The signal layer behind better measurement and maximum ROAS

No. Matching relies entirely on platform-side verification, not prediction or fingerprinting. When a user first lands on the site, parameters such as click ID, browser ID, user agent, and IP are captured and stored server-side. When the user later provides an email or phone number, this information is sent to Meta or Google, and the platform verifies it through its own identity system. The matching logic belongs to the ad platform's identity infrastructure, not to B2Metric.

No. GA4 is mainly used for analytics and reporting. SignalOne works to send cleaner, richer, and more reliable signals to GA4, ad platforms, CRM, or B2Metric itself. In short: GA4 reports, SignalOne collects, verifies, enriches, and routes the signal.

No, it doesn't need to replace MMPs for app install attribution. MMPs are strong at install, session, and app attribution. SignalOne adds a layer on top that connects app events with backend outcomes, CRM quality, LTV, churn risk, and segments. The question shifts from 'which campaign drove the install?' to 'which channel actually produced quality users and revenue?'

No, GTM is not required. SignalOne can be integrated through a web script/SDK, mobile SDK, backend API, server-side endpoint, webhook, existing data layer, or CRM/warehouse export. GTM can speed up setup if already in place, but direct web/app/backend integration works just as well without it.

No. SignalOne runs server-side; there is no SDK dependency. Ready-made integrations with BigQuery, Adjust, and AWS S3 are already available. All data flow is collected and managed from the B2Metric dashboard.

Each of these tools operates within its own scope; none of them unify all channels into a single server and automatically feed that back to ad platforms. SignalOne consolidates all signals into one pool, performs identity resolution, and automatically forwards these profiles to Meta, Google, and TikTok — automating what would otherwise be manual deduplication across the stack.

This gap comes from platforms using different attribution windows: Meta uses 7 days, Google uses 90 days. The same purchase gets credited by both platforms, pushing the combined total above actual revenue. Because SignalOne collects all click data on a single server, the attribution model (last-click, first-click, data-driven) is entirely up to the customer, removing dependency on platform dashboards.

SignalOne is built to work in line with the customer's existing consent setup. Depending on whether the user has given analytics, marketing, or only essential consent, the system determines which events are collected and where they are routed. Technical compatibility is assessed with consent providers such as OneTrust, Cookiebot, or similar tools. The core principle: SignalOne does not bypass the consent structure — it operates based on it.

Any measurement system can create risk if it isn't set up correctly. In a SignalOne implementation, consent, hashing, data minimization, and destination routing are the key topics addressed. The goal is not to collect data indiscriminately, but to collect and use permitted, business-justified signals correctly. Before setup, the events to be collected, the identifiers to be used, and how consent status will be read are all clarified in advance.

SignalOne doesn't need to carry raw PII directly; fields like email and phone are typically normalized and hashed. On the backend or CRM side, pseudonymous identifiers such as customer ID or lead ID can be used instead. The data type stored, retention period, location, and access structure are all defined together with the customer.

SignalOne is not a system that spends media budget directly; its impact comes from higher-quality signal, more accurate attribution, and better audience activation. Example contributions: connecting conversions that falsely appear as 'direct' back to their real source, identifying unnecessary brand search spend, retargeting high-intent users, and excluding existing customers from acquisition campaigns.

Many conversions appear as direct or brand search in the last session, even though the user was originally influenced by Meta, TikTok, or Google earlier on. SignalOne preserves the earlier source memory — UTM data, referrer, click IDs — so the true source at the time of conversion is retained. This makes it possible to see whether brand search spend is truly incremental and reduce unnecessary spend.

Not if it's set up correctly. In the initial phase, the existing setup is preserved and SignalOne runs in parallel as a test ('shadow mode'). Existing GA4, Meta Pixel, and Google Ads events are compared against SignalOne events, and the switch to production is done in a controlled way. In some setups, client-side tags remain in place while SignalOne operates as a server-side enrichment/deduplication layer.

Timelines vary depending on scope and access; simple web event tracking can be completed faster, while CRM/backend/consent/multi-platform integration extends the timeline. Access typically required includes GTM/GA4, Google Ads, Meta Business/Pixel, TikTok Ads, a CRM technical contact, backend event documentation, and consent management platform details.

It wouldn't be accurate to claim an immediate guaranteed performance boost. In the first phase, the goal is to fix signal quality. A more realistic expectation: measurement quality improves first, then platform optimization starts receiving better signal, and over time improvements in CPA, ROAS, and lead quality can be observed.