What is Atomic Research?

Atomic Research is a methodology which helps organize information gathered through user research. The goal is to provide highly reliable recommendations, in order to make more relevant decisions. It allows you to anticipate the future needs, ultimately enhancing overall customer satisfaction. Let's discover this methodology.

Atomic Research in practice

Atomic Research breaks down the knowledge gained from customers into four components:

• experiments
• facts
• insights
• recommendations

In practice, the recommendations include experiments, facts and insights:

• We did a user test and a remote test (experiments),
• we found out 3 in 5 users didn't understand and 20% of users are unable to find the feature (facts),
• which makes us think that the language used on the buttons isn't clear (insights),
• so we'll add icons to buttons (recommendation).

Let's dig into this framework:

"We did [experiments]..." — An experiment is a collection of "user interactions". It is a data point (a connection in analytics or a click on a A/B tested interface) for quantitative research or a verbatim (a quote extracted from unstructured text) for qualitative research.

"...we found out [facts]..." — A fact is a statement describing what was discovered based on user interactions. Facts make no assumptions and they should never reflect any opinion.

"...which makes us think [insights]..." — An insight is a sentence interpreting the chosen facts. An insight is linked to one or several facts even if they come from other experiments. Some facts might also disprove an insight.

"...so we'll do [recommendations]." — A recommendation is a sentence presenting a relevant solution based on one or several insights. The more insights that connect to the recommendations, the stronger the evidence supporting their value.

The origin of Atomic Research

Atomic research, introduced by Daniel Pidcock in 2018 and inspired by the principles of Atomic design, aims to harness customer knowledge. It is broken down to its smallest element, a verbatim or a data point, the "atom". These atoms are then assembled into molecules, forming intricate organisms.

• The atom is a user interaction. It is a data point for quantitative research or a verbatim for qualitative research.
• The molecule is a fact, made of several user interactions (atoms).
• The organism is an insight, based on one or several facts (molecules).
• The final component is a recommendation, based on several insights (organisms).

The beauty of Atomic research is that it forces evidence-based thinking. It encourages people to spend time digging into data, both qualitative and quantitative, to sort it and label it. This way, they create a solid foundation of knowledge for the entire organization.

In a nutshell, Atomic Research adds credibility to assertions by showing their provenance. It enhances trust in recommendations, allowing teams to bring customer feedback to their rightful position at the center of the decision process.

How companies embrace Atomic Research?

Companies are beginning to adopt digital tools to apply this methodology to their data. This has led to the emergence of two types of tools:

• Low-code tools like Notion, Airtable or Coda. They allow UX researchers to organize data as they want, using specific tables for specific components of the Atomic research.
• UX text tagging tools like Dovetail or Condens. They allow UX researchers to organize particularly well qualitative data, thanks to the tagging features, and to build insights.

The big idea behind Atomic research is to offer a methodology allowing to mix quantitative and qualitative data to strengthen the insights. And it is built to be used at large scale.

But when we look at how Atomic research is executed, we observe that it does not succeed to scale up. In France, several organizations abandoned Atomic research for three main reasons:

• Extracting verbatims from unstructured text requires excessive resources, primarily through manual processes.
• Existing tools fail to seamlessly integrate quantitative and qualitative data, limiting the depth of analysis.
• Implementing low-code tools at a large scale poses significant challenges.

That is why we launched Synopsis, a digital solution to leverage your customer insights. It uses AI to automatically sort and label user interactions, it builds insights mixing qualitative and quantitative data in a comprehensive way and it is usable by the whole organization seamlessly.

Sources

• What is Atomic UX Research? — Daniel Pidcock
• Foundations of atomic research — Tomer Sharon
• Introduire la priorisation RICE au coeur de l'Atomic Research — Yolaine Mercier et Michael Baeyens
• Atomic research in the European Commission — a UX case study — Pedro Almeida