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Know what’s
driving growth.
Decide where to
invest next.

Connect your marketing data and get an independent Marketing Mix Model showing what’s working—and where your next dollar should go.

Get my measurement report

FREEYour first measurement report.

  • Cross-channel contribution
  • Incremental & marginal ROAS
  • Saturation insights
  • Budget recommendations
Preview MMM InsightsIllustrative data
Estimated return by channel: Google Ads 2.09× (95% interval 1.41–2.78×), Meta Ads 4.32× (3.84–4.78×), Microsoft Ads 7.74× (4.79–11.40×).

Meet the team behind MAI

Yuchen

Yuchen

CEO

Previously VP of Engineering at Instacart and Principal Engineer at Google Ads

Ph.D in Machine Learning from Northwestern, B.Eng from Tsinghua

Jian

Jian

CTO

Previously Sr Director of Engineering at Instacart and Senior Staff Engineer at Google Ads

Ph.D in Machine Learning from UC Santa Cruz. B.Eng from Fudan

Liang

Liang

Founding Engineer

Previously Staff engineer at Instacart and Google Ads

Ph.D in Machine Learning from Indiana University Bloomington. B.Eng from SJTU

Xiangyu

Xiangyu

Member of Technical Staff

Previously Director of Engineering at Instacart and Staff Engineer at Google

Ph.D in Statistics from Duke University. Bachelor in Math from Peking University.

Haipeng Zheng

Haipeng Zheng

Data Scientist

Previously Member of Research Staff at The Voleon Group

Ph.D. Princeton University

B.Eng. Tsinghua University.

Shuo Feng

Shuo Feng

Data Scientist

Previous Head of Data Science at TikTok, leading user growth and marketing measurement.

A clearer picture of your marketing.
And your next move.

Channel contribution: baseline 32%, Google 32%, Meta 26%, Microsoft 10%.

Understand what drives growth

See each channel’s estimated contribution to business results, alongside baseline demand.

Estimated incremental and marginal ROAS for Google Ads, Meta Ads, and Microsoft Ads.

Know what your next dollar could return

Compare incremental and marginal ROAS, with uncertainty visible alongside the estimates.

Estimated incremental revenue flattens as scenario spend increases.

Find room to grow

Explore where channels may have headroom and where additional spend starts to deliver diminishing returns.

A proposed allocation of a $100,000 monthly budget across Google, Meta, and Microsoft Ads.

Compare your next move

Review your current allocation against a proposed plan, including the assumptions and uncertainty.

All charts use illustrative data. Estimates are not guaranteed outcomes.

Get this analysis for my business

Two views of the same spend decision.

Channel contribution explains the modeled past. Spend response helps explore what could happen next.

MMM interpretationIllustrative
Example question

Does a channel’s past contribution mean the next dollar will return the same?

Illustrative response

Not necessarily. Compare modeled contribution with the spend-response curve and marginal return estimates. Keep uncertainty and diminishing returns in view when considering a new mix.

Conversation example · no live account data
Illustrative modeled contributionIllustrative example
Baseline demand
40%
Google Ads
35%
Meta Ads
25%

Synthetic model shares, not customer results or a forecast. Data/model readiness checks apply; advanced budget planning requires enabled access.

Connect your data.
Get a clearer direction.

Marketing Mix ModelingProduct preview

Turn your ad data into
smarter budget decisions.

Bring your sales and ad-spend history together to estimate channel contribution and explore a better budget mix.

Start with the data you have

Connect your sales and advertising sources. We’ll assess your history and help identify what’s ready for modeling and what needs more data.

Preview MMM Insights

Illustrative channel ROAS: Google Ads 3.60× (95% interval 2.80–4.40×), Meta Ads 2.90× (2.10–3.80×), Microsoft Ads 3.20× (2.30–4.10×).Channel ROAS
  1. 01

    Connect your data

    Connect supported sales and advertising sources.

  2. 02

    Build and check the model

    MAI evaluates data coverage and quality, then builds and checks your model.

  3. 03

    Review your report

    Explore channel performance, return estimates, and budget scenarios.

Not sure your data is ready? Start with your available sales and ad-spend history. MAI checks coverage and quality before modeling.

A few things
you might be wondering.

How is MAI different from other MMM vendors?

MAI combines self-serve marketing mix modeling with agent-powered optimization. Start by understanding channel performance, then use MAIOS to move from measurement to approved action.

What data do I need?

Start with your sales and ad-spend history. MAI can evaluate usable sales history from Shopify or GA4 alongside supported ad-channel data. Readiness depends on the history, coverage, and quality available for your business.

How do I connect my data?

Connect your supported sales and advertising sources, such as Shopify or GA4 and Google Ads, Meta Ads, or Microsoft Ads. MAI evaluates the available history before modeling; you don’t need to assess data readiness on your own.

How long will my report take?

Report preparation starts after your data passes the coverage and quality checks. Timing depends on your data readiness and the model checks.

Not sure whether you have enough data?

Start by connecting your available sources. MAI checks whether there is enough usable information to build a model and highlights gaps and next steps. A report depends on passing the data and model checks.

What does it cost?

Your first measurement report is free. Ongoing modeling is available through a paid plan; review the current plan details during signup.

Make your next marketing decision
with clearer evidence.

Understand your results. Explore your next move.
Start with your first MAI Measurement report.

Get my measurement reportYour first report is free.