PipeValue's answer

PipeValue closes the loop between your CRM and your ad platforms. It reads the real € value of every lead — from who actually converts and how much they're worth — and streams it back to Google, Meta and LinkedIn in real time. So instead of optimizing for the cheapest form-fills, your campaigns learn to bid on the leads that generate revenue. No data team, no server-side pipeline to maintain: connect your CRM (or drop a CSV) and go live in minutes.

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.

PipeValue's answer

Most attribution tools stop at reporting — they tell you what happened. PipeValue acts on it: it sends real revenue values back to the ad platforms so bidding improves automatically, and it proves the incremental lift so you can see the impact. It's built for speed (one-click OAuth, AI field mapping, no data team), runs across Google, Meta and LinkedIn from a single value model, and keeps data secure with an encrypted token vault and EU data residency. Same ad budget, more revenue.

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.

PipeValue's answer

Performance marketers, growth teams and lead-gen agencies running paid acquisition (Google, Meta, LinkedIn) on top of a CRM like HubSpot or Pipedrive — especially where lead quality varies a lot and a €50k deal shouldn't look the same as a tire-kicker to the algorithm. Agencies use it to run value-based bidding across many client accounts from one place.

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."

PipeValue's answer

PipeValue was born from a simple frustration: ad platforms optimize for whoever fills a form the cheapest, while the leads that actually build a business get ignored. Teams knew their best customers were hiding in their CRM, but feeding that signal back to the platforms required a data-engineering project most couldn't afford. PipeValue turns value-based bidding into a one-click product — connect, score, send, prove the lift — so any team can make its ad budget chase revenue, not clicks.

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.

PipeValue's answer

PipeValue runs on modern, secure cloud infrastructure with EU data residency and a per-tenant encrypted token vault. It integrates through official server-side conversion APIs — Google Enhanced Conversions / Data Manager, Meta Conversions API and LinkedIn Conversions API — and connects to CRMs via OAuth. The value engine scores every lead in real time from your closed-won outcomes.

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.