Build the Attribution Stack: Four Quarters of Measurement Decisions
Objective: Navigate four quarterly measurement decisions for a growing DTC brand, applying the lesson's layered attribution stack (DDA baseline, incrementality testing, specialized AI platforms, MMM) to choose the right tool for each stage of growth and budget.
You're the head of growth marketing at Sula Vineyards, scaling direct-to-consumer wine-club subscriptions alongside retail distribution, and you own the measurement stack decisions each quarter as spend and channel count both grow.
Four quarters, four decisions. Each stage gives you a dashboard snapshot and a measurement choice; the lesson's stack layers determine which option is optimal for that stage.
At each stage of Sula's DTC growth, which layer of the attribution stack should you add next, and which would be premature or wasteful?
Before you start
What you'll need
- —Has read the lesson's Building Your Attribution Stack for 2026 section
- —Understands the difference between DDA, incrementality testing, and MMM
- Incrementality test
- a holdout or geo-based experiment that measures true causal lift from a channel by comparing exposed vs. unexposed groups, the ground truth an attribution model's credit estimates get checked against.
- Media Mix Modeling (MMM)
- a statistical model that correlates aggregate channel spend against revenue over time, without individual-level tracking, used for strategic rather than tactical budget questions.
Free path (everything below is enough to finish)
Free, already the GA4 default, sufficient for the Q1 and Q2 decisions
Free, enough to track a 4-stage decision log
Paid upgrades (optional, faster/deeper)
A specialized AI attribution platform (Northbeam, Triple Whale) only earns its cost once a brand clears roughly ₹5 crore in DTC revenue or runs 5+ active channels, per the lesson's Layer 3 threshold; add it in Q3 of this simulation, not sooner.
Free tier exists, but connecting multiple data sources cleanly benefits from a paid analytics stack behind it at this revenue scale
The process
Simulation
Q1: Three Channels, First Real Budget
Week 1Sula's DTC wine club just launched. You're running email, paid search, and Instagram ads, roughly 60 conversions a month across all channels combined, and leadership wants to know which channel to fund more.
GA4: 3 active channels, ~60 conversions/month total, last-click currently the default model.
What's the right first move on measurement this quarter?
Final deliverable
A 4-quarter decision log showing which attribution layer was added at each stage, with the lesson-grounded reasoning for why that layer and not another.
See a reference example
Bansal Wire Industries, attribution stack decision log (excerpt) Q1: Switched to GA4 DDA as baseline (free, catches last-click's worst distortions) Q2: Ran a 3-week holdout on the top-credited channel before scaling its budget further Q3: Held off on a specialized platform, revenue and channel count hadn't yet crossed the lesson's threshold Q4: Commissioned an MMM model once 2 years of spend/revenue history existed, used it to answer a strategic category-shift question DDA couldn't answer
Success criteria
You're done when you can:
- Chooses DDA over MMM or a specialized platform in Q1, matching the lesson's data-volume constraints
- Chooses a holdout incrementality test over blindly trusting or blindly cutting spend in Q2
- Recognizes the revenue/channel threshold for adding a specialized platform in Q3
- Correctly routes the strategic year-over-year budget question to MMM, not DDA, in Q4