How We Measure — Performance Analytics

Phase by phase

The full measurement build.

Phase 01 · Weeks 1–3

Connect

API ingestion across all your platforms, server-side events, identity stitching, warehouse setup.

Phase 02 · Weeks 3–6

Model

Data modelling, attribution logic, custom KPIs, dimensional consistency across channels.

Phase 03 · Weeks 5–8

Visualize

Role-based dashboards: exec, channel-lead, performance, finance.

Phase 04 · Weeks 8–12

Forecast

Predictive models, scenario planning, budget optimization with confidence intervals.

Phase 05 · Continuous

Optimize

Anomaly alerts, recalibration, quarterly board readouts, model retraining.

Phase 01 — Connect

The plumbing. Server-side events, identity stitching across cookies + emails + IDs, warehouse setup if you don't have one yet.

6–12 source-system ingestions
Server-side event collection (CAPI, GA4, etc.)
Identity stitching across IDs
Warehouse provisioning (or use existing)
Consent-aware payloads + privacy review

3 weeks

Phase 02 — Model

The semantic layer. Data modelling in dbt or equivalent, attribution logic, custom KPIs, dimensional consistency.

Star schema across marketing entities
dbt-tested data quality
Multi-touch attribution logic (3 models)
Custom KPI definitions, documented
Dimension consistency audit

3–4 weeks

Phase 03 — Visualize

Role-based dashboards. Exec sees direction. Channel leads see optimization. Finance sees attribution. Ops sees operations.

Exec scorecard (5 KPIs, daily refresh)
Channel-lead dashboards per platform
Finance / FP&A attribution view
Ops / monitoring dashboard
Mobile-friendly views

3 weeks

Phase 04 — Forecast

Predictive models with confidence intervals. Scenario planning. Budget optimization based on marginal ROI per channel.

Revenue forecast model + CIs
Scenario planning UI (3 levers)
Marginal-ROI budget optimizer
Holdout-validated accuracy report
Forecast vs actual review cadence

4 weeks

Phase 05 — Optimize

Now the system runs. Anomaly alerts, retraining, QBRs, continuous improvement.

Statistical anomaly alerts (Slack)
Monthly model retraining
Quarterly business review
Annual data architecture review
Continuous improvement backlog

Continuous

Build-as-code, owned by you.

All ingestion, modeling and dashboarding lives in your repository. dbt models, SQL, Looker / Mode / Tableau definitions — everything reviewable, version-controlled, transferrable. If we ever part ways, your team owns the entire system.

0

In your repo

No vendor lock-in, ever.

0

Iteration cadence

You see value every two weeks.

Stack

Tools we run with.

Snowflake / BigQuery

dbt

Looker / Mode

Segment / Rudderstack

OneTrust consent

Anomaly alerts (custom)

Slack notifications

HubSpot + Salesforce

Quality bar

SLAs you can hold us to.

15-min latency

From event to dashboard.

92% forecast accuracy

Trailing 4 quarters.

SOC2-friendly

Consent + privacy patterns built in.

30-day off-board

Documented + supported handover.

FAQ

Process questions.

Yes — almost always. We accelerate the marketing-analytics roadmap inside your team's existing standards (PR review, dbt project, deployment pipeline).

Yes. Server-side events with consent payloads, hashed identifiers, EU-region warehouses where required. Shipped GDPR + CCPA + DPDP-compliant stacks.

We provision Snowflake (recommended for marketing) or BigQuery in week 1 as part of Phase 01. Cost lands around $1.5K–$5K/mo depending on volume.

Attribution, MMM, incrementality.