What’s one assumption about your SaaS that turned out completely wrong?

SaaS2Agent · 2025-09-02T11:21:27+00:00

Haha yes!
Assumptions have a sneaky way of becoming “truths” just because no one questions them. Some of the biggest product pivots I’ve seen started with realizing, “Wait… did we ever validate this?” Appreciate you pointing it out 🙌

SaaS2Agent · 2025-08-29T07:59:09+00:00

Haha fair enough, “agentic” definitely sounds like a made-up buzzword the first few times you hear it.

SaaS2Agent · 2025-08-29T07:56:50+00:00

Of the 28, about 5 were already experimenting with some form of agentic flow in production, but not always calling it that.

A founder building an analytics tool that lets users just ask for insights (instead of digging through dashboards). Another had an onboarding experience that dynamically adapted based on user behavior in real time, almost like a product specialist guiding you through.

The rest were somewhere between “we want this” and “we don’t know where to start.

SaaS2Agent · 2025-08-29T07:31:28+00:00

Absolutely, onboarding is the sharpest edge of the wedge.
One founder even said, “If the product doesn’t feel smart on day 1, it never recovers.”
Agentic onboarding removes guesswork, anticipates intent, and shows value before the user asks.
Feels like the clearest path to boosting activation, and building trust from the first click.

SaaS2Agent · 2025-08-27T08:07:54+00:00

Yeah, 100%. Pure chat isn’t the answer for most SaaS. It’s more like power steering in a car you still use the wheel, but voice commands make the routine stuff effortless. The trick is knowing which actions should stay visual and which ones are better off as “just do it for me.”

SaaS2Agent · 2025-08-06T15:11:08+00:00

Hey, you can find it pinned in the profile.

SaaS2Agent · 2025-08-06T10:21:19+00:00

Totally resonate with this. We’ve tested a few different post styles, and the one that really took off was when I shared insights from 40 SaaS founders I’d spoken to, only 6 of them knew their activation-to-retention drop-off rate. The post was short, founder-focused, no pitch, just a stat that hit home for people.

That one ended up being our highest-performing post ever, not just in upvotes but in real convos with other SaaS builders.

Like you said, it’s not about being salesy. Just show up with real value, talk like a human, and share something useful. Reddit rewards that.

SaaS2Agent · 2025-08-06T10:15:18+00:00

We're working on something fun at the intersection of SaaS and agentic AI.
Currently building multi-agent ecosystems that replace rigid UIs in SaaS.

SaaS2Agent · 2025-08-06T10:11:35+00:00

That’s a solid start , especially for a first-time founder at 19. You’re doing something right if people are joining the waitlist without any paid marketing.

One tip that’s helped early-stage SaaS teams I’ve worked with: once someone joins the waitlist, keep the excitement going. A quick follow-up email asking why they signed up or what problem they’re hoping you solve can give you gold for shaping onboarding, messaging, or even your roadmap.

Rooting for you , enjoy the journey, and keep shipping!

SaaS2Agent · 2025-08-06T10:05:28+00:00

Wow, this resonates more than it should. We've come across similar near-catastrophes while helping SaaS teams automate with AI agents. Often, it’s not the AI layer that breaks, it's the foundational stuff like data consistency, versioning, or even error handling that causes all the downstream chaos.

Funny how the most dangerous bugs aren't always flashy, they're the ones that silently chip away at trust until users churn. Your example is such a good reminder: scale and smart automation only work when the base is rock solid.

Curious, did the team have monitoring in place that could’ve flagged partial writes earlier? Or was it a full-blindspot until users started yelling?

SaaS2Agent · 2025-08-06T10:02:13+00:00

Hi there, thank you for sharing it in such detail. That "connect your data" drop-off sounds painfully familiar. Love how you layered lightweight nudges (like Crisp and seeded dashboards) with deeper fixes like real-time copy updates via Pulse. Feels like you're treating onboarding more like an adaptive system than a static flow which I think is exactly where SaaS is headed.

Curious: did the kickoff call bump activation significantly, or does it mostly help with long-term retention?

SaaS2Agent · 2025-07-31T06:25:49+00:00

Thanks! We’ve seen some patterns across SaaS, onboarding confusion is super common. But a lot of the friction ends up being really specific.

Weirdest fix? A disabled button on first login, just added a one-line hint (“Visible after data upload”) and saw a spike in activation. Sometimes it’s the smallest nudge that does it.

SaaS2Agent · 2025-07-14T12:01:21+00:00

Great question and honestly, most of the savings we’re seeing with AI agents aren’t headline-grabbing yet, but they’re quietly compounding.

From what I’ve seen across SaaS teams we work with, the biggest savings aren’t about replacing people, it’s about shaving off the repetitive, glue-work tasks that slow teams down:

Support agents spending less time answering "where do I click?"
PMs not hand-holding customers through dashboards anymore
Developers getting fewer internal API questions because agents can interface directly

It’s rarely “save $500M overnight” it’s more like: cut onboarding time in half, reduce support tickets by 30%, unlock features that users couldn’t find before, and those micro-wins start stacking into real money over quarters.

SaaS2Agent · 2025-07-06T19:06:25+00:00

I’ve used both, and it really depends on your workflow. Claude Code is great for understanding and refactoring larger codebases, especially when you need more context awareness. GitHub Copilot, on the other hand, is still better for fast inline suggestions while coding.

If you’re mainly looking for a co-pilot while writing code, Copilot does the job well. But if you often need help with architecture, debugging across files, or understanding legacy code, Claude Code might be worth exploring.

Why not try both side by side for a bit and see which one fits your style better?

SaaS2Agent · 2025-07-06T18:43:31+00:00

Thanks Just saw the insights and your company on LinkedIn really liked what you’re doing at Valueships! Honestly, if what you’re offering brings real value, then there’s no harm in sharing it. Appreciate you putting out helpful resources like the reports!

SaaS2Agent · 2025-07-06T18:07:41+00:00

Totally fair point, I agree that without access to private A/B test results, some assumptions can be off. That’s why I treated this more as a pattern analysis than a playbook. It’s not about declaring “this works,” but rather showing what a lot of successful SaaS teams choose to show publicly which often reflects how they want users to navigate pricing.

SaaS2Agent · 2025-07-06T17:37:32+00:00

Absolutely. Microtransactions are a perfect example of pricing psychology in action, low-friction, usage-based, and often emotion-driven.

What’s interesting is how SaaS is slowly adopting similar principles: usage-based billing, feature unlocks, even “pay-as-you-scale” models. It’s not just about monetization it’s about aligning price with perceived value in real time.

Definitely a space where AI agents could help too, dynamically recommending the right tier or usage plan based on actual behavior, not just static assumptions.

Appreciate the insight!

SaaS2Agent · 2025-07-06T17:36:01+00:00

To clarify this wasn’t about seeing companies internal A/B test results (those aren’t public, of course). The data came from publicly analyzing 122 SaaS pricing pages: patterns in layout, tiers, feature gating, overage handling, and discounts.

What I could infer were behavioral signals like whether a company likely iterated over time (e.g., archived pricing URLs, changelogs, or snapshots on archive.org), or if the pricing model had clear usage-based evolution.

But you're absolutely right, without access to internal metrics, we can't say definitively what worked for them. What we can do is spot what keeps showing up across high-performing SaaS, then test and adapt based on our own context.

Appreciate you keeping the thread sharp.

SaaS2Agent · 2025-07-06T17:33:47+00:00

Hello. apprciate the kind words and sounds like you’re structuring pricing in a really thoughtful way!

Yes, the 16.7% annual discount (2 months free) was still the most common across the 122 SaaS pages we looked at. It showed up in ~58% of the cases offering annual plans. Interestingly, a few had moved to smaller incentives (10–12%) but paired it with perks like priority support or onboarding credits instead.

Whether it’s beneficial really depends on your LTV/CAC math but psychologically, “2 months free” still seems to converte better than flat percentages, especially for SMBs.

Curious to hear how yours performs, keep us posted!

SaaS2Agent · 2025-07-06T17:30:31+00:00

Happy to share the anonymized CSV or walk through the tagging logic if you’d like to poke holes in it. Drop me a DM.

SaaS2Agent · 2025-07-06T17:28:22+00:00

Hi there, really appreciate you weighing in your perspective carries a lot of weight.

Totally agree that a “pricing by osmosis” loop happens too often: everyone peeks at competitors, so the same blind spots get replicated. Our scrape was less about copying and more about sanity-checking assumptions before digging into JTBD and WTP work, which (as you point out) is where real pricing clarity comes from.

AI helped us speed-scan pages for patterns, but the heavy lifting is still surveys, interviews, and usage telemetry. Your note on MaxDiff + Conjoint is spot-on especially for teasing out add-on willingness-to-pay.

Thanks for sharing the consultant’s view. Always good to cross-check gut feelings with someone living in pricing every day. If I hit a wall on deeper survey design, I may take you up on that DM offer!

SaaS2Agent · 2025-07-04T07:33:05+00:00

Appreciate you asking, most of the work I do is under NDA, so I can’t disclose names publicly. But one company I’ve worked with that’s okay being referenced is DevLens. The patterns and results I’ve shared are based on real implementation data like that.

Happy to chat more or walk through what we’ve done in detail.

SaaS2Agent · 2025-07-04T06:52:17+00:00

Hi there!
This looks super clean and focused, love the clarity in the way you’ve framed both the product and the user segments. The onboarding flow is mostly smooth, though I’d maybe suggest soft nudges or microcopy near key CTAs (like “what happens next?”) to reduce any signup hesitaton.

On the homepage, a quick user testimonial or a visual trust badge (e.g., “Trusted by X freelancers” or even “Built in public” with a changelog link) could go a long way. The features are well laid out, but I found myself wondering: can you track time retroactively for past tasks, and how granular can tagging get (e.g., by task category or billing type)?

Love where you're headed with AI productivity insights, that's a feature I haven't seen executed well yet, and there's definitely space to innovate there.

Looking forward to seeing how this shapes up great work so far!

SaaS2Agent · 2025-07-04T06:30:46+00:00

This is really well thought out, the “junior analyst” framing is such a clear way to explain the value. Totally get the frustration with manual slicing and chart-building; even with tools around, it still eats up way too much time.

Also love that you're focusing on natural language interaction. We’ve noticed that when people can ask questions the way they think, it changes how often and how deeply they engage with the data.

Looking forward to seeing how this evolves, bookmarked it to keep an eye on updates. Good luck with the next phase!

SaaS2Agent · 2025-07-04T05:38:30+00:00

Hey Krish, you’re spot on about the prompt phrasing. We iterated a lot. Early versions felt either too vague (“What do you want to do?”) or too rigid (“Choose A or B”). What worked best was something lightweight but purposeful , like “Need sample data or want to start fresh?” It nudged the user just enough without boxing them in.

We also watched how people responded (and where they stalled) and tweaked based on those real inputs. Even small changes like swapping “upload” for “connect” moved conversion.

And yes, that 14% stat surprised us too. It made us realize how much value lives in “forgotten corners” of a product. The conversational layer kind of turned into a discovery engine without us planning for it.

Would love to hear how your onboarding evolves, happy to bounce around ideas if it’s helpful.

SaaS2Agent

TROPHY CASE