See which channelsdrive your revenue.

GA4, Meta and Google each count your revenue their own way, and the totals never add up. Our Marketing Mix Models show what each channel adds, without cookies or user tracking.

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+€38K

expected revenue next month, same €100K budget (range €29K to €47K)

TodayRecommended

Meta Ads€30K → €38K

Google Search€35K → €33K

YouTube€15K → €19K

Display€12K → €5K

Microsoft Ads€8K → €5K

Pricing

We have something for everyone, from a monthly budget split for a growing brand in one market to a full measurement service for advertisers spending across many countries.

Budget Optimizer

For growing ecommerce and app brands that want a clear budget split every month.

€500a month, per market

✓ Monthly optimal budget recommendations
✓ Built on an open-source MMM methodology
✓ Models trained, maintained, and refreshed on an ongoing basis
✓ Priced per market, so it scales cleanly as you add markets

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Complete MMM Suite

For larger advertisers who want three models to agree before big budget moves.

Custompriced to your markets and channels

✓ Everything in Budget Optimizer
✓ Robyn, Meridian, and PyMC-Marketing run in parallel on your data
✓ Three models compared side by side
✓ Where the models disagree, we show you
✓ Ongoing training and maintenance, across every market

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Complete Measurement Service

For companies that want every model checked against real experiments.

Custompriced to your markets and testing plan

✓ Everything in Complete MMM Suite
✓ Incrementality / geo-lift tests designed and run alongside the models
✓ Models calibrated against your own test results
✓ Ongoing testing cadence as your business changes
✓ Custom attribution modeling, for a complete measurement setup

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Compare the tiers side by side

Feature comparison across the three marketing mix modeling tiers
Feature	Budget Optimizer	Complete MMM Suite	Complete Measurement Service
Monthly budget recommendations	✓	✓	✓
MMM methodologies	1 (open-source)	3: Robyn, Meridian, PyMC	3: Robyn, Meridian, PyMC
Cross-model triangulation	—	✓	✓
Incrementality / geo-lift testing	—	—	✓
Calibration against test results	—	—	✓
Custom attribution modeling	—	—	✓
Ongoing model maintenance	✓	✓	✓
Pricing	€500/mo per market	Custom	Custom

Ecosh · Ecommerce · Digitegu 2025 Winner

56% more revenue after Ecosh found out which ads worked

GA4 said Meta drove 6% of revenue. Meta said 54%, Google said over half. Our model showed that each extra euro on Meta brought in 2.5× as much as an extra euro on Google, and Ecosh had its best year on record.

Extra revenue per extra euro spent

Meta Ads

2.5×

Google Ads

1×

Read the full case study →

See where the next euro should go

Every channel runs out of steam at some point. The model finds where each one starts to flatten, so budget moves from the channel that has hit its ceiling to the one that still has room.

✓ Separate the effect of ads from seasonality, discounts, PR and other factors
✓ Account for lingering effects days or weeks after someone sees an ad
✓ Know where extra spend starts doing less

Revenue from each channel as its spend grows

Know the risk before you move budget

Budget decisions come with uncertainty. For every plan, we forecast the most probable outcome along with the worst and best case, based on your data and how each channel behaves.

✓ Spot the risk of overspending where returns flatten
✓ Find the upside you miss by underspending on strong channels
✓ Compare plans side by side before you commit

Next month's revenue forecast for three budget plans

Frequently asked questions

What business questions can MMM answer?

MMM helps you answer strategic questions like the impact of each channel after adjusting for seasonality, discounts, and external demand shifts. It helps you identify overspending vs underspending, forecast the effect of budget shifts, and quantify what promotions actually drove.

Is MMM better than attribution or experiments?

They serve different jobs. MMM stays robust when tracking is limited and provides causal estimates across channels. Experiments are gold-standard for specific hypotheses but can be costly and slow. Attribution splits credit; it doesn't prove incrementality.

What data do we need?

Ideally 2 to 3 years across key channels (spend, impressions, clicks) plus outcome data (revenue, leads, and so on). We also include non-marketing factors like holidays. You don't need perfect data: we build the best model possible from what exists.

How do you validate the model?

We stress-test assumptions, validate against holdouts where possible, and compare MMM insights with experiments when available. We iterate: define, test, simulate or experiment, measure, improve.

Book a free strategy session

Curious if marketing mix modeling is right for you? Book a free call and we'll map your measurement and budget plan.

Karl

Founder of ePPC, your first point of contact.

Chinmay

Builds the custom marketing mix models that turn your data into budget decisions.

Or write to us: karl@ppc.ee