> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dema.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Interpreting calibrated attribution
> Understand how calibrated attribution changes your channel mix, read before-and-after comparisons, and know when to update your calibrations.
## Accessing calibrated attribution
Once calibration is configured, a new attribution model called **Dema CFA** (Dema causal factor attribution) becomes available as a selectable option in your reports. You can switch between attribution models to compare calibrated and uncalibrated views of your data.
***
## Before vs after calibration
Once calibration is applied, your attribution data reflects **causally adjusted contributions** rather than raw MTA or ad platform-reported values. The key changes to look for:
* **Channel contribution shares** shift to reflect true incremental impact
* **Source channels** (direct and other unattributed traffic) absorb the redistribution delta
* **Daily totals remain unchanged** - only the distribution across channels changes
***
## Example walkthrough
Consider a merchant with the following daily attribution (using MTA as the base in this example):
| Channel | MTA Gross Sale | Share |
| --------- | -------------- | -------- |
| Meta | €10,000 | 40% |
| Google | €8,000 | 32% |
| Direct | €5,000 | 20% |
| Other | €2,000 | 8% |
| **Total** | **€25,000** | **100%** |
After running an incrementality experiment, the merchant discovers that Meta's true incremental ROAS is about 70% of what MTA attributes. They set a calibration multiplier of **0.7** for Meta (MTA-based).
**After calibration:**
| Channel | Calibrated Gross Sale | Share | Change |
| --------- | --------------------- | -------- | ------ |
| Meta | €7,000 | 28% | -12pp |
| Google | €8,000 | 32% | - |
| Direct | €7,143 | 28.6% | +8.6pp |
| Other | €2,857 | 11.4% | +3.4pp |
| **Total** | **€25,000** | **100%** | - |
The €3,000 reduction from Meta is redistributed proportionally to Direct and Other (the source channels), based on their original gross sale shares. The daily total of €25,000 is preserved exactly.
**What this tells you:**
* Meta was previously over-credited by about 30% according to the incrementality experiment
* Direct and organic traffic were contributing more than MTA suggested
* Budget optimization decisions based on calibrated values will be more accurate
***
## Downstream effects
Calibrated channel-level contributions flow into **campaign-level attribution**. This means:
1. **Channel-level calibration** adjusts the total contribution for each channel (e.g., Meta Paid Social)
2. **Campaign/ad-level distribution** uses MTA patterns within that channel to allocate the calibrated total across individual campaigns, ad sets, and ads
This gives you granular, campaign-level attribution that reflects causal incremental impact, not just MTA correlation patterns.
***
## Comparing ROAS metrics
With Causal factor attribution enabled, you can compare multiple views of channel performance:
| Metric | What it reflects | Source |
| -------------------- | ------------------------------------------------------- | ------------------------------- |
| **MTA ROAS** | Correlation-based return on ad spend | Multi-touch attribution model |
| **Ad platform ROAS** | Platform's self-reported return | Meta, Google, TikTok dashboards |
| **Calibrated ROAS** | Causally adjusted return on ad spend | Causal factor attribution |
| **Incremental ROAS** | Experimentally measured return from a single experiment | Incrementality experiments |
**Incremental ROAS** is a snapshot from a specific experiment run over a fixed time period. It gives you high-confidence causal evidence, but only for that moment in time. **Calibrated ROAS** extends that knowledge into your ongoing attribution, so you can make day-to-day budget decisions grounded in causal evidence without needing to run a new experiment every day.
***
## When to recalibrate
Your calibration settings should be reviewed and updated when:
After completing an incrementality experiment, check whether the results change the benchmark distribution. If the distribution shifts meaningfully, update your multiplier.
Significant changes in your market (new competitors, seasonal shifts, or major campaign strategy changes) can affect how incrementally effective your channels are.
Even without new experiments, reviewing calibrations quarterly ensures they still align with your business reality and haven't drifted.
When you add a new marketing channel, check the benchmarked distribution and set an initial calibration. Plan an incrementality experiment to validate.
***
## Improving accuracy over time
The more incrementality experiments you run, the more accurate your calibrations become:
Use Dema's platform-wide benchmarked incremental factors as your initial calibration. This is already better than uncalibrated MTA.
Prioritize incrementality experiments on your largest channels. This is where miscalibration has the biggest budget impact.
After each experiment, review the updated distribution and adjust your multiplier. The bell curve will narrow, reflecting increased confidence.
Gradually run experiments across your channel portfolio. Each experiment improves the overall accuracy of your calibrated attribution.
**The virtuous cycle:** Better calibration leads to better budget allocation, which leads to better business outcomes. Running even a few well-designed incrementality experiments can significantly improve the accuracy of your entire attribution system.