Acme Beauty/Revenue
Revenue over the last 30 daysApr 2026
Mar 8Mar 18Mar 28Apr 7
Grow your brand
on trusted signals.
Adwize connects trusted marketing data, custom models, and trained agents in one governed system.
Acme Beauty/Collection health
Purchase event volume Expected Actual
Apr 1Apr 5DeployApr 14
Acme Beauty/Revenue forecast
Total revenue30 days actual · 15 days ahead
Mar 20Apr 4NowApr 26
Acme Beauty/Recommendations
Paid mediaPending review
Test the high-intent audience against broad targetingPrimary: ROAS · Evaluation: 10% holdout · Confidence: calibrated
LifecyclePending review
Protect margin in the welcome series for high-value subscribersPrimary: Margin · Guardrail: unsubscribe rate · Audience: 8,420
Growth leadNeeds input
Fix the attribution gap before the next budget cycleMonitoring found missing campaign fields on 12% of purchases
One connected system
From raw signals to better growth decisions.
Each layer informs the next. Inputs, models, playbooks, and outputs stay visible so teams can move quickly without creating another black box.
01
Observe
Connect the tools and data you already use to run the business.
02
Resolve
Connect identity and journey signals across sources.
03
Model
Train scores and forecasts for the business problem.
04
Reason
Give specialist agents context, playbooks, and constraints.
05
Act
Review recommendations and prepare governed actions.
The platform
One place to see what happened and decide what comes next.
Move from trusted signals to custom intelligence and trained recommendations without losing the context behind the decision.
Analytics
Keep the business in view with the KPIs marketing already runs on.
You define the KPIs to follow. We keep the set small and focused on the relevant ones, so the team can move from those numbers to the next action.
Growth control room
Bring the numbers the team already uses into one place, then move into monitoring or a model without losing context.
Monitoring
Catch the silent break before it reaches reporting.
See event volume against its expected range, then move from the alert to the field, deploy, or rule that caused it.
Collection health
The expected range stays visible, so a drop is diagnosed as a tracking break, not a change in demand.
Intelligence
Model the decision, not another dashboard.
Configure a proven pattern or build a custom model, then evaluate it against a baseline or holdout.
Scenario evaluation
Compare a growth scenario to baseline and keep the decision range visible before anyone changes spend.
Reasoning
Train agents on your growth problem, then review the next move.
Specialists read your context, models, and playbooks, then hand the team a recommendation instead of a generic suggestion.
Reviewable next move
Each recommendation comes with the primary metric, the evaluation, and the constraint it has to respect.
Open at the foundation
Inspect it. Run it. Build on it.
For some teams, the foundation layer behind collection and modeling is sufficient and public on GitHub. It's free, it's flexible. And if you start using it, a star will make our day.
Let’s look at your brand first.
See what your tracking is missing. Then 20 minutes with us on what comes next.