An sGTM container on a subdomain you own becomes the single intake. Server-to-server forwarding to every ad platform after that. Recovers the 20 to 40 percent of conversions browser pixels quietly drop.

CAPI feeds for every social platform. Server-side events deduplicate against the browser pixel, and the bid algorithms learn from the full conversion set, not the half that survives iOS and Safari.

Hashed email and phone returned to Google with every conversion. Smart Bidding gets back the identity signal Chrome's third-party cookie deprecation took away.

A clean GTM and sGTM container as the discipline layer. Documented triggers, governed variables, and a deprecation log so the tag stack stays auditable instead of accreting fragile pixels over time.

Stage-weighted conversion events from Salesforce, HubSpot, and Pipedrive imported back into Google and Meta. The platforms optimize for SQLs and closed-won, not the form fills that never close.

When closed-won is too slow or too sparse to bid on, we engineer leading indicators that correlate with revenue. Calibrated proxies become the conversion event, so the algorithm gets fast, reliable signal without lying about what it represents.

Not every conversion is worth the same. Stage probability, deal size, and segment value collapse into a single weighted revenue figure that bidding, forecasting, and reporting all share. The numbers finally agree.

Salesforce, HubSpot, and Pipedrive cleaned up at the stage and field level so marketing source data, lead status, and revenue close the loop without manual reconciliation. The CRM stops being a measurement liability.

BigQuery as the source of truth. Fivetran piping ad spend, CRM, and product analytics in on a schedule. Marketing data sitting next to the data finance and product actually trust.

Built from scratch in Looker Studio, not template imports. Every chart traces back to a BigQuery query you can audit, and every dollar reports against pipeline and closed-won.

Model comparison, signal density review, and an honest read on what your current attribution is and isn't telling you.

Position-based, time-decay, and data-driven models reconciled against geo lift and MMM reads. MTA stops being a single number and becomes a directional input alongside causal measurement.

Designed-for-channel incrementality tests. Causal reads on whether the spend is actually moving pipeline, not just correlating with it.

Power analysis, holdout sizing, and pre-registration before the spend moves. CausalImpact and Bayesian structural time series for the read. The test is designed to answer one question cleanly, not generate a number that confirms what we already wanted to believe.

Robyn-based MMM with adstock, saturation curves, and budget allocation outputs. Built at blackbirdmmm.com and tuned per account, so the top-down read on channel impact agrees with the bottom-up bidding data.

Spend decile cuts on every channel reveal where the next dollar earns above target and where it stops. Budget moves to the slope of the response curve, not the average.

Behavioral clustering over warehouse data. Real customer segments built from usage, revenue, and engagement patterns, fed back to platforms as high-value audience signals.

Predicted lifetime value modeled from cohort behavior, not platform proxies. CLV thresholds become bid signals so the algorithm spends against customers who actually pay back.

Recency, frequency, monetary scoring across the full customer base. Surfaces who to nurture, who to win back, and where re-engagement budget should land.

Time-to-conversion modeling by channel, segment, and offer. Identifies which spend closes in seven days and which takes ninety, so attribution windows and forecasts match how customers actually buy.