Problem Validation and Customer Research: Challenges and Lessons Learned

Doing your first user research might be scary. You don’t know where to start and what to expect, without even mentioning that it has to be conducted remotely these days. In this article, I would like to share my experience, challenges I faced, and lessons I learned. I was fortunate enough to have a coach guiding me through the labyrinth of user research and sharing her wisdom (can’t thank her enough for that, honestly). So, let’s dive into it!

Finding a Problem

Always start with a problem. Ultimately, your research aim would be to validate the problem you think exists and then find solutions. If you start with a solution and then try to fit a problem, you won’t be able to come up with a good solution or you would spend your time solving a problem that doesn’t exist.

Assumptions and Hypotheses

You’ve done your preliminary research and you’ve got a feeling of what you want to investigate more.

Assumptions list and assumptions criticality chart. Assumptions are rated on how critical they are and how known they are
Assumptions list and assumptions criticality chart. Assumptions are rated on how critical they are and how known they are
Assumptions list starting with the most critical and assumptions criticality chart. You might wish to concentrate on the top right corner (critical and unknown)

Research Approach

After you created your hypotheses, you need to decide your research approach. What would be the best way to validate your hypotheses?

Research planning

So, you have decided on your research approach, now it’s time to draft the questions! Don’t skip this stage, always draft your questions before you load them to the platform of your choice. And ask somebody else to look at them and tell you if they don’t make sense. I’ve seen a few surveys asking participants to input their names, while surveys are expected to be anonymous. If for whichever reason, you need to collect names, make it optional and include data protection statement.

  • Write at least 20–25 questions including qualifying questions (especially important if you’re looking for participants meeting certain criteria; e.g. in my case, I wanted to survey parents based in the UK with children under 18 years old).
  • Provide as many answer options as possible. I found many people put two options and ‘other’ in every question, which poses a couple of problems: it would be hard to analyse after when everybody writes their own ‘other’ answer (obvious one) and you don’t give enough options to the respondents (you’ve got to think of users and their circumstances all the time!).
  • Don’t ask leading questions (prompting participants to answer in a particular way).
Example of a survey question evaluating financial literacy asking participants to grade statements from agree to disagree
Example of a survey question evaluating financial literacy asking participants to grade statements from agree to disagree
Example of a survey question designed to evaluate various money management skills (rather than asking them to assess their money management skills directly). Participants are asked to agree or disagree with a number of statements, which then can be analysed against other survey responses

Research Findings

You’ve done all the hard work, now the fun part! Not saying it’s not hard, but I’ve always found data analysis fascinating, which is nerdy me coming from the analytics background, convinced that data can be fun.

Infographics demonstrating the main survey findings related to when and how to learn about money and who should drive this
Infographics demonstrating the main survey findings related to when and how to learn about money and who should drive this
Example of demonstrating main survey findings to stakeholders
Example of affinity map
Example of affinity map
Example of an affinity map

Data analyst, graphic designer and illustrator, UX/UI designer based in Manchester, UK

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