r/SaasSelection

I've been comparing marketing data platforms for SEO, paid acquisition, attribution, reporting, and growth analytics.

Some of the main options:

• Windsor.ai: Marketing attribution & data integration
• Fivetran: Data integration & ELT
• Supermetrics: Marketing reporting & data connectors
• Rivery: Data pipelines & integration
• Adverit: Enterprise marketing analytics
• Funnel: Marketing data & performance analytics
• Improvado: Marketing data aggregation
• Airbyte: Open-source data integration
• Hevo Data: Automated data pipelines
• Dataddo: Data integration
• Coupler.io: Marketing reporting
• Dataslayer: Marketing data reporting
• Matillion: Cloud data integration
• Meltano : Open-source ELT
• Stitch: ETL/ELT

For growth marketing, I'd mainly compare them on:

• SEO + paid media reporting
• GA4 + Search Console integration
• Cross-channel attribution
• Customer acquisition tracking
• Ecommerce/revenue data
• Automated reporting
• BI and dashboard integrations

My rough categories:

Marketing attribution: Windsor.ai
Marketing reporting: Supermetrics / Funnel
Enterprise analytics: Adverity / Improvado
Data pipelines: Fivetran / Rivery / Hevo
Open source: Airbyte / Meltano
Simple reporting: Coupler.io / Dataslayer

For growth marketers who've actually used these tools:

Which one are you using, and what made you choose it over the alternatives?

One answer only — the first thing that comes to mind, no overthinking. Trying to collect a real list, not a wishlist of big ideas.

For example, assuming an API behaves a certain way, misunderstanding an existing utility/dependency, or getting a business rule wrong.

How do you currently catch these assumptions before the agent makes changes?

I'm specifically interested in the cases where the agent sounds completely confident but is actually wrong.

Cursor, Claude Code, Codex, Copilot, etc. can handle a lot now, but I'm curious what developers still find unnecessarily manual.

Could be anything—context, debugging, reviewing generated code, managing prompts/rules, testing, documentation, switching between tools, whatever.

What's something you find yourself doing repeatedly that you wish the AI/tool handled better?

Working on a few AI products solo and trying to find real problems worth solving instead of building in a vacuum.

If you work in coding tools / event ops / edtech (or anything really) — what's something you do manually every week that you wish was automated, and why hasn't an existing tool fixed it for you?

Not pitching anything, just doing research before I build.

Looking back, what’s one decision you overthought that ended up not mattering much?

Or the opposite, something you barely thought about that turned out to be really important.

Curious where founders actually end up wasting the most time.

When I’m debugging or building something from a webpage, I sometimes end up jumping between the browser, DevTools, screenshots, console errors, source code, and my AI coding assistant.

I’m curious how other developers handle this.

Do you have a workflow or extension that makes it easy to send the right context from a webpage to Cursor, Claude Code, Copilot, ChatGPT, etc.?

What still feels unnecessarily manual or annoying?

I currently run 6 operational micro ai saas products that generate a little over $20k in monthly recurring revenue.

I hardly wrote a single line of traditional code. i used ai to generate literally everything, from the database architecture to the user interface.

it wasn't magic on day one. i spent hours stuck in endless debugging loops and dealing with faulty ai code before i finally cracked the formula.

it basically comes down to three rules:

- keeping the idea aggressively minimalist (build a true mvp, not a platform).

- guiding the ai step-by-step instead of asking it to build the whole app at once.

- launching fast to get real user traction instead of perfecting features in secret.

lately, i've seen way too many non-technical founders give up at the very first ai bug or deployment error. or the worst, give up without push anything in marketing !!!!

it's a massive shame, because the technical barrier to entry has practically disappeared and the marketing is easy in 2026

because of this, i’m launching a skool community to share my exact method.

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific prompt sequences, n8n workflows, and copy-and-paste templates i'll be sharing.

but right now, our main objective is simply to build together. working alone in a silent corner is the absolute fastest way to quit.

if you want to join a group of active creators and build or launch your own ai saas: drop a comment below or send me a dm, and i’ll send you the invite link.

I’m about to start building my first SaaS and want to avoid the common beginner mistakes.

I’ve spent time researching problems, talking to people, and learning what makes a product worth paying for, but I know there’s a lot I don’t know yet.

If you’ve built a SaaS (successful or not), I’d love to hear:

1. What’s one mistake you wish you’d avoided?
2. What would you do differently if you were starting today?
3. What’s something beginners focus on that doesn’t actually matter?
4. What’s one thing that made the biggest difference for you?
   Any advice, lessons, or resources would be greatly appreciated. Thanks!