Causal attribution, measured on profit
Ad platforms over-report. Multi-touch attribution only sees what was tracked. Causal factor attribution weights both against experimental evidence — and reports the result in contribution margin and new customers, not just revenue.
Profit, not revenueand new customers
Weights you setper channel, funnel, market
From your testsnot vendor assumptions
Two sources, two different answers, no way to choose
Meta says it drove the sale. Your multi-touch attribution says organic search did. Both are describing the same order, and both are partly right — the platform because it did touch the customer, your MTA because it recorded the last click. Picking one means accepting its bias. Averaging them means pretending the bias is the same everywhere, which it is not.
Corrected revenue still tells you the wrong thing
Most causal measurement stops at incremental conversions or incremental revenue. Two channels can deliver identical incremental revenue while one sells high-margin products customers keep and the other sells low-margin products that come back. Dema applies the same causal correction to contribution margin and to new customers, so the channel that looks best is the one that actually made you money.
Set the weighting, don't pick a side
Causal factor attribution applies an explicit weight to each source. Take 30% of what the ad platform claims and 70% of what MTA reports — or whatever ratio the evidence supports. You can see the weight, change it, and explain it to your CFO. Tools that silently auto-adjust your reporting give you a number you cannot interrogate; this gives you one you can defend.
Search Paid30 experiments
Benchmark avg
1.20x
95% range
0.64x – 1.76x
The weights come from experiments, not opinions
An incrementality test measures what a channel actually caused by changing spend in some regions and not others. That result is the weight. Before you have run tests, Dema starts you on platform-wide benchmarks drawn from experiments across our customer base — a defensible starting point rather than a guess — and each test you run replaces a benchmark with evidence from your own business.
Channel level
Meta, Google, TikTok and affiliate do not over-report by the same amount. Each gets its own weight.
Funnel level
Prospecting and retargeting deserve very different discounts. Branded search usually deserves the largest.
Market level
Reporting bias varies by market, driven by tracking coverage, consent rates and channel mix.
Where causal attribution sits in the stack
Ad platform data and multi-touch attribution are inputs. Incrementality testing is the evidence. Causal factor attribution is what turns that evidence into a corrected number you can act on daily, and marketing mix modeling is what covers the whole mix continuously. Causal attribution and MMM are the destination — the raw platform numbers are just where you start.
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Frequently asked questions
Causal attribution corrects conventional attribution using evidence about what marketing actually caused, rather than what was merely recorded. In Dema this is implemented as causal factor attribution: each source of attribution data is given an explicit weight, and those weights are derived from incrementality experiments instead of being assumed.
Yes, and that is the point. Most causal measurement reports incremental conversions or incremental revenue. Dema applies the correction to contribution margin and to new customers as well, because two channels with the same incremental revenue can have very different margins once cost of goods, fulfilment and returns are accounted for. Optimising incremental revenue can quietly reduce profit.
Multi-touch attribution divides credit between the touchpoints it recorded, so it inherits every gap and bias in that recording. Causal attribution takes that output as one input among several and adjusts it — for example accepting 70% of the MTA figure and 30% of the platform's — using weights measured through experiments.
From geo-based incrementality tests on your own business wherever you have run them. Before that, from Dema's platform-wide experiment benchmarks, so every channel starts with a defensible multiplier rather than a placeholder. Each new test replaces a benchmark with your own evidence.
Yes, and they should. Weights are set at channel, funnel-stage and market level, because reporting bias is not uniform: branded search typically claims credit for demand that already existed, prospecting rarely does, and tracking coverage varies market to market. A single global correction factor hides more than it fixes.
No. Totals always conserve. When one channel's attributed share goes up, another's goes down — the split changes, the total does not. That property is what makes the corrected numbers safe to budget against.
The weighting depends on whatever attribution inputs you have, and those may be cookie-dependent. The evidence behind the weights is not: incrementality testing and marketing mix modeling both run on aggregate data. As tracking coverage falls, the sensible move is to lean further on MMM and incrementality, which causal attribution is designed to accommodate.