teams can design focused surveys with one or two questions to test assumptions. Teams that adopt rapid public polls can embed data into their decision-making processes without disrupting their project deadlines.

Lean Surveys Allow Us to Ask “Why”

Lean startup methodology is about needing data-driven findings for relevant target audiences. And technology has finally enabled getting statistically significant answers, signals that can be actioned upon.

They are not meant to replace traditional market research, but rather provides us with the option to think iteratively about the questions we ask. With insights from the first wave of questions, teams can test their assumptions. Teams, that look at findings can analyze the data and ask “why”? A “why” can be followed up with another question and teams can refine their strategies or campaigns as necessary.

With the focused questions, teams can gain insights that are relevant now to make our decisions. whether it is market entry, or for a campaign, or whether consumers are aware of the types of eco packaging available.

This lean approach to public polling with statistical significance also increases the accessibility of market research for teams within global brands as well as startups, SMEs, and non-profits. This ultimately empowers all teams to continuously learn and iterate their services, products, and strategies iteratively.

Find out more about how Dalia used one question to create impact within our company, and radically empower partner organizations that we worked with at my talk at IIeX Europe 2020.

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big ideas seriesdata collectionlean methodologies

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The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.

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