PostHog VS mbuzz.co
Compare PostHog VS mbuzz.co and see what are their differences
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For developers just starting out, PostHog is a free way to understand how your product is being used, without having to send any data to 3rd parties.
For enterprise customers, one data security becomes a key concern, or B2C businesses where using a SaaS solution is unaffordable, it's typical to see teams hosting an event capture platform, a data lake, and sophisticated analytics tools. The end result is that data scientists are needed and most developers don't have easy access to product intel. PostHog solves that gap - it lets everyone understand how your product is being used, without having to send data to 3rd parties, even once you have scaled to millions of visitors.
It has a JS snippet that can autocapture events, and pre-built libraries to push backend data to. Build up full user histories, visualize product trends, funnels, and run experiments with new features.
mbuzz is multi-touch attribution for technical marketers who've stopped trusting their dashboard. Here's the thing nobody selling you attribution wants to say out loud: every tool runs a model under the hood, and the number it reports isn't "the data." It's that model's opinion of the data. Same touchpoints, different model, completely different "best channel."
mbuzz runs eight of them at once. First-touch, last-touch, linear, time-decay, position-based, Markov, Shapley, data-driven. You can compare them, argue with them, and write your own in a SQL-like DSL if none of the stock eight fit how your business actually works.
PostHogPostHog is particularly well-suited for product teams, developers, and startups that require deep insights into user interactions and need the flexibility of a self-hosted solution. It is also a good fit for organizations that prioritize data privacy and want to maintain full control over their data.
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mbuzz.co's answer:
Every attribution tool runs a model under the hood and reports its number like it came from physics. mbuzz is the only one that shows the model. Eight of them side by side, plus a SQL-like DSL to edit or write your own. You stop arguing about which channel works and start arguing about which model you should trust.
mbuzz.co's answer:
Dreamdata, HockeyStack, and Northbeam all ship with a proprietary "data-driven" model you can't see inside. You pay $1,400โ$5,000 a month to trust their math. mbuzz runs eight models you can inspect, lets you edit the logic in a SQL-like DSL, keeps your data exportable on every plan, and starts at $0. For a $1โ100M company spending $20Kโ$1M a month on ads, that's the difference between renting an attribution tool and owning an attribution stack.
mbuzz.co's answer:
Technical marketers, marketing ops, growth engineers, and data-savvy CMOs at startups and mid-market SaaS, DTC, fintech, and healthtech companies spending $20Kโ$1M a month on paid media. Specifically the ones who've stopped trusting their dashboard โ who want to audit the math themselves, not hear "trust our algorithm."
mbuzz.co's answer:
Years of wrestling with the limitations of various existing solutions, platform-inflated ROAS, and enterprise attribution tools that cost more than the budgets they were measuring. Every tool I tried picked one model and hid the math. I wanted to compare models, argue with them, and write my own rules โ so I built one. mbuzz is the attribution platform I wished existed when I was trying to explain channel performance to a CFO who didn't believe the Meta pixel.
mbuzz.co's answer:
Ruby on Rails (backend + dashboard), PostgreSQL, Sidekiq for background jobs, Stimulus/Turbo for the frontend. Open-source SDKs in Ruby, Node, Python, and PHP. Deployed via Kamal on DigitalOcean.
Based on our record, PostHog seems to be more popular. It has been mentiond 72 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Opus, zero nudges. Realised on its own that an abandoned signup never fires identify, triangulated the anonymous session from time, platform and registration events, decoded the PostHog replay blobs, confirmed the duplicate account in Supabase, proved the reset email never sent, and pulled the root cause out of an unmasked DOM field. One prompt in; root cause out. - Source: dev.to / 11 days ago
This is the same model that PostHog, Supabase, and dozens of other developer tools use. Open core, with a managed offering on top. - Source: dev.to / 4 months ago
Offchain: Website traffic, in-app behaviour, marketing channels, growth campaigns (Google Analytics or PostHog). - Source: dev.to / 3 months ago
--- Title: "Validate Your Startup Idea in One Weekend: Next.js + PostHog + Stripe Test Mode" Published: true Description: "A step-by-step workshop for wiring up a landing page with analytics, a waitlist, and Stripe test-mode checkout to measure real willingness-to-pay before writing product code." Tags: typescript, api, architecture, cloud Canonical_url:... - Source: dev.to / 3 months ago
Topic PostHog (Web Vitals) Apogee Watcher Primary job Product analytics OS; Web Vitals are real-user metrics from the browser Synthetic PageSpeed monitoring + CrUX in results Instrumentation Requires posthog-js on the site No script on monitored sites Metrics FCP, LCP, INP, CLS from real sessions ($web\_vitals) when capture runs Lighthouse lab + CrUX (where available) via PSI Cookieless analytics With... - Source: dev.to / 4 months ago
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