Atomic Research Nuggets

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This part of the field guide comes from our 2019 version of the UX Research Field Guide. Updated content for this chapter is coming soon!

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If you paid attention in your grade school science classes, you probably remember what an atom is: the smallest unit of matter.

When you break research data down into its smallest unit, you get stand-alone facts or “nuggets” of information. These atomic research nuggets can act as the building blocks of flexible, high-impact insights repositories and evidence-based decisions. 

Let’s dig into the history of atomic nuggets for UX research and when, how, and why to use them. 

In this chapter:

  • What are atomic UX research nuggets?
  • Pros and cons of using an atomic nugget framework
  • When should you use atomic nuggets?
  • How do you create and record atomic nuggets?
  • Tools for creating, sharing, and storing atomic nuggets

What are atomic UX research nuggets?

Atomic nuggets (a.k.a atomic insights or just facts) are data-backed user insights, broken down into their smallest parts. Usually stored in an insights repository as part of a knowledge management strategy, atomic nuggets are immutable pieces of data consisting of an observation, evidence to support that observation, and relevant tags for searching and filtering. 

For example, an atomic nugget from a user interview could look like:

  • Observation: Support articles are difficult to find with the current navigation.
  • Evidence: [Link to a video clip of the user saying, “I’m not sure how to find the support articles from here—I assumed they’d be under ‘resources’ or somewhere in the footer.”]
  • Tags: #Support, #Navigation, #Interviews
The 3 components of an atomic insight nugget for UX research - observation (what did you learn), evidence (how do you know it's true), tags (which themes is it related to)
The 3 components of an atomic insight for UX research

Developed concurrently by Tomer Sharon and Daniel Pidcock, atomic research was inspired by atomic design—an approach to design in which systems are broken down into their smallest modular parts. These ‘atomic’ parts can then be reused and recombined to create new designs more efficiently. 

Research findings can be broken down and reused in a similar fashion, as described by Tomer Sharon in his article about the foundations of atomic research

“Atomic Research is an approach to managing research knowledge that redefines the atomic unit of a research insight. Instead of reports, slide decks, and dashboards, the new atomic unit of a research insight is a nugget. A nugget is a tagged observation supported by evidence. It’s a single-experience insight about a customer’s experience.” 

Daniel Pidcock’s definition and approach to atomic insights is slightly different from Sharon’s. Pidcock breaks nuggets down into four components (experiments, facts, insights, and recommendations) as opposed to Sharon’s three (observation, evidence, tags). However, the theory and foundations of the two approaches are similar enough that we’re going to refer to ‘atomic research’ as encompassing both throughout this article.

Listen to Daniel Pidcock describe his approach to atomic UX research at the 2018 UX Brighton Conference in the video below.

How do atomic research nuggets differ from research reports?

If an atomic nugget refers to a single insight or observation from a study, then a research report would be more like a ‘molecule’ or whole ’organism’ of insights. 

Tomer Sharon describes the difference between a report and an atomic unit of a research insight

“The nature of research with users (and of these reports) is that you always learn more than what you intended to learn. As a result many reports include unrelated topics, insights, and findings that could prove useful in the future.”

Atomic nuggets are not meant to replace reports. Instead, atomic nuggets are intended to be used in addition to reports to make repositories more fluid and valuable in the long-term. 

Writing and storing full study reports is still important for retaining the context behind each insight—the study type, focus, raw data, and potential limitations or bias. But, tagging nuggets within each report allows you to create a modular knowledge system. Over time, these insights build off of each other and enable you to make decisions that are based on evidence from many different studies. 

Lucy Denton, Product Design Lead at Dovetail, used the atomic method for a large-scale, high-stakes research project, and found that it helped focus the team’s roadmap moving forward: 

“By boiling everything down to actionable ‘nuggets’ instead of defaulting to a typical research report as an output, Dovetail was able to develop a clear and strategically sound plan for moving forward.”

🎧 Listen to Lucy talk about the Dovetail project using atomic research on the Awkward Silences podcast

The atomic model does NOT…

Require you to change or alter the data in any way. Atomic insights are still true, objective discoveries from research—they’re just a different way of sorting and categorizing the information you collect from studies

Pros and cons of atomic research nuggets

The atomic research model has been embraced by some and rejected by others, each with valid appraisals. 

At User Interviews, we’re not expressly ‘for’ or ‘against’ atomic research.  As a general rule, we’re optimistic and encouraging of experimentation—but our goal with writing about this topic is not to push anyone into adopting it. Instead, we want to give you a balanced, objective overview of atomic research and let you decide whether it’s the right approach for you. 

We’ve taken a close look at the arguments for and against atomic research and broken down the proposed benefits and limitations for you below. 

Benefits of atomic research nuggets include: 

  • Improved archiving and searchability: Atomic nuggets improve the archival quality and accessibility of insights, preventing useful insights from being lost, forgotten, or buried in long reports. This is especially useful for democratized or distributed UX research teams
  • Increased customer empathy: Nuggets quickly relay information to stakeholders, helping you create a shared understanding of the customer. 
  • Reduced waste: Nuggets allow you to tag and capture insights unrelated to the original research question. This reduces the amount of disposable research in your repository—the ‘irrelevant’ info you throw away at the end of a study. 
  • Reveals multi-study patterns: Nuggets reveal patterns across different projects and methodologies, allowing you to easily identify knowledge gaps and reduce the labor required in future studies. 
  • Better decision-making: Nuggets allow you to connect insights from multiple sources to make better, evidence-based decisions. The more nuggets you have that support a decision, the higher your confidence can be. 

Criticisms of atomic research nuggets include:

  • Lack of context: One of the most powerful criticisms of atomic research is that it tends to highlight insights while de-emphasizing the context in which those insights were discovered. Some researchers have referred to this as a “dumbing down” of research findings, arguing that you can’t accurately interpret and act on insights independent of their context. 
  • Unwieldy effort required: Along with writing full study reports, researchers have to take the extra time and effort to tag nuggets from each study, regardless of whether or not they’re currently applicable to any open questions or pending business decisions. This additional effort can interfere with the agility of UX research teams
  • Manual reorganization of old reports: Most companies already store research findings in the form of reports. To switch to an atomic model, they’d have to either manually re-tag and re-organize historical data (which may be too cumbersome a process for time-constrained teams) or disregard it in future atomic-based decisions. However, dealing with legacy reports is always difficult when moving to any insights repository system.
  • Less useful for quantitative research: Some opponents of atomic research have expressed concerns about its compatibility with quantitative metrics. Tomer Sharon has responded to this concern, saying that quantitative research isn’t meant to be shoehorned into the atomic approach, but instead done in parallel for a qualitative understanding of the ‘why’ behind certain quantitative metrics.

Many of these criticisms of the atomic research model have been refuted by Daniel Pidcock as well. He argues that a good atomic repository can have as much context as a report-based one—this depends more on how you use atomic insights than an inherent issue with the process. Additionally, atomic research can be agnostic in terms of its data sources, making it easier to combine qualitative and quantitative data once the data has been processed into nuggets.

Listen to Daniel Pidcock address these concerns in-depth in his Atomic UX Research Best Practices talk at User Research London 2022:

When should you use atomic research? 

Any organization that does research is likely able to incorporate atomic insights into their analysis and synthesis process, but that doesn’t mean it’s the best option for everyone. 

Tomer Sharon identifies three situations where atomic research is most effective:

  1. When an organization has a large number of employees, particularly those that are geographically dispersed, such as big-chain brands like Walmart or Starbucks. 
  2. When an organization has many customers, such as Lyft or the NFL.
  3. When the customer experience is delivered through multiple channels, including brick-and-mortar spaces, apps, and person-to-person services. For example, airports or retail brands. 

In other words, the larger and more complex the business or the customer experience, the more useful atomic insights will be. 

Studies are often conducted on a relatively small, manageable scale that doesn’t necessarily take into account the entire context of the business. For example, they may be specific to one channel or customer segment. Atomic insights can help you identify broader, brand-level patterns across these many projects. 

📚 The bigger the team, the bigger the bottleneck. Learn how to relieve common recruitment and panel management pains as your UX research practice scales

How do you create and record atomic research nuggets? 

If you’ve determined that atomic research is the right model for you, here’s how to get started. 

Psst—looking for an atomic UX research cheatsheet? Download this one, developed by Daniel Pidcock at Glean.ly, to reference as you develop the facts, evidence, insights, and recommendations for each nugget.

example of atomic research in practice - experiments, facts, insights, conclusions
Atomic research in practice by Daniel Pidcock

1. Establish a solid taxonomy. 

The tagging structure (also known as a taxonomy) is a key element of atomic UX research and the foundation of an effective research repository.

Establishing an effective taxonomy off the bat will set you up for success with atomic UX research—and also help you avoid common headaches with your research repository generally, whether you’re using a simple Excel spreadsheet or a purpose-built repository tool. 

As Hugo Froes, Product Operations Lead at OLX Motors EU, says in his article about creating a research repository:

“Establish your approach/framework well, so that indifferent of the tool, the logic is well thought out and you know how you plan on synthesising the data. Once I had gotten to grips with Polaris [the atomic repository template created by Tomer Sharon] and had adapted it to our needs, the core concepts could easily work in almost any solution because the basis was solid.”

There are a number of different types of tags that you can include in your taxonomy for atomic UX research. Tags may be:

  • Procedural, e.g. the date, time, source, research method, or evidence media type
  • Demographic, e.g. age or location
  • Experience-oriented, e.g. the magnitude or frequency of the observation or the emotional state of the participant
  • Business-oriented, e.g. the related revenue range, business unit, or product line
  • Service design-oriented, e.g. the journey, act, scene, character, or prop

Include tags to suit your organization’s needs, but be sure to bake in flexibility as well—taxonomies are like living, breathing organisms, and they should be able to change as your customer knowledge and goals change over time.

2. Conduct the research!

Because atomic nuggets are an approach to the analysis and synthesis of research data, the atomic research model doesn’t require any changes to the actual research process

Plan your study, recruit participants, conduct research, and write up a complete study summary as usual. 

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3. Identify single-experience facts.

Once you’ve combed through your research data, start identifying single-experience facts—that is, unbiased quotes, observations, or statistics gained from the study.

 

“Facts make no assumptions, they should never reflect your opinion, only what was discovered or the sentiment of the users.” - Daniel Pidcock

Here are some examples of single-experience facts:

  • 50% of users couldn’t find the “add to cart” button
  • 2 out of 5 users scrolled to the bottom of the page
  • 1 user said they’d like the ability to create wishlists to save products for later

These are “single-experience facts” in that:

  • They’re reflective of only one type of experience
  • They’re objectively supported with data
  • They don’t venture any guesses regarding the driving factors behind the experience

Don’t combine facts (e.g. 1 user accidentally clicked the “add to cart” button, and 50% of users couldn’t find it) or add any reasoning (e.g. users struggled to find the “add to cart” button because it’s too low on the page) at this stage. 

4. Derive insights from those facts. 

Now that you’ve identified a list of unbiased facts, start to interpret what those facts mean. What do they teach you about the user experience? 

Here are some examples of the insights or interpretations you might derive from the example facts in step 2: 

  • The language used on the “add to cart” button isn’t clear
  • The “add to cart” button is located too low on the page
  • The “add to wishlist” feature isn’t prominently displayed

You can connect one or more facts (from the same study or from previous studies) to create an insight, but the more facts you have to support an insight, the more confident you can be in its accuracy. In some cases, new facts might disprove an existing insight. 

5. Identify opportunities or recommendations. 

Now that you’ve identified useful insights, what are you going to do with them?

For example, if you determined that the language on the “add to cart” button wasn’t clear, your recommendation could be to experiment with different copy or add an image to the button. Or, if the “add to wishlist” button wasn’t prominently displayed, you could change its color or move it to a more noticeable position on the page. 

✨ Prefer to work visually? Use this Atomic UX Research template from Figma to organize the different elements of your atomic nuggets. 

6. Record the nugget in your repository.

Make note of the nugget in your research repository.

In order for atomic nuggets to be useful and usable, you need to have a repository that supports them (more on that in the Tools section below) and add all of the necessary information to the record. 

Be sure to include:

  • What you observed: Include both the ‘what’ and the ‘why’ in this section. 
  • Evidence that supports your observation: This can be in the form of a short 30–45 second video, screenshot, image, audio, or text. 
  • Tags: These will help you filter and search for relevant nuggets in the future. 

For example, here’s a walkthrough video of how researchers can tag key insights and search or filter them in the repository tool Aurelius. 

7. Share the insight. 

Finally, you need to share the insight with stakeholders

Sharing insights can be done through many different channels, such as:

  • Company-wide research newsletters
  • Slack channels 
  • Live presentations

We recommend sharing through more than one channel to increase the likelihood of people seeing, remembering, and using the insights. 

📚 Need help getting started? Check out these 31 creative templates and examples of UX research presentations and reports.

Tools for creating, sharing, and storing atomic research nuggets

You have two primary options for creating, sharing, and storing atomic research nuggets:

  1. Manually, using spreadsheets like Google Sheets or Airtable. Spreadsheets are easy to start and use, but they’re difficult to maintain as you scale. If you go the manual route, you can use free templates like Polaris by WeWork, an Airtable template from Tomer Sharon, or this Notion template by Konstantin Escher, head of User Research at OneFootball. 
  2. Using specialized repository tools. Purpose-built tools are great for large teams or those with more frequent research cycles. Typically, they have dynamic search functions which allow you to get the most out of your nuggets.

Below are some of the most popular repository tools for storing atomic-based research data, including:

  • Glean.ly
  • EnjoyHQ
  • Aurelius
  • Dovetail
  • Consider.ly

Note that not all insights repository tools make findings accessible in the form of atomic nuggets. For example, Condens decided against the atomic format, but they still allow you to filter and search for keywords to find relevant data. 

Glean.ly

Glean.ly is a single, searchable, secure, scalable user research repository, built by Daniel Pidcock specifically for the atomic research model. Currently in its beta phase, Glean.ly includes flexible admin controls and provides a “confidence score” for each insight based on factors like the type, age, and amount of evidence. 

✨ Watch a full demo of the Glean.ly platform in the video below.

EnjoyHQ 

EnjoyHQ is a research repository with tools for aggregating research data from multiple places, searching and organizing data, and easily sharing your findings with stakeholders. It has a wide suite of integrations and flexible pricing plans to grow with your team. 

Roberta Dombrowski, VP of User Research at User Interviews, chose EnjoyHQ as our repository tool. Hear her discuss the process of rolling out our repository in this Awkward Silences podcast episode

Learn how to implement atomic research using EnjoyHQ in this article by Sofia Quintero, Founder of EnjoyHQ with UserZoom. 

Aurelius 

Aurelius is an all-in-one repository for researchers to organize notes, capture insights, analyze data, and share results with stakeholders. It includes features for automatic keyword analysis and automatic reporting for every project. 

Read their blog about creating atomic nuggets for UX research in the Aurelius platform or check out their overview video below. 

Dovetail 

Dovetail is a repository tool that allows you to analyze, synthesize, store, and share your customer insights. Their repository is deeply searchable and includes built-in analytics to help you monitor how research is used across your organization. 

✨ Learn how to create atomic nuggets using the Dovetail platform in this article or join one of their weekly live demos to see it in action. 

Consider.ly

Consider.ly is another repository tool built specifically with atomic UX research in mind. Their tagging system allows for a structured searching and filtering of data, and they also have tools for qualitative data analysis

✨ Learn how to apply atomic UX research in Consider.ly in the video below. 

🛠  For a deeper dive into these and other important tools in the UXR toolstack, check out our 2021 UX Research Tools Map.

In a nutshell

As you scale your UX research practice, atomic research nuggets can help you:

  • Organize your data in an easily-accessible way
  • Reveal multivariate insights across projects
  • Make confident, evidence-based decisions

Of course, the atomic method is just one strategy for sharing research findings with stakeholders. Browse the other chapters in the Reporting and Deliverables Module to learn how to write reports and presentations, create user personas, or develop customer journey maps

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