AttributeIQ's answer

Most attribution tools measure marketing activity in isolation from revenue; AttributeIQ verifies attribution against actual deal stage, amount, and outcome, and supports First-Touch, Last-Touch, and Multi-Touch models within the same dataset so teams aren't locked into one framework.

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.

AttributeIQ's answer

AttributeIQ is built for B2B teams running GA4 and HubSpot who need credible attribution without a data warehouse, a dedicated analytics engineer, or months of implementation. Attribution data appears within 24 hours of connecting sources, and reporting includes board-ready exports so marketing can walk into a leadership meeting with pipeline, revenue, and channel figures already assembled, instead of reconciling numbers across spreadsheets the night before.

mbuzz.co's answer:

Dreamdata, HockeyStack, and Northbeam all ship with a proprietary "data-driven" model you can't see inside. You pay $1,400–$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 $1–100M company spending $20K–$1M a month on ads, that's the difference between renting an attribution tool and owning an attribution stack.

AttributeIQ's answer

AttributeIQ is built for B2B SaaS marketing teams, typically Heads of Content, Marketing Ops, and CMOs, who already run GA4 and HubSpot and need to prove which content and channels drive pipeline and revenue. It fits companies with an active but lean marketing function (roughly 2 to 200 employees) that need defensible attribution reporting without the headcount or infrastructure enterprise attribution platforms assume.

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 $20K–$1M a month on paid media. Specifically the ones who've stopped trusting their dashboard — who want to audit the math themselves, not hear "trust our algorithm."

AttributeIQ's answer

AttributeIQ was founded by Muiz Thomas out of firsthand frustration doing B2B SEO consulting through his agency, GrowUp, where proving which content actually influenced closed deals was consistently the hardest question to answer credibly for clients. That gap, between marketing activity and verified revenue outcome, became the reason for building the platform.

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 — 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.

AttributeIQ's answer

AttributeIQ is built on Next.js for the application layer, Supabase for backend infrastructure and authentication, and BigQuery to ingest and store raw, unsampled GA4 event data at scale. Billing runs through Stripe, and the platform integrates with HubSpot via OAuth for CRM data and Slack for real-time alerting.

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.