mbuzz.co

What it is

mbuzz is a server-side multi-touch attribution tool for digital marketing teams spending roughly $10k–$1m per month on ads. Its core goal is to reconcile inconsistent ROAS definitions across Meta, Google, Shopify/actual revenue, or bank-side revenue, helping teams decide where to allocate budget. Compared with relying only on ad platforms’ self-reported data, mbuzz emphasizes independent attribution, server-side data collection, cross-device identification, and a closed loop for offline conversions.

Core capabilities

In terms of features, mbuzz lets users compare eight attribution models side by side, including first-touch, last-touch, linear, time decay, position-based, Markov, Shapley, and data-driven models, with support for a custom Attribution DSL. Spend Intelligence can analyze marginal ROAS, payback period, and channel saturation, while LTV Mode re-evaluates channels based on 12-month lifetime value. It also supports browsing individual conversion journeys, funnel events, and identity profiles, as well as sending closed deals from CRM, Stripe, or POS systems back via API to enable closed-loop attribution.

Data sources, integrations, and platform

mbuzz primarily relies on server-side SDKs/APIs to collect data such as visitors, sessions, events, conversions, UTM parameters, and referrers, and uses identify to link anonymous visitors with user IDs. It supports Ruby, Node.js, Python, and PHP SDKs, REST API, Server-Side GTM, and mentions integrations with frameworks such as Rails, Django, Laravel, and Node. On the ad integration side, Google Ads is already available, while Meta and LinkedIn are still listed as coming soon. All plans include CSV export, API access, and an MCP server.

Pricing and value for money

Pricing is fairly transparent: the Free plan includes up to 30K records/month; Starter is $29/month for up to 1M records; Growth is $99/month for up to 5M; and Pro is $299/month for up to 25M. Paid tiers have clear overage rates, with no per-seat pricing, no contracts, and no sales calls. Compared with tools mentioned in the text such as Dreamdata, Rockerbox, and Northbeam, mbuzz has a lower entry price, making it suitable for small to mid-sized teams that want to validate the value of attribution before committing further.

Pros, cons, and who it’s for

Its strengths are transparent models, flexible integration options, server-side collection that reduces data loss from ad blockers and browser privacy restrictions, and the ability to handle offline B2B deals. Its drawbacks are that ad platform integrations are still incomplete, the free tier does not pull ad spend, and server-side implementation requires engineering involvement. The public materials also do not specify SLA, support channels, or compliance certifications. mbuzz is best suited to growth, ecommerce, and B2B teams with some technical resources, meaningful ad budgets, and a desire to move from platform-reported ROAS to attribution based on actual revenue.

Access from China and alternatives

The text does not provide information on network accessibility from mainland China, payment methods, or localization support, so its China access status is rated as unknown. Domestic teams should test whether the console, API, documentation, and Google Ads OAuth flow are stable in practice, and also confirm whether mainland Chinese credit cards are supported. Comparable alternatives include GA4, Dreamdata, Rockerbox, Triple Whale, Northbeam, as well as commonly used data analytics and marketing attribution solutions in China.