Unified measurement
Know which channels actually drive profitable growth
MMM and incrementality testing in one platform, connected to an AI agent that turns results into daily decisions.
Marketing Mix Modeling
Always-on budget optimization. Response curves show where each euro works hardest across channels and markets.
Incrementality testing
Geo-based experiments that prove causal impact. Know which channels actually drive new revenue.
Causal factor attribution
Calibrate your attribution with real experimental evidence, not clicks, not platform claims.
Marketing Mix Modeling
Always-on budget optimization across every channel and market
Response curves show where each euro works hardest. The optimizer recommends the best allocation for your objective (profit, revenue, or new customers) and refreshes as new data arrives.
Flexible objectives
Optimize for profit, revenue, new customers, or long-term value. Set different goals per market: growth markets chase new customers, mature markets protect margin.
Marginal ROAS
Not average ROAS. The return on the next euro. The optimizer reads the slope of each response curve to find where spend still has room to grow.
Guardrails & constraints
Protect brand spend, freeze channels under test, cap max swings. The optimizer respects your rules.
Multi-metric steering
Sales, margin, new-customer profit, LTV. Switch the objective and watch the optimal mix shift.
Scenario comparison
Save a profit plan and a growth plan side by side. Compare budget shifts, then pick the path that fits.
Profit-focused
Models run on contribution margin, not just revenue.
Calibrated by experiments
Incrementality tests keep your MMM grounded in reality.
Agent-powered
Ask the agent to compare channels, run scenarios, or shift budget.
Ad platforms overstate performance by up to 40% on average. Unified measurement reveals where your budget actually drives results.
Incrementality testing
Four experiments that answer your biggest questions
Does this channel actually work? Change spend in treatment regions and measure the causal impact on sales, profit, and CAC.
Should we add this channel? Introduce spend in test regions and see if it creates incremental revenue or just cannibalizes existing.
What happens if we spend more? Validate whether the MMM response curve predicts correctly at higher spend levels.
Is bidding on your own brand incremental? Often reveals branded search is claiming credit for organic conversions.
Experiment pipeline
Active, scheduled, and completed experiments. The agent monitors results and flags significance automatically.
Experiments
Broad match expansion: Google
RunningAdvantage+ creative: Meta
RunningGeo holdout: TikTok SE
ScheduledBid strategy A/B: Shopping
CompletedCausal proof
See the real incremental effect on profit and customers
Every test compares treatment vs control regions. The result is a causal measurement of lift, not correlation, not modeled estimates. This is what actually happened because of your marketing.
Incrementality results
Estimated incremental revenue per channel based on geo-holdout tests run over the last 8 weeks.
By channel
“Since we started using Dema, we can optimize our budgets for the most profitable growth in real time.”
Causal factor attribution
Correct attribution with real experimental evidence
Standard MTA counts clicks. Ad platforms over-report. Causal factor attribution adjusts each channel's contribution using multipliers derived from your incrementality experiments, or Dema's platform-wide benchmarks when you haven't tested yet.
Benchmarked from day one
Platform-wide experiment data gives every channel a starting multiplier. No tests required to begin.
Sharpens with your data
Each incrementality test narrows the distribution. More experiments, more confidence, more precise attribution.
Totals always conserve
When one channel's share goes up, another goes down. Your total revenue stays the same, only the split changes.
The calibration loop
Tests calibrate MMM. MMM guides spend. The agent ties it together.
Incrementality results anchor the model with causal ground truth. The model gets smarter over time. The agent uses both to answer your questions and recommend next steps.
Run a geo-test
Pick a channel, country, and campaigns. Dema handles the geo-split, power analysis, and statistical evaluation.
Calibrate the model
Test results feed into MMM as ground-truth anchors, keeping response curves honest.
Act on the results
The agent combines test results and MMM curves to recommend budget shifts and flag opportunities.
Customer stories
Trusted by category leaders
Frequently asked questions
Platform ROAS tells you what the ad platform claims. It's biased because platforms optimize for their own attribution. Dema's unified measurement uses Marketing Mix Modeling for the big picture and geo-based incrementality tests for causal proof. The agent helps you compare all three methods side by side.
They're stronger together. Incrementality tests answer specific causal questions ('Does Meta in Germany work?') but can't cover every channel every month. MMM gives continuous, full-mix measurement, and incrementality results calibrate the model so it stays accurate.
Most tests run 4-6 weeks including a 2-week post-treatment observation period. Setup takes minutes: pick a channel, country, and campaigns, and Dema handles the geo-split, power analysis, and statistical evaluation.
Dema MMM runs continuously. It ingests new spend and revenue data as it arrives, so your model is always current. No waiting for quarterly consulting deliverables.
Connect your ad platforms (Google, Meta, TikTok), your e-commerce platform (Shopify), POS or store systems, and your cost data. Dema builds the unified data model automatically. Most teams are set up within a day.
Yes. Ask it which channels are incremental, run what-if scenarios on budget changes, compare MMM vs platform ROAS, or have it suggest which tests to run next. The agent has full access to your measurement stack.