SCALE
HOLD
REDUCE
CUT
Advanced measurement and attribution for data-driven decisions
The tiles below are short, interactive simulations of the core problems we fix: how attribution models shift credit, how past holdout tests calibrate current MTA, why A/B tests reveal true incrementality versus correlation, and where returns saturate and effects decay. You’ll also find links to the causal methods toolkit and a membership-lift triangulation analysis.
PROBLEM
Your marketing team is fighting over credit. See how last-touch vs multi-touch changes everything.
📈PROBLEM
Is your growth real or just correlation? Interactive demonstration of confounded vs randomized data.
🎯PROBLEM
MTA shows 1,600 conversions. Past tests suggest 50% incrementality. Calculate true impact.
📉PROBLEM
When does more spend stop working? How long do effects last? Explore diminishing returns.
🔬TOOLKIT
DiD, ITS, PSM, Granger causality. Full econometric arsenal for rigorous causal inference.
💳ANALYSIS
Prove loyalty program lift. Triangulation across DiD, DoWhy, and OLS methods.
Turn attribution into confident budget decisions with clear, auditable attribution for confident allocation.
Reconcile walled gardens and see real contribution across channels.
| Channel | Claimed | ROAS |
|---|---|---|
| $75k | 3.00 | |
| $60k | 3.00 | |
| TikTok | $40k | 2.67 |
| Total Claimed | $175k | Overlap 75% |
| Channel | True | iROAS |
|---|---|---|
| $43k | 1.71 | |
| $34k | 1.71 | |
| TikTok | $23k | 1.52 |
| Totals | $100k |
Platforms like Facebook, Google, and TikTok can claim the same sale. Watch how their claims can add up to more than actual revenue. This is why calibration exists.
Prefer a step-by-step exercise? Open our simple walkthrough ↗
Use past holdout data to calibrate MTA and reveal true campaign impact.
| Last Quarter's Holdout Test (Ground Truth) | |
|---|---|
| Actual Conversions (from Test) | 600 |
| MTA-Attributed Conversions (during Test) | 800 |
| This Quarter's Attribution (MTA) | |
| Campaign A | 1,800 |
| Campaign B | 1,200 |
| Your Calibration Task | |
| Calibrated Campaign A | |
| Calibrated Campaign B | |
| Calibrated Total | |
You just saw how naive correlation analysis shows 17x ROAS, while proper A/B testing reveals only 2x true return. That's the difference between wasting millions and making profitable decisions. Most businesses are flying blind, mistaking correlation for causation.
When sales and ad spend both rise, it’s easy to assume causation. But is the growth real, or just correlation? This simulation reveals the expensive truth of confusing the two.
This view suggests a 17x return, justifying aggressive spending. However, it fails to account for customers who would have converted anyway.
By isolating a control group, the A/B test reveals the true return is 2x. This is a profitable, but vastly different, business case that prevents millions in misallocated budget.
Marketing Mix Modeling (MMM) quantifies diminishing returns (saturation) and carryover (adstock/decay) so you can invest with confidence. If your question is “what’s actually driving the business?”, MMM is the tool that answers it.
• Where each channel’s curve saturates.
• Half-life (or decay rate) of media effects.
• True channel contributions and base vs media split.
Validated model + code, a scenario planner, and a budget shift recommendation, aligned to your constraints (seasonality, floors/caps, brand vs performance).
We can run Meridian, Robyn, or PyMC-Marketing. For fast proofs, I can stand up a clean Excel version you can audit cell-by-cell.
Define KPI, align costs, de-dupe, and lock invariants.
Spin up a base model to surface obvious wins/risks.
Cross-checks, posterior predictive checks, and out-of-sample tests.
Optimize to constraints; simulate shifts and expected lift.
Code + docs + dashboard; optional training for your team.
Find where more spend stops working and how long your marketing efforts last.
As spend increases, each additional dollar brings back less revenue. The aim is to stop before your Marginal ROAS falls too low.
A single pulse of spend decays over time. The “Half-Life” tells you how many weeks it takes for impact to fall by 50%.
The more you want to learn, the more data you need.
MMM works best when you have time on your side, movement in budgets, and a focused set of questions. The tiles below outline the key signals that indicate readiness for a successful MMM project.
SIGNAL
≥ 2 years of weekly data (or 4-5 years monthly) allows the model to see seasonality and trends.
SIGNAL
Flat budgets hide impact. Sensible increases and decreases are required for the model to learn.
SIGNAL
Every channel, control, and seasonal factor costs data. Start with a focused scope.
SIGNAL
Use a consistent metric like revenue or conversions. Noisy or sparse data may need aggregation.
SIGNAL
While there's no magic number, MMM becomes more cost-effective as media spend grows ($1M+/yr).
SIGNAL
Markets change. Plan to retrain your model on a cadence that matches your planning cycles.
A short plan beats a hundred random events. Watch the quick explainer, then sketch your own plan with the interactive builder.
You have tracking. But no shared measurement plan.
No one agrees on goals, events, or what "good" looks like. You want a short, written plan that maps real business outcomes to GA4 and the rest of your stack.
See the difference between scattered tracking and intentional measurement. One creates confusion, the other creates clarity.
Answer five quick questions. We'll generate a mini measurement plan and a prioritized implementation table you can export.
| KPI | SUGGESTED GA4 EVENT NAME | OTHER TOOLS |
|---|
| KPI | EVENT NAME | PRIORITY | NOTES |
|---|
Set your efficiency goal, add your channels, and instantly see which to scale, hold, reduce, or cut.
CPA mode: enter spend and conversions. Lower CPA = better performance.
| Channel | Category | Spend | Conv. | CPA |
|---|
No channels yet. Click "+ Add" to get started.
(813) 922-8725 (8139-CAUSAL)Whether you're interested in discussing potential opportunities, sharing insights about analytics challenges, or simply want to connect over shared interests in causal inference and measurement, I'd love to hear from you.
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