Stuck on "what next"?
Eusebiu and I shipped a few SaaS products together, and it's always the same wall we end up hitting: we achieve some initial traction, and then growth turns into guesswork.
We'd end up juggling a dozen expensive and disconnected tools — analytics here, a competitor tracker there, a spreadsheet of "growth ideas" nobody executes — and still couldn't answer the one question that mattered: what's the most impactful thing we should be working on next? marketing? new features? direct sales?
Powsight exists because we didn't want another dashboard to interpret. We wanted a system that builds a real profile of our specific business and, most importantly, tells us where the leverage is based on hard data (Stripe finances, Google Analytics traffic and events). It's meant to act as your personal growth team -- analyse the business data then surface insights that'll power your SaaS growth.
This is just the beginning --we have plenty of other features in mind, all around a single goal: helping startups grow.
We've just launched, so we're looking for founders who want to scale their SaaS while helping shape the product early, so we're offering 50% OFF for life with code EARLY50.
My personal promise to you, if you join, is that your product's growth will become our #1 priority.
So, what next?
The part not addressed yet is how you rank across categories that are not comparable on the same axis. Marketing, a new feature, and direct sales outreach have different time horizons and different confidence levels in the data behind them. If Powsight tells someone the top single action this week is a landing page tweak, how does that get weighed against a sales push that might take a month to show up in Stripe. Is there one ranked list across all three, or does it stay in three lanes and the founder still does the final weighing themselves?
Great questions.
We're not prescriptive about the "top single action this week". This is a hard problem indeed, one that we'd like to solve in time but we're definitely not there yet.
What we do today: insights get generated daily across separate lanes (revenue, acquisition, retention, product, market) each surfacing deep findings within founder's own SaaS data. The user assesses the insights and recommendations and makes the final call on what's worth acting on.
With this being said, we're going to continuously improve the insights engine which means the certainty around recommendations should only increase.
I think the hardest part of things is “what’s next? ~
There are always several ideas.
The dilemma lies in figuring out what is worth doing.
A useful filter I’ve often found effective is: what’s the one metric we’d work on if we could only move one this month?
It shifts the conversation away from “what sounds useful?What is it that may actually move the business?”.
I’d also be careful to distinguish between whether the data supports an opportunity, or just describes what has or will happen.
For useful knowledge of where growth came. The actual value lies in knowing what to do differently as a result of it.
This is great framing.
I'm personally guilty of answering "what may actually move the business?" with a lot of guesswork — probably natural, but looking back I ignored a lot of useful data and didn't get enough traction on my other products because of it. Powsight is a direct result of that: I needed something continuously analyzing my data while I build/ship/market, to give me signal I'd otherwise miss.
I do agree that the distinction is important, but in my experience, even the "describes what happened" side is underrated when it's presented well — it surfaces things you wouldn't have noticed on your own. Example from my other product (a template builder for repeatable prompts):
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Insight: Your core product promise is "Save It Once. Find It Fast." However, your analytics reveal a severe breakdown in the "Save It Once" part of that loop. In the current period, users triggered
library_search92 times, but only triggeredprompt_created4 times andprompt_copiedjust 1 time. This massive imbalance means users are logging in (`auth_signed_in`: 14) and immediately trying to search, but because they haven't added their own content, they are searching an empty database. Finding nothing, they leave without extracting any value (only 1 prompt copied). This perfectly explains why you previously felt the need to build a "Curated Library" (to give them something to find), but the actual root cause is a failing "empty state" experience. If the UI drops new users into a search interface before forcing or guiding them to save their first prompt, they will never experience the core value of a private creative memory.Recommendation: Make the search in the library return 2 sections (private + public) not only private. Because lots of users start searching first
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That one genuinely would've slipped past me — the change was small enough that I hadn't considered it, but the data made the case clearly.
Thanks for the comment — very thought provoking 👍
The “sometimes the honest answer is nothing needs attention today” part is interesting. A lot of productivity tools seem optimized to keep surfacing work rather than helping you decide what doesn’t deserve attention. Curious how often the product actually gives that answer in practice.
Good question — honestly, more often than you'd expect. The dashboard is built to understand your current business state at a glance, so on a normal day for most founders it's just "nothing urgent, here's your overview, goal progress and daily insight" and that's it.
That's the whole design bet: focus over engagement. Dashboard talks about your progress or shows if anything needs attention (often the answer is no), metrics are there if you want to go digging, and insights are once per day so it doesn't turn into another feed to keep up with.
That’s a pretty clear design choice — especially limiting insights to once a day rather than creating another feed. I’d be interested to hear how users respond to that over time. If you’re open to continuing the conversation, feel free to share your email.
Absolutely. Seems like I'm not allowed to post links/emails yet so you can DM me on X under the same handler
Got it — you can also reach me at hello@beryxa.com. Feel free to email me there.
The “focus over engagement” choice is unusually clear for a growth product. I’d make “nothing urgent today” a first-class outcome and measure whether it reduces founder thrashing, not just whether people open the dashboard. For each insight, capture confidence, the evidence window, the action taken, and a short post-action note; otherwise a revenue bump can get credited to the recommendation when the real cause was a renewal or seasonality. A useful early metric might be decision latency: time from insight surfaced to a deliberate act, discard, or “not enough evidence” decision. Does Powsight currently distinguish an insight that was wrong from one that was right but simply not actionable yet?
Great suggestions 👍
> otherwise a revenue bump can get credited to the recommendation when the real cause was a renewal or seasonality
Yes, I totally agree -- it's critical that we identify correlation and causality and flag them as such
> Does Powsight currently distinguish an insight that was wrong from one that was right but simply not actionable yet?
Not yet but we're working on it -- it will be part of the insight improvement flywheel
> For each insight, capture confidence, the evidence window, the action taken, and a short post-action note
Initially I was thinking that adding notes on the insight may be complicating the UX too much (more clicks), but maybe now that you mention it, it can live as a different action on the insight, so users that want to track acting on an insight can
@negrudev For founders at $1K to $10K MRR, I would keep 1-to-1 outreach but make it trigger-based. Target teams that already have Stripe and GA data plus a visible decision problem: a pricing change, stalled activation, rising churn, a new acquisition channel, or a founder publicly asking what to do next. Offer a concierge decision teardown before asking them to connect data: one business question, the evidence you would inspect, one recommended action, and the metric that would validate it. Run ten manual pilots and measure three things: how many founders act within seven days, how many return for a second decision, and how many will pay after the first useful recommendation. I would also test referral partners such as fractional CFOs, growth advisors, and product studios because they already see this problem across multiple clients. If you want, I can help you turn this into a two-week outreach and conversion plan in a focused call today.
The strongest wedge here might be narrowing the promise from "growth intelligence" to "one uncomfortable action you should take next." Founders can ignore dashboards, but it is harder to ignore a specific weekly decision with a confidence level, the evidence behind it, and a tiny feedback loop after they act. I would make that action history visible early, because it becomes the proof that the product is learning the founder's business instead of just summarizing it.
This is very close to what we we're thinking of -- we've actually designed the "action history" but haven't prioritized it for the beta launch.
What we don't do right now and it's great that we get thoughts on is the concept of "a specific weekly decision" -- we're currently surfacing 1-2 insights per domain (market, revenue, acquisition, etc) per week but nothing too prescriptive such as "this is the one thing that moves the needle this week".
We'll have to think more about this as it's obviously very valuable, but hard to identify (sometimes there's multiple small things that compound, sometimes it's just one high ROI action).
Thanks for pointing it out.
The feedback loop you've built (act, discard, or flag as wrong) is the most important piece here, and it's the bit most analytics tools skip entirely. Dashboards describe. This one could actually teach.
One thing worth thinking about early: the hardest recommendations to validate won't be the ones that clearly fail. They'll be the ones that seem to work for the wrong reason. Someone acts on "push marketing this week", MRR goes up, the insight gets credit. But the real cause was a large customer renewing. A mandatory short note before marking any insight "done" would give you the qualitative texture you'll need to pattern-match over time, not just the boolean.
I ran into the growth-guesswork problem at every stage with my own product. It doesn't disappear after traction. It just gets more expensive to guess wrong. What MRR range are most of your early beta founders at when they first connect Stripe?
100%. I think this problem can be avoided if the loop starts from the actual action-result correlation, and only then rank higher correlated insights, if any. Which means user feedback on the insight is a nice to have, but not something that drives insight ranking -- that would be driven by hard data. Will think about this more, but your suggestion is definitely a plus.
> I ran into the growth-guesswork problem at every stage with my own product. It doesn't disappear after traction. It just gets more expensive to guess wrong.
Would you be curious enough to try it, see what gives for your case? Any account related data automatically gets purged on delete in case you don't like it or have any concerns around data.
> What MRR range are most of your early beta founders at when they first connect Stripe?
No data yet, but we've started reaching out to 1k-to-10k as our first guess for willingness to pay and high ROI from our insights
The pattern I see across my portfolio companies is that "what next" is rarely a missing-data problem, it is a conviction problem: founders usually know the answer and pick the more comfortable task instead. That means your real retention battle is behavioral, whether the founder acts on the daily insight, not whether the insight is correct. I would track insight-to-action rate as the north star from day one, because it will predict churn better than anything in Stripe.
Great point. Curious if you've seen anything that moves the needle on the behavioral side specifically? I would think hard-based evidence is a good start, but maybe not enough?!
One thing we did so far was to decide on delivering deeper insights once per day, rather than on request, as to nudge the users towards making actual progress on it rather than scrolling a feed of suggestions.
Two things moved it for me. Deadlines with a person attached: my portfolio founders act when they know someone is asking about that specific number on Friday, so consider letting a founder name one accountability contact who gets a copy of the weekly insight. And shrink the ask, because "run a pricing test" gets ignored while "email these six churned accounts today" gets done.
In time, we do plan on adding optional social features as well for people that want to participate in a community of fellow SaaS founders, so maybe this "Deadlines with a person attached" may become "Deadlines committed publicly to the community", maybe... I'll have to think about it but what you're saying makes a ton of sense
> "run a pricing test" gets ignored while "email these six churned accounts today" gets done
I think what you're pointing at here is specific & short beats general & hard because the progress is faster and starts the habbit of acting on suggestions. I think we'll have to find a balance here -- deliver high ROI recommendations but maybe split big-effort ones into atomic steps
Thanks for all the insights, I really appreciate it 👍
Interesting to see this framed around Stripe/GA data — makes total sense once you have customers and traffic to analyze. The version of "what next" I'm dealing with is earlier and messier: pre-revenue, so there's no funnel yet to instrument, just a handful of channels to test (organic content, community engagement, affiliates) with no clean signal on which one's actually working versus which one just feels productive.
The "act on it, discard it, or flag it as wrong" feedback loop you described further up is the part I'd want even at this stage — not because I have enough data for a model to learn from yet, but because writing down "I tried X, here's what happened, here's whether I'd do it again" is basically the same discipline, just done by hand instead of a dashboard doing it for you. Might be worth thinking about whether Powsight has anything useful to say to founders pre-traction, or whether that's deliberately out of scope for now.
We're actually in the same spot right now with Powsight as a product (after launch, pre-revenue).
While there's no customer data yet, we still have traffic data, which tells us enough about the channels and markets that provide opportunities. Also, the product, acquisition and market insights are still very valuable and on point, because they don't need customer data. Furthermore, no customers still means something to the insights engine, which when talking about revenue will focus on providing opportunities to get you your first.
But maybe the most helpful thing for us at this stage is having all the data under one dashboard (switching dashboards always gave us mental fatigue) -- this means we can track customers, traffic and other metrics in a single platform (soon we're also going to include github + others to bring more data under the same roof).
> but because writing down "I tried X, here's what happened, here's whether I'd do it again" is basically the same discipline, just done by hand instead of a dashboard doing it for you
This is very similar to one of the larger features currently in the works called "Workbench". We may trim it down to release it faster. Anyways, great point.
So yeah, while lack of customers is not ideal, we do care about pre-revenue or early stage SaaS because we're using Powsight to scale itself.
I'm open to any additional thoughts you may have on this :) You're point above is super valuable, so thanks a lot
That actually reframes it well for me — I'd been assuming "pre-traction" meant "no useful data" full stop, but you're right that product/acquisition/market signals are there way before there's a paying customer to point them at.
The single-dashboard bit is what lands hardest though. My version of a "workbench" right now is a plain text file — every channel gets a dated entry, what I tried, what happened, whether I'd do it again. Works fine, but there's zero cross-referencing, so if I want to know whether the weeks I posted more actually got more downloads, I'm doing that comparison by hand. Sounds like what you're building is basically that, minus me having to do the boring part.
Curious how you're thinking about people in this exact spot — pre-revenue but happy to hand over Analytics/GitHub data. Is that an actual segment for you, or more a stepping stone toward the paying customer who already has traction?
I've discussed with Eusebiu (my co-founder) and we'll build a version of this by the end of next week -- we'll call it "Journal" 👍
Yes, pre-revenue SaaS founders are definitely a segment for us, not at all a stepping stone.
We started building this with one question in mind "How can we make more tech startups succeed?", because we wanted to succeed ourselves and we knew the rates are very low (we ourselves have failed 3 times). We decided our answer is "guide growth" and that means guiding the entire journey from 0 to 1.
That's also why we allow users to create and monitor multiple products once they join -- creatives have multiple ideas they want to put in practice, and we're there to support your next idea from day 1 (pre-revenue).
Good to hear it's a real segment and not just a placeholder until the paying cohort arrives — that's usually the tell for whether a product's actually solving a problem or optimising for a persona. "Journal" sounds like the right size for it too: cheap enough to ship fast, and it gives you a natural bridge into the Stripe/GA data later, since you'll already have a record of what a founder tried before there was anything to measure. Curious to see how it turns out — will keep an eye out.
we've just deployed it and added it as context for all insights. Additionally, we've created a new insight type called "Progress" that tracks your actions/journaling specifically and how that affected metrics in time.
I've created a short walkthrough video so you could get a glimpse of it: https://youtu.be/TA8BevDKijQ
Let me know if this is useful to you 👍
That was fast — great to see it shipped already. I'll watch the video and give it a proper look. Appreciate you building it out, especially tying it into Progress rather than treating it as a bolt-on feature.
Your core product decision is confidence, not recommendation breadth. For every surfaced action, show the evidence, expected metric impact, reversibility, and the next observation that would invalidate it. Then let founders mark each recommendation as acted on, ignored, or wrong. That feedback becomes both training data and proof that the system improves. I’d recruit the first customers around one promise: one decision per week tied to revenue, not another stream of generic insights.
you've actually nailed our approach 💪 evidence + grounded recommendation is exactly how we do it today, and yes — founders can mark each one as done, discarded, or wrong. That feedback loop is something we want to lean into more explicitly as proof the system is actually improving, not just logging actions.
Expected metric impact and reversibility aren't surfaced yet in a structured way — that's a gap we're adding to the roadmap. Same with showing the observation that would invalidate a recommendation; that's a sharp addition I haven't thought of before.
You're right to point this out
> one decision per week tied to revenue, not another stream of generic insights
Agreed, there's no point in generic insights -- they have to be specific, contextual, grounded in data. Because of that, we currently provide insights under the following topics: revenue, acquisition, retention, product, market -- once per day; we're going to expand to more when the need arises but it seems we're on the same page here -- focus is king.
Separately — curious what your take is on the best channel to recruit early customers for something like this. We've started with 1-on-1 outreach.
For context: we're targeting products with early traction/PMF already, since we need real datapoints (customers, traffic) to have anything to analyze.
The interesting part is that Powsight is trying to answer “what next?” from the actual Stripe and Analytics data instead of giving founders another dashboard. This feels like it could be pulling in a lot more organic traffic than it probably is right now. Especially just after launch, that could bring a lot more founders into the early feedback loop. Have you seen any unexpected patterns in what founders ask it to look at?
Honestly, no data yet — we launched yesterday in beta.
One small correction on the framing though: founders don't really "ask" Powsight anything day to day — that's actually the point, once Stripe/GA4 are connected it just runs in the background and pushes daily insights automatically, no querying required. So the unexpected patterns we're most curious about once people are in will be less "what do they ask" and more "which daily insights actually get acted on vs. dismissed."
Our goal is to guide founders while they work, not add another task on the list.
Appreciate the read on the traffic angle too — hoping that's exactly what pulls more people into this feedback loop early 😊
Since you’ve just launched, I’d also look at building some authority around Powsight through press releases and relevant media placements. Getting featured on the right publications could help with both credibility and traffic, while giving you more visibility for future launches and the “featured”/social-proof side of the product.
I can help with this if you’re interested. If you want to explore what kind of placements could make sense for Powsight, feel free to reach out to me.
Thanks, will do.
For now, we're focused on direct outreach so we can establish personal relationships with a small number of customers, but we'll keep in touch for after the beta phase 👍
Makes perfect sense. Direct outreach is probably the right move during beta getting close to those first customers should give you much better product feedback.
Once you’re through that phase and ready to scale distribution, happy to revisit the media side. Good luck with the beta 👍
thanks 💪 same to you!
@negrudev I can turn this into a two-week outreach and conversion plan for Powsight, including the trigger list, concierge pilot, partner channel, qualification criteria, and the numbers that determine whether the motion is working. The focused 20-minute session is $75. Book the Startup Advisory call here: https://calendly.com/dontae-threeum-nsuo/startup-advisory-call-20-min
thanks 👍 will think about it and reach out if the case
Hi! I'm building BrandScope — a tool that tracks how AI assistants (ChatGPT, Gemini, Perplexity, Qwen, GLM) mention brands in their answers. Found Powsight on Indie Hackers and ran a free AI mention scan as a demo.
TL;DR: 4 of 5 AIs recognize Powsight as "growth intelligence for SaaS founders" — but you're missing from the "top tools" lists of half of them.
Results across 5 AIs:
Best growth analytics for founders? GPT, Gemini, GLM recommend Powsight. Perplexity, Qwen: no mention.
Top growth analytics tools 2026? GPT (#1) and Gemini list Powsight. Perplexity instead matched "Powsight" to PowerSight, an unrelated company. Qwen: no mention.
Context + goals + metrics → insights? 4/5 AIs name Powsight. Qwen: no mention.
Powsight vs ChartMogul? GPT, Gemini, Perplexity, GLM all say Powsight fits "growth intelligence" better; ChartMogul wins on pure subscription analytics.
Is Powsight worth it? 4/5 say yes with fair caveats. Qwen: no mention.
Three things worth knowing:
Name confusion: Perplexity associates "Powsight" with PowerSight (an energy company) in top-tools questions — more content pairing "Powsight" + "growth intelligence for SaaS founders" fixes this.
No third-party coverage yet: your own site is AI's only source. One IH launch post, writeup, or directory listing would give AIs a second source.
Qwen (Chinese AI ecosystem) has zero knowledge of Powsight.
Happy to send the full report with verbatim AI quotes — what's the best email?
“What next?” is honestly one of the hardest questions once you already have something working. Sometimes having too many possible directions is worse than having none. How are you deciding what gets prioritized?
On the dashboard we do have the notion of "Top insight" which is provided by our internal scoring system based on probable impact, but otherwise we don't prioritize currently -- so far we've decided that once we analyze the data and provide recommendations, the founder knows to assess priority well enough.
Moreover, insights rarely overlap over any given week because they are being delivered on separate aspects of the business (market news, acquisition, retention, etc). Every week your data (actions, finances, analytics) tells a story -- we interpret it across longer timeframes and let you know what worked (so you can double down), what didn't (so you can avoid), red flags (so you are aware of it) or opportunities (so you can jump on it).
> Sometimes having too many possible directions is worse than having none
Now that we have AI, which accelerates the delivery, having multiple possible directions you could try may not be such a bad thing -- of course it depends on the stage the product is in (pre vs post PMF) but multiple directions provide optionality for founders that are stuck. If it's so clear that the best thing to do is X, we will surface it on several occasions (week after week) so the emphasis is indeed placed on that, but we're not prescriptive about it.
The disconnected tools trap is real. Hit the same wall building my SaaS, Atrium. Just more on the finance side (burn rate, investor updates) instead of growth. How are you separating signal from noise once Stripe and GA data's flowing in?
I think noise mostly comes from lack of context — the more the system knows about your specific business, the better it gets at filtering out what doesn't matter.
Insights are deep and specific, contextual, based on your specific business profile, rooted in data which our engine analyzes throughout time -- there's not much space left for noise. And if something still slips through, the founder has the final say: act on it, discard it, or flag it as wrong. That feedback loop is what improves future insight quality over time.
Have you managed to get past that wall with Atrium?
I'm curious whether the burn rate/investor-update pain showed up early on, during that messy pre-PMF exploration phase most startups go through.
Selamlar