the good

A leading creative software company came to us with a timely problem. Its AI tools ran on a currency it called “credits,” a monthly allotment that let subscribers generate images, edit video, and more across its suite of creative apps. While users have become familiar with credits in the world of AI, the company’s own research showed that users didn’t understand, in relation to this product, what a credit was, what it was worth, or how fast they were spending it.

Nobody wakes up wanting a CRM. No one lies awake dreaming of expense-reporting software, and rarely does someone feel a flicker of joy at the thought of a new project management tool. Yet B2B software is a trillion-dollar industry built almost entirely on things nobody “wants.” That contradiction sits at the center of Rob Snyder’s new book, The Power of Pull.

92% of SaaS leaders say they’re satisfied with their visibility into the first 30 days of the product experience. Ask a follow-up question, though, and the picture changes fast: only 24% of those same teams track where users actually drop off during onboarding. Only 24% track NPS or CSAT from new users at all. That’s not a footnote.

Trial-to-paid conversion benchmarks are everywhere. A quick search turns up dozens of reports on what a “good” conversion rate looks like by industry, trial model, and company stage. What’s much harder to find is a real picture of what those trial experiences actually feel like from the inside, day by day, email by email, all the way through to the moment someone tries to cancel or upgrade. Getting that picture takes real work.

In product-led growth, the product sells itself. Free trials, frictionless onboarding, viral expansion mechanics. The strategy is proven. Canva, Zoom, Slack, and Dropbox have all used their products as their primary growth engine, and the SaaS world has largely followed their lead. But having a product-led strategy is not the same as having a product that actually leads growth. The strategy gets you to the door.

Here’s a question most SaaS teams can’t answer cleanly: of the users who started a trial last month, what were they actually trying to accomplish? Not their job title. Not their company size. What specific outcome did they show up hoping to get from your product? If the honest answer is “we don’t really know,” there’s a problem. Because the users who converted and the users who churned almost certainly weren’t trying to do the same thing, and your onboarding flow treated them as if they were.

Free trial. Freemium. Reverse trial. Each is a growth strategy, but they’re not interchangeable. These three SaaS trial strategies create fundamentally different user experiences in the first 30 days. They prompt different psychological dynamics, different activation patterns, and different failure modes. Choose the wrong one for your product, and you risk building an acquisition engine that quietly undermines your retention.

A user just clicked “Start Free Trial,” full of optimism after reading your landing page, watching your demo video, maybe even seeing a glowing case study. They’ve been sold. They’re in. Then the product loads. And the experience they get doesn’t quite match the one they signed up for. It’s not always dramatic. Sometimes the product is genuinely good. But it’s more complex than the marketing suggested. Or the “instant insights” they were promised require weeks of setup.

Every product team faces similar questions: Why aren’t users converting? Which design should we choose? What features do users actually want? The challenge isn’t just finding answers; it’s knowing which research method will get you there efficiently. This guide matches common business questions to the right ux research approaches. Whether you need answers in days or require deeper investigation, you’ll find a practical path forward.

Every week, a new AI research tool promises to change how teams understand their users. Faster insights. Cheaper than recruiting. Results in minutes instead of weeks. The demo videos are compelling, and the pitch is always some version of the same thing: why spend time and money talking to real users when AI can simulate them for you? We decided to find out if any of that holds up.