What is Rapid UX Research?

You’ll often hear actors, entertainers, stand-up comedians, and bands use the line, “It took us years of hard work to become an overnight success.”

Rapid research is a bit like that. While it feels like a relatively new addition to UX research, it’s been building in the background for longer than you’d think, developing as the demands of our industry become more immediate and more decision-making is done through data and research and no longer left to chance. With technology moving faster than ever, the competition more dynamic, and the smallest percentage gains translating into millions of dollars, fast and efficient processes are definitely essential to today’s practices.

So, when designers, developers, stakeholders, and everyone else working on the project have questions that need answering yesterday, do we have a system ready to hit the ground running and deliver the data behind those answers in record time?

We do now. Welcome to rapid UX research.

Rapid research definition:

A rapid research program is a standalone, lightweight process that explores and delivers quick feedback on a preset research topic chosen by the designers, researchers, or stakeholders.

In other words, a rapid research program is quick (often a week but rarely longer than a fortnight), based on defined questions and criteria, built on a tight, templated, and compact schedule (so that everything from the project definition to delivering its stripped-down findings is efficiently contained), and ready to go with whatever its next demand is in record time.

What makes a rapid research framework different from traditional UX research sprints?

With existing agile processes working in sprint sessions to drive faster product development, haven’t we already got a method to gather those quick research insights rapid research is designed to deliver?

Well, rapid research projects (and a couple of closely related practices) deliver a UX research practice that takes that idea and runs with it. However, it goes a considerable step further, providing researchers, designers, and stakeholders with a quick feedback system for data-driven decisions that just can’t wait.

Often, agile processes become too focused on the product or feature instead of what’s at the heart of UX research—its users. Rapid UX research, while being as evaluative as the product developers need it to be for the product or feature, focuses on users’ performance, reactions, and concerns despite its small participant pool. Whatever the user research teams learn can be fed back into the mix, creating a backdoor of generative research many agile sprints miss out on.

Understanding user needs

Delivering high-quality insights without extended timeframes relies on interactions and interviews that get straight to the heart of the matter. Questionnaires are targeted and concise, and observational studies are quick to perform yet provide clear and immediate insights into real users’ needs.

Given the time shortage, participant recruitment must also be organised, fast, and effective, creating a small but relevant pool most likely to deliver the required data.

Effective testing methods and tools

As with the rolling research process and continuous discovery research process, there are common grounds between these three somewhat similar practices.

First, sticking to strict schedules is essential.

Second, having well-defined, adaptable templates for every script, stage, and practice will simplify practitioner efforts, while automating as many tasks as possible will keep everything moving at a swift and streamlined pace. This bank of templates is crucial for further research cycles and for bringing junior researchers on board to carry out suitable operations with limited input from a more advanced user researcher.

Short-form interviews, simple questionnaires, and A/B testing all suit the process (but are by no means an exhaustive list), delivering valuable insights in the least amount of time. Keeping studies structured and consistent is key, with qualitative data delivery far more suited to this style of research sessions.

Speed-driven synthesis

An effective rapid research program requires more than organizing participant recruitment and delivering tests. Data holds the gold of our research operations, so it’s crucial to identify the key themes through our rapid feedback evaluations. Thankfully, technology is on the research teams’ side regarding data analysis, with many real-time and automated analysis tools and analytical predictive data models available for identifying key metrics, patterns, and anomalies.

For further efficiency of analysis, automate data extraction (with AI-powered translation and transcription tools) and apply widespread knowledge sharing throughout the organisation via existing, most effective communication channels and a well-structured research repository.

As stripped down as the research process is, the findings and reports should match. Long-winded reports rarely get read, if even flicked through, so stick to simple, single-page findings with visual tools and diagrams to make the data as easy to digest as possible.

Where a rapid research project uncovers major or unforeseen issues, stakeholders can decide the next step, whether deeper analysis is required to track progress over time or to carry out further rapid cycle evaluations. In such instances, this scalable, rapid research framework becomes invaluable.

Running multiple research studies with multiple teams

Another beauty of running a rapid framework is that multiple studies can be carried out over the same or an overlapping period. With your bank of templates, sharing tasks and participants between research teams may be more efficient and help reduce the total amount of work required. Also, depending on how many teams you have available, holding interviews conducted by the same researchers can add continuity and flow across programs.