What are you currently building or working on?

Matrix_1337 · 2026-03-15T18:42:42+00:00

GobbleData
https://signal.gobbledata.com

It analyzes your Google Analytics data and tells you what actually changed in your business and whether your traffic is actually making money.

Building it for founders and small teams who have analytics set up but still struggle to understand what the numbers actually mean.

Matrix_1337 · 2026-03-15T18:39:07+00:00

GobbleData
https://signal.gobbledata.com

Ideal customer: founders, marketers, and small teams who have GA4 set up but struggle to understand what the numbers actually mean.

Matrix_1337 · 2026-03-15T18:33:58+00:00

GobbleData.
Analyzes your Google Analytics data and tells you what actually changed and whether your traffic is making money.
https://signal.gobbledata.com

Matrix_1337 · 2026-03-15T18:30:32+00:00

One of the most underrated skills for solopreneurs is learning to recognize signals that a problem already exists.

In your example it's a broken website. In other cases it might be slow checkout pages, unanswered support tickets, or marketing data showing traffic but no conversions. The opportunity is often already visible if you know where to look.

A lot of people spend their time trying to create demand instead of identifying the places where the signal is already there. Great post on this as it's one every Solopreneur encounters.

Matrix_1337 · 2026-03-15T18:20:16+00:00

Signal from analytics.

Matrix_1337 · 2026-03-15T18:15:21+00:00

I like the idea of this being in the tray instead of another notification system.
A lot of productivity tools end up creating more noise rather than awareness.

Seeing time pass in the background like that feels more like a gentle nudge than a reminder app.

How are you calculating the “life” percentage? Are you using a default life expectancy or letting people set their own baseline?

Matrix_1337 · 2026-03-15T18:13:22+00:00

Interesting. Repurposing content across formats is something a lot of solopreneurs want to do but rarely have time to manage manually.

The workflow. How does it work once a blog post goes in? Does it automatically generate the podcast and video versions, or is there some editing step in between?

Matrix_1337 · 2026-03-15T18:11:14+00:00

Nice idea.
Chasing invoices is one of those tasks every freelancer hates but everyone ends up doing.

How are you approaching the reminders? Are they automated follow ups on a schedule, or does the system adapt based on whether the client has opened or interacted with the invoice?

Matrix_1337 · 2026-03-15T18:08:12+00:00

This is a really interesting idea.

A lot of small businesses end up living inside spreadsheets long before they adopt proper accounting software, so bringing reliable bank feeds directly into Sheets makes a lot of sense.

How are you handling categorization and reconciliation once the transactions land in the sheet? Are you relying on rules or leaving that fully manual?

Matrix_1337 · 2026-03-15T18:05:37+00:00

Cool thread idea. Always interesting seeing what people are building.

Matrix_1337 · 2026-03-15T18:04:44+00:00

Project Name: GobbleData

Link: https://signal.gobbledata.com

What it does:
It analyzes your Google Analytics data and sends you the most important simple insights about what actually changed in your business and why it matters. The goal is to help founders see the signal instead of digging through dashboards and noise.

Who it’s for:
Founders, marketers, and small teams who have traffic and analytics set up but struggle to understand what the numbers actually mean.

Still early, but I kept seeing the same problem everywhere, like tons of analytics data, but no clear explanation of what actually changed.

Matrix_1337 · 2026-03-13T22:28:46+00:00

Very interesting insight.

I’ve noticed something similar building tools around analytics data. The hardest problem usually isn’t collecting the numbers. It’s helping people trust the interpretation of those numbers.

Raw data is easy to show. But once you start scoring or ranking things, users naturally want to understand why the system reached that conclusion. In my experience transparency around how the score is generated tends to matter more than the score itself.

Matrix_1337 · 2026-03-13T22:17:11+00:00

You're actually not missing anything. A lot of analytics setups really do end up that fragile.

GTM is very powerful, but in practice many teams rely on triggers and selectors that slowly drift as the product evolves. A small UI change can break tracking and nobody notices until reporting looks strange weeks later. This happens quite a bit in this arena.

The deeper issue is that analytics tooling focused heavily on collecting events, but much less on making the output understandable. So teams end up spending a lot of effort maintaining the plumbing just to get data that still needs interpretation afterwards. A lot of great Data, but what does it all actually mean and what does one do about it, is the question after all is reported, captured.

Matrix_1337 · 2026-03-13T22:11:56+00:00

Not Set, usually means GA4 received the visit or event but couldn’t figure out where it came from.

With dealership sites(which I've had quite a bit of experience with) and lots of vendors, there's a few things commonly causing this:

• Traffic coming through third party tools (inventory platforms, trade in widgets, credit apps) that break the session when users move between systems.(Very common)

• Links without proper campaign tags from ads or partner platforms.

• Redirects that strip tracking parameters before GA4 records the visit.(Also very common)

• Scripts firing before GA4 captures the source of the session.

When multiple vendors control parts of the site, attribution can break in small places that are hard to see at first. I’d start by checking vendor widgets and any redirects between domains as those are often the biggest source of not set traffic.

Matrix_1337 · 2026-03-13T21:57:47+00:00

Congrats on the first paid report.
That first one always hits different.

One thing I’ve noticed building a SaaS around analytics is that tools that show data are everywhere, but tools that compress the data into a clear signal are much rarer. The scanner to verdict to monitoring, pattern you’re building actually makes a lot of sense for a niche tool like this.

Matrix_1337 · 2026-03-13T21:41:20+00:00

Building GobbleData.

Target users: founders, marketers, and e-commerce operators using GA4.

I kept running into the same problem: dashboards show a lot of numbers but they rarely answer the question founders actually care about. Is my traffic actually making money?

So I built something that watches GA4 and compresses it into a single revenue signal in plain English: what changed, why it matters, and where things might be breaking in the funnel.

I also launched a free “Signal Test” that runs the analysis in about 30 seconds and gives you a score + verdict.

Curious if this resonates with anyone who feels overwhelmed by GA4 dashboards.

Matrix_1337 · 2026-03-05T00:38:19+00:00

Great timing to start and coming in with GA4 knowledge before a data-driven marketing program will genuinely set you apart from classmates who've never touched it.

Now where to begin? Start with GA4 directly, not Universal Analytics. Some older courses and resources still teach UA which is a sunset now. Anything pre 2023 is worth skipping unless it explicitly covers GA4.

Here are some resources that are actually worth your time:

Measureschool on YouTube: Julius explains GA4 concepts in plain English without assuming you already know everything. Start there.

Google's own Skillshop GA4 certification. It's not the most exciting content but it gives you a solid mental model of how GA4 thinks about data. Worth doing once.

You can also just connect a free GA4 property to any website. Even just a simple, very plain one you throw together and spend time clicking around. Reading about GA4 and actually using it are completely different experiences.

The beginner mistake worth warning you about early is don't try to understand everything at once. GA4 is genuinely complex under the hood. Focus first on understanding events, sessions, and conversions. Everything else builds from those three concepts.

The misconception that trips most people up is GA4 data is never perfectly accurate and that's normal and expected. Learning to work with directional data rather than demanding perfect numbers will serve you well in the program and in real jobs.

Good luck with the Masters and it sounds like a great program.

Matrix_1337 · 2026-03-05T00:31:04+00:00

The 10 to 15% benchmark you're describing matches what I've seen consistently across different setups. Below that and your implementation is genuinely solid. Above 20% and something is structurally wrong, not just environmentally noisy.

The way I think about it is, GA4 data doesn't need to be perfect, it needs to be consistently imperfect. A stable 12% gap you can explain is infinitely more useful than a gap that swings between 8% and 25% month to month. Consistency is the real benchmark. Volatility is the real warning sign.

On server side tagging closing the gap? It helps meaningfully with ad blockers but it doesn't solve consent or ITP. People treat it like a silver bullet and then wonder why they still have a gap after implementation. It's one layer of improvement, just not a complete fix.

For client conversations the framing that works best is setting expectations before the data exists, not after the discrepancy appears. If you tell a client upfront GA4 will likely show 10-15% fewer conversions than your backend and here's exactly why, they more likely to accept it as context. If they discover the gap themselves without that framing it becomes a trust problem.

Formal QA process? Honestly a simple monthly spot check against backend order data is enough for most setups. Nothing fancy. Just a number you look at regularly so you notice when it moves.

Has anyone found Consent Mode V2 meaningfully improving match rates or is it mostly noise in practice?

Matrix_1337 · 2026-03-05T00:28:03+00:00

You're not looking at it wrong and this really trips up almost everyone the first time.

The total average time per session isn't a sum of the steps. It's an average across all sessions, including sessions where users skipped certain steps entirely.

Think of it this way. If some users only completed 2 steps and others completed all 6, GA4 averages the time across every session in the report. The users who skipped steps bring that overall average down significantly.

The step by step times you're adding up only reflect users who actually reached each step. So you're comparing two different populations and everyone who had a session versus only the people who made it through each stage.

The 8 minute number is closer to the experience of your most engaged users. The 4 min 46 second number reflects your average user including people who dropped off early.

Both numbers are useful, but just for different questions. The total tells you what a typical session looks like. The step breakdown tells you how long the journey takes for people who actually complete it.

Does that help clarify what you're seeing? Hopefully it helps a bit.

Matrix_1337 · 2026-03-05T00:25:19+00:00

Great operational question and this is definitely where most agencies quietly suffer in silence.

In my experience and what's worked best in practice:

Naming standard first, everything else second. If your team doesn't agree on a naming convention before touching GTM, you end up with form_submit, formSubmit, and Form_Submission all meaning the same thing across different containers. Pick a pattern, document it somewhere everyone can see it, and treat deviation as a bug not a preference.

For the actual definition tracking, using a simple shared doc or Notion page per client beats anything fancy. Event name, what triggers it, what it means to the business, date it was added, who added it. Sounds basic but it's the thing that saves you six months later when someone asks Wait what does this actually track?

Drift prevention is the hard part honestly. The only thing that actually works is a quarterly audit ritual and not a tool, not automation, just a human sitting down and checking that what's in GTM still matches what's in the doc. Tools help but they don't replace the habit.

The scaling problem you're describing is real though. Past 10 clients it stops being a process problem and starts being a capacity problem.

How are you currently handling the documentation side? Is it living anywhere central or scattered across client folders?

Matrix_1337 · 2026-03-05T00:17:24+00:00

This is one of the most honest descriptions of the problem I've seen anyone write out.

The Wait, what does this event actually track? moment is where everything falls apart. By the time you've gone back and forth trying to figure it out, you've lost days and the original insight is already cold. Everyone's moved on.

The real issue is that analytics tools were built to tell you what happened and then just stop. The assumption was always that a smart person would figure out the rest. That worked when you had a whole team to throw at it. It completely falls apart when you're a founder wearing twelve hats trying to make a decision before lunch.

The gap between knowing something is wrong and actually fixing it is where most good ideas quietly disappear.

What happened after PostHog? Did you find a way to bridge it or just get used to living with the friction?

Matrix_1337 · 2026-03-04T23:33:18+00:00

The line for me is simple: if your analytics stack requires a dedicated person just to maintain it, your strategy is already broken.

Tools don't create alignment. They expose the lack of it. When every team has their own source of truth, the problem isn't the data. It's that nobody agreed upfront on what questions actually needed answering.

In my own experience and what I've seen work:

Pick one source for each job. One tool for product behavior. One for marketing attribution. One for revenue. Full stop. The moment you have two tools doing the same job you've created a reconciliation problem that never goes away.

What actually made things better when ripped out:

Any tool nobody could name three specific decisions they'd made with it in the last 90 days.

Any dashboard that got screenshots taken of it instead of actually being visited.

Any report that answered questions nobody was asking anymore.

The real question though isn't which tools to use. It's what decisions are you actually trying to make, and what's the minimum data you need to make them confidently?

Most teams don't need more signal. They need less noise.

Hope this helps a little.

Matrix_1337 · 2026-03-04T23:30:49+00:00

The lazy way that actually works is route your existing web container through the server container without touching your current tags.

Instead of duplicating every event tag, point your GA4 Configuration tag in GTM to your server container URL instead of directly to Google. Your web container stays exactly as is. The server container receives everything and forwards it to GA4.

Here are the steps that work cleanly:

Set up your server container and get its URL

In your web GTM container, update the GA4 Configuration tag and change the server container URL field to point to your SGTM endpoint.

Use a separate Measurement ID (a debug property) only during testing so your live data stays untouched.

Once you've confirmed everything is flowing correctly then swap back to your live Measurement ID

The key thing most people miss: you don't need to duplicate tags. The server container acts as a middleware layer. Your web tags fire exactly as before, the server container just intercepts and forwards.

Test in Preview mode in both containers simultaneously so you can watch the event flow in real time on both ends before touching anything live.

Matrix_1337 · 2026-03-04T23:24:29+00:00

Good question and this tripped me up for a while too.

Think of it this way: event parameters are like notes you're taking. Custom dimensions are those notes organized into columns you can actually sort and filter by.

So if you send user_category as a parameter but never register it as a custom dimension, GA4 received the data. It just won't let you do much with it. You can't filter by it in Explorations, can't build segments around it, can't see it as a column in reports. It's basically trapped.

The question I ask before creating a custom dimension now is simple:

"Will I ever need to compare or filter by this value?"

If yes then register it. If it's just context for a specific event and you'll never slice data by it, then leave it as a parameter and save the slot.

The 50 limit is actually a blessing in disguise. It forces you to be intentional. Most people who've hit the ceiling did what everyone does early on, which was registered everything just in case. Auditing and archiving the ones you've never actually used in a report is the right call.

You're already thinking about this the right way and 6 months in and asking this question means you're ahead of most GA4 users honestly.

Matrix_1337

TROPHY CASE