I built a behavioral analytics tool for app builders — here's why and how

Every solo developer hits the same wall eventually.

You ship something. You get users. Then one by one, they quietly disappear. No error in your logs. No angry email. Just silence.

That silence is the most expensive problem in SaaS and nobody talks about it enough.

The problem I kept running into

I'd build a feature, ship it, and have no idea if anyone actually used it. I'd watch my user count slowly decline and have zero visibility into why. Was it the onboarding? A confusing page? A feature nobody wanted?

The tools that exist to solve this (Mixpanel, Amplitude, FullStory) are powerful but they require you to already know what questions to ask. You have to build funnels, define events, set up dashboards. That's a full-time job.

I didn't want a tool that gives me more data to interpret. I wanted something that just tells me what's wrong.

What I built

AppScope is a behavioral analytics platform with an AI insight layer on top. The idea is simple: drop in one script tag, and within 48 hours the platform tells you what's broken in plain English.

No funnels to configure. No dashboards to build. It just watches and reports.

Here's what it detects automatically:

• Churn risk — users who never completed onboarding and have gone silent
• Friction points — pages where users leave within 60 seconds of arriving
• Onboarding drop-off — exactly which step loses the most users
• Dead features — features that less than 5% of users ever touch

Then it passes those detected patterns to an AI layer that narrates them like a product analyst would: "Step 2 is your biggest drop-off point, losing 4 users — double the drop at any other step. Review what's being asked there and either simplify it or defer it until users experience core value first."

The tech stack

• Backend: FastAPI + PostgreSQL (hosted on Render)
• SDK: Vanilla JavaScript — no dependencies, ~5KB
• AI layer: Claude API for insight narration
• Frontend: Plain HTML/CSS dashboard (hosted on Netlify)

I deliberately avoided heavy frameworks. The SDK needs to be something any developer can drop into any app without worrying about bundle size or conflicts.

Installation

<script src="https://your-appscope-url.netlify.app/appscope.js"></script>
<script>
  AppScope.init({
    apiKey: 'your-api-key',
    userId: currentUser.id, // optional
  });
</script>

That's it. Drop it before </body> and you're live.

For tracked elements, just add data attributes:

<button data-as-track="upgrade_button" data-as-feature="billing">
  Upgrade
</button>

What I learned building this

The hardest part wasn't the tracking or the AI — it was the low data problem. A new client with 50 users doesn't have enough signal for meaningful churn prediction. So the system has two modes: descriptive insights for low-volume apps (drop-off rates, dead features) and predictive insights as data accumulates.

The second thing I learned: founders don't want more data. They want someone to read the data for them. That insight shaped everything about how AppScope presents information.

Try it

I just launched the beta. It's free, no credit card required.

👉https://shiny-malasada-5a8b3a.netlify.app/register.html

Looking for feedback from developers and founders who've dealt with silent churn. What would you want a tool like this to tell you?

Comments

[PubliFlow]

Thanks for the detailed response, Glen! Session-based grouping is a smart interim solution — it handles the most common noise patterns without over-complicating the pipeline.

On the server-side SDK roadmap: one thing I've seen work well is offering a lightweight HTTP endpoint that accepts batched events. That way, even if the JS snippet gets blocked, backend integrations (Node, Python, Go) can still feed data in. Umami does something similar and it's been effective for privacy-conscious users who still want analytics.

I'm building a SaaS starter kit myself (PubliFlow) and one of the trickiest parts was deciding whether to bundle analytics or let users plug in their own. Curious — would you consider offering a self-hosted version at some point? That could be a nice complement to the hosted tool.

Keep building! 🚀

[PubliFlow]

This is a solid approach to analytics. I found that the biggest challenge with behavioral analytics is filtering signal from noise — we ended up implementing event deduplication and session-based grouping which dramatically improved our insights quality. One thing worth exploring is using server-side event collection instead of relying purely on client-side tracking, as it avoids ad blocker interference and gives you more reliable data. What stack are you using for the real-time processing pipeline?

[Glen Modiba]

Thanks for the insights, event deduplication is something I've already thought about as a next step, especially as data volume grows. Right now I'm using session-based grouping on the backend which helps with the noise problem, but you're right that deduplication at the event level would improve insight quality significantly.
The ad blocker point is well taken. Currently client-side only, but server-side collection is on the roadmap , the plan is to offer a backend SDK alongside the JS one so server events (signups, payments, subscription changes) can be piped in directly, which also solves the ad blocker issue entirely.
Stack is FastAPI + PostgreSQL for ingestion and storage, vanilla JS SDK on the client side, and Claude API for the AI narration layer. No real-time pipeline yet — currently batch analysis on demand, but moving toward scheduled analysis as the user base grows.
What stack did you end up using for your real-time processing