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Accessing calibration settings

Navigate to Settings > Causal factor attribution in the Dema app to open the calibration configuration panel. The attribution settings page showing calibration rules Here you’ll see your current calibration rules, source channel groups, and the ability to add, edit, or remove calibrations.

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Understanding the benchmark distribution

Before setting any multiplier, review the incremental factor distribution shown for each channel. This bell curve represents the expected incremental ROAS based on:
  1. Platform-wide experiments - Dema’s benchmark from all finalized incrementality experiments matching the channel
  2. Your own experiments - If you’ve run incrementality experiments, they refine the distribution and narrow the curve
Bell curve distribution with merchant experiments highlighted
Even if you haven’t run any incrementality experiments yet, Dema provides a benchmarked distribution based on experiments across the platform. This gives you a data-driven starting point rather than guessing.
Reading the bell curve:
  • The peak represents the most likely incremental ROAS for this channel
  • A wide curve means more uncertainty (fewer or more varied experiments)
  • A narrow curve means high confidence (many consistent experiments)
  • The mean is the recommended calibration starting point
You can also switch between reference metrics to view the distribution in terms of:
Reference metricWhat it measures
ROASIncremental return on ad spend (revenue per spend)
Gross salesAbsolute incremental gross sales value

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Adding a calibration rule

Click New calibration to create a new rule. Each rule targets a specific scope of your marketing data. Adding a new calibration rule with scope selectors

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Defining the scope

Each calibration rule can be scoped at multiple levels of granularity:
  • Channel group - Marketing category (e.g. Paid Social, Paid Search, Display)
  • Channel - Specific ad platform (e.g. Meta, Google, TikTok)
  • Funnel campaign - Campaign funnel stage (e.g. Lower funnel, Upper funnel)
  • Market - Geographic market (e.g. SE, DE, US)
  • Country - Specific country (e.g. Sweden, Germany)
  • Storefront - Specific storefront (your store identifier)
Leave any field set to All to apply the rule broadly. For example, setting channel group to “Paid Social” and leaving everything else as All applies the calibration to all paid social channels across all markets.

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Specificity matching

When multiple rules could match a data row, the most specific rule wins. Specificity is determined by how many fields are set to specific values (not set to All). Example:
  • Rule A: Paid Social / All / All / All with multiplier 0.8
  • Rule B: Paid Social / Meta / All / SE with multiplier 0.6
For Meta traffic in Sweden, Rule B applies (more specific). For other paid social traffic in Germany, Rule A applies (only match).

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Choosing the calibration type

For each rule, select how the calibration is applied:

MTA

Multiplies the MTA-attributed value by the multiplier.Best for: Channels where MTA captures the journey well but over- or under-attributes the conversion.

Ad platform

Uses the ad platform’s reported value as the base, then applies the multiplier.Best for: Channels where ad platform reporting is more reliable than MTA tracking (e.g., limited cookie visibility).
Selecting calibration type - MTA vs Ad platform toggle

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Setting the multiplier

Use the slider to set the calibration multiplier. The bell curve distribution is shown alongside to help you choose an appropriate value. Multiplier slider with the bell curve distribution Guidelines for setting the multiplier:
MultiplierMeaningWhen to use
1.0No adjustmentMTA values are already accurate
< 1.0Reduce contributionChannel gets more credit than it causally drives (common for retargeting)
> 1.0Increase contributionChannel drives more than MTA captures (common for upper-funnel / awareness)
The benchmark distribution’s mean is a good default starting point. If you’ve run your own incrementality experiments and they align with the benchmark, you can be more confident in using the mean. If your results diverge, favor your own experimental data.

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Source channel groups

Source channel groups are channels that absorb the redistribution delta when other channels are calibrated. By default, these are typically:
  • Direct - Direct / type-in traffic
  • Other unattributed - Sessions with no attributed marketing touchpoint
When you increase a paid channel’s contribution, the same amount is subtracted proportionally from source channels. This ensures daily totals are always conserved.
If the total calibration delta exceeds what source channels can absorb, the system automatically caps adjustments. This prevents any channel from going negative.

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Version history

Every time you save a calibration configuration, a new version is created. You can:
  • View previous versions to see how your calibrations have changed over time
  • Restore a previous version to revert to an earlier configuration
  • See who saved each version for audit purposes
Version history showing previous calibration configurations

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Saving your configuration

After configuring your calibration rules, click Save to persist your changes. The new configuration:
  1. Is stored as a new version (the previous configuration is preserved in history)
  2. Takes effect on the next pipeline run (once a day every morning)
  3. Applies to all future attribution calculations until changed again
1

Review your rules

Verify that each calibration rule targets the correct scope and has an appropriate multiplier based on the benchmark distribution or your experiment results.
2

Check source channels

Ensure your source channel groups are configured correctly. In most cases, direct and other unattributed traffic are the right choices.
3

Save

Click Save to create a new configuration version. Your changes will take effect on the next scheduled pipeline run.