Returning to UX Research
Unfortunately, one of the biggest areas to suffer budgetary cuts in the SaaS product world has been UX research. I’ve seen this through a number of client engagements. The assumption by executive teams has been that they get enough data from website analytics, app usage data and so on. That was understandable, but it hasn’t played out very well. Why?
All that data you’re pulling in is lagging data. Most importantly, that type of quantitative data only tells you what people did. It doesn’t tell you why. And if you’re trying to build a product or drive your marketing based on what people did, you’re never going to understand why they bought your products or service. And that’s where the gold is.
SaaS products are in a bit of a winter lately. Just take the MarTech (Marketing Technology) sector as an example. Today, there are over 14,000 MarTech products on the market, 99% of which are SaaS products. From proposal writing and design to tools like Canva and CRMs small and large. Differentiation is down to features and benefits arguments. Not good.
Rebuilding UX Research in Challenging Times
So the thing is, if you’ve cut your UX research budgets and now you’re relying on a product designer, marketing coordinator and, I’ve seen this, a business analyst to drive your minimal analytics on user insights, you’re in trouble.
One helpful approach is to ensure that your UX research processes and methodologies are built into your agile processes. Assuming you’re using an agile approach. The critical part is understanding the value of qualitative insights integrated with quantitative data. See above where quant tells you what and qual tells you why.
An agile UX research approach can take a while to get in sync with the product team, but when it does, it’s quite remarkable. A good UX researcher understands the value of quant and qual data and how empathy plays a role. If they do netnographic research into user groups, online forums and social media listening, you’ve got a true ace on your team!
If they’re really good, they’ll also be building an archive of digital artefacts such as screen shots, past interviews, user journey documentation and social media listening. Such an archive can be invaluable when aligned with using LLM tools like Claude or ChatGPT for analysis.
They key to leveraging historical research of course, is having a good knowledge management system that can be mined. Thus can be easily built in SharePoint or similar tools like Notion. Drawing upon historical research can identify features that flopped our got dropped that were preferred, whuch can guide new feature development and A/B testing.
Such insights can inform backlog prioritizing, opportunity mapping, pain points, validation of feature ideas and concepts and enrich user stories and personas development.
I’ve helped a number of UX research teams get aligned with product development and understand how to move faster and know the limitations of qual and quant data and where to apply empathy while not getting bogged down in cultural insights that don’t help.
It’s a true mix of art and science, but done right, leads to UX researchers bringing true value to the table and SaaS products being able to find true differentiation for their brands.
