Trusted by 100+ marketing teams
Make budget decisions to hit your financial target
Measure, simulate and forecast future scenarios to learn at each moment what budget decision is going to get you closer to your financial target.
Book a demo
incrementality.cassandra.app/dashboard
Q2 2026 · Revenue target tracker
7d
30d
90d
YTD
Revenue target
€4.2M
Annual commitment
Current trajectory
€3.8M
€400K gap to close
Confidence
78%
↑ 6% vs last month
Best next move
+€30K Meta
+€95K incremental rev
Path to target
The problem
Will your marketing budget hit your revenue target?
You can't answer that with confidence today. That's the problem.
You committed to a number. Every budget move is supposed to close the gap to it. But you don't know which moves do — so half of them might be widening the gap instead. And the cost of being wrong compounds every quarter you stay uncertain.
Year-end forecast
Will you hit €4.2M?
Unknown
Recent budget moves · Did they help?
+€40K → Meta Ads
?
−€25K ← TV
?
+€15K → TikTok
?
Trusted by leading marketing teams
What's at stake
Scattered measurement is what freezes you.
You run MTA daily, MMM quarterly, geo experiments when you can. Different cadences, different methods, no convergence on a single answer.
Instead of clarity on where to spend next — you freeze.
Meta Ads — what should you do?
Same channel. Three methods. Three answers.
MTA
Multi-touch attribution
Updated daily
4.2x
↑ Spend more
MMM
Marketing mix model
Updated quarterly
1.8x
→ Hold steady
GEO
Geo lift test
Last run: 6 weeks ago
0.9x
↓ Cut spend
Spread between methods
0.9x
3.3x spread
4.2x
⏸︎
Decision this week: Frozen.
The solution
Cassandra closes the gap between investments and financial target → every-day
By triangulating MMM, incrementality testing, and attribution — and accounting for the uncertainty in each — Cassandra tells you where and when to invest to maximize the probability of hitting your financial target.
incrementality.cassandra.app/target-tracker
Target tracker · Q2 2026
Updated today, 8:02 AM
7d
30d
90d
YTD
Revenue target
€2.98M
/
€4.2M
Trajectory
€3.8M
Gap
−€400K
Confidence
78%
Now · 71%
Projected · 90%
€0
€1.05M
€2.1M
€3.15M
€4.2M
Channel
Attributed ROAS
MMM ROI
Exp ROI
Consensus
Action
4.2x
2.1x
2.4x
2.3x
High
↑ Increase +€30K
6.1x
2.9x
1.9x
2.0x
Medium
→ Hold
0.3x
3.2x
2.8x
3.0x
High
↑ Increase +€50K
2.8x
2.6x
1.1x
1.0x
Low
↓ Reduce -€20K
3.5x
1.4x
—
—
Test needed
⚡ Run test
Measures progress daily
Not at quarter-end. Every morning, against your target.
Reallocates when you drift
Off pace? Cassandra suggests the specific move that puts you back on.
Shows you why
Every suggestion comes with the evidence behind it. Defensible to your CFO.
Evidence-based planning
We only optimize what we can prove. We test what we can't.
Cassandra scores every channel based on risk and efficacy — not just ROAS. The score accounts for convergence across MMM, incrementality testing, and attribution; model stability across retrains; and the width of the confidence interval.
Channels with high scores get optimized. Channels without enough evidence get experiments automatically designed to calibrate their measurement — reducing risk before budget moves toward them.
Every budget decision is backed by evidence. Every gap in evidence triggers an experiment.
Evidence
Display Video Awareness
Score
84
Summary
Low uncertainty — CI (1.58x — 2.20x)
Slight under-attribution — measured 1.89x vs MMM 1.72x (10% error)
Stable — iROI consistent across retrains
Convergent — measurement methods agree
Uncertainty
Low uncertainty
MMM iROI confidence interval
1.58x
1.89x
2.20x
Calibration
Slight under-attribution
MMM prediction vs experiment results
Predicted
Measured
Error
1.72x
1.89x
10%
Stability
Stable
Trained on 5 time windows of your data — ROI estimates held steady. Safe to act on.
Convergence
Convergent
Agreement across measurement methods
Attribution ROAS
1.78x
MMM iROI
1.89x
Experiments (2)
Start
End
Type
Budget
Lift
iROI
95% CI
Apr 14, 2025
May 12, 2025
↑ Increase +30%
€64K
+22.4%
1.86x
[1.58 — 2.14]
Oct 06, 2025
Nov 03, 2025
↓ Decrease -25%
€52K
-19.8%
1.92x
[1.64 — 2.20]















