A 7-step research strategy framework
No matter how much you plan to use AI to power your research, keeping a strong foothold in the guiding principles is vital. Let’s drill down and refresh on some user research fundamentals:
1. Choose your research methodology and plan
If you’re the planning type, you’ll love this stage. Here, you’ll decide which research methodology aligns with your goals.
Figuring this out involves considering what you must learn and how your team will use the findings. In other words, it’s time to define success.
Once you know your purpose, it’s time to choose between a qualitative research (subjective experiences) or quantitative research (numbers and stats-oriented) approach. It’s also super common to arrive at a blend of two, known as mixed methods research.
Classic research type examples and branches:
Diary study (aka ‘camera study’)
Closed-ended survey questions
Likert scale assessments
Data mining and analytics
2. Recruit suitable participants
After choosing your research method, you must ensure relevance by finding participants in your target demographic. Some proven ways to recruit participants include:
Social media outreach (posting on Facebook, Reddit, or Discord)
Hiring a research participant recruiter
Paid ads calling for volunteers (online and offline)
3. Gather feedback
Here’s the fun part—gathering feedback. With your newly found participants and research plan, it’s time to undertake your research. Don’t rush this part. You need plenty of time to gather quality feedback.
Examples of gathering feedback include
Sending out email surveys and collating the responses
Gathering reviews and other customer data to perform sentiment analysis
Observing participants and consolidating your findings
4. Categorize the feedback
Now that you’ve got loads of information from several sources, what do you do with it? Pop your organizing hat back on and index the data into manageable categories.
For example, the feedback type: What’s the feedback about?
Also, the theme: What’s the business area?
5. Code the feedback
Coding makes it easier to group, understand, and act on feedback. Essentially, every piece of data should have a code.
Suppose a customer wants to see a new automatic saving feature on their banking app—the code could be “auto-saving on the app,” with any related feedback coded accordingly. Creating a codebook documents your research framework and eases team collaboration. Here’s a guide to crafting your own →
6. Analyze the codes
You may need to revisit the codes and feedback several times. Or you might realize that certain feedback needs multiple codes to cover all requests. (Applying more than one code means a more thorough approach so that you won’t miss out on crucial insights).
For example, feedback data might apply to multiple areas, such as mobile experience and customer appreciation.
7. Score, summarize, and share
It’s time to group similar codes to determine the most common feedback and score them accordingly.
For instance, if the most common code is “shopping cart bug,” prioritizing that is likely to keep customers happy and the revenue flowing. Once your data is tidy and easy to understand, summarize your findings into a digestible analysis that’s clear and actionable. Make it available to key stakeholders organization-wide so they can work with valuable insights and keep fine-tuning your product in ways that make your customers feel heard.