AD cube Marketing Mix Modeling quantifies how each channel contributes to business results, accounting for seasonality, market conditions and cross-channel effects. It then allows teams to compare budget scenarios and identify where future investment can generate the greatest impact.
What sets us apart
What sets our MMM apart isn't just reading week by week, it's delivering insights at weekly detail. That's what makes it actionable: a tactical, operational view you can act on over shorter horizons, without losing sight of the long-term picture.
Hierarchical Bayesian models identify relationships that are difficult to observe manually and quantify uncertainty, instead of hiding it behind a single deterministic output.
MMM also works on specific product lines or multi-country strategies, we'll define the right configuration together with your team.
MMM and Core
Both decide about budget, at different altitudes. The Marketing Mix Model works across channels, week by week. Core works inside the campaigns, day by day.
| Marketing Mix Model | AD cube Core | |
|---|---|---|
| Layer | Channel | Campaign |
| Cadence | Weekly | Daily |
| What it decides | How much to invest, and where | Where to move budget while campaigns are running |
| Who uses it | Marketing director, finance, management | In-house performance team |
| Includes offline | Yes | No |
The two are independent, and they work well together: the model sets the plan, Core executes inside it.
The problem it solves
Marketing teams often see performance, but not the contribution behind it. Platform dashboards can report what happened inside each channel, but rarely explain which activities are actually driving growth in which channel, or where the relationship between spend and result is becoming inefficient.
AD cube MMM identifies relationships that are difficult to observe manually and helps move marketing planning away from isolated assumptions.
Who it is for
The product is designed for organizations managing significant budgets across online and offline activities, where results depend on multiple variables moving at the same time.
It is especially relevant when budget decisions need to be discussed across different roles, including marketing teams, finance, management, agencies and business units.
Higher marketing ROI through more precise allocation decisions.
Improved strategic performance across the marketing mix.
Optimized budget distribution across channels and time periods.
Greater decision capacity based on data rather than isolated assumptions.
Better marketing insight and discovery of new growth opportunities.
Past monitoring
Select a monthly or weekly view to compare each channel’s share of spend with its share of measured effect, one of several diagnostic views in the platform. Where the two diverge, allocation may be inefficient.
Read monthly or weekly, the view compares budget allocation and revenue contribution across each channel, with online and offline activity inside the same analytical logic.
Each channel can then be examined through its own saturation curve and history, showing where investment is still responsive and where returns begin to flatten.
Good to know
The model is configured around the client’s real operating structure: channels, markets, time windows, offline activity, online platforms, reporting needs and available historical data.
The model adapts to incomplete or disorderly data as it is, instead of asking every company to fit a standard configuration before it can produce value.
Its natural-language reports then turn the output into clear explanations for marketing, finance and management, while keeping the full analytical detail available for specialists.
Future optimization
Build a what-if scenario around a specific time period, a planned budget or a revenue target. As the point moves, expected revenue and ROAS change with it, making the spend-return trade-off visible before budget is committed.
Scenarios can be built around the horizon that matters, weekly or monthly, and expanded into further plots: suggested allocation by channel, expected ROAS by channel, marginal returns and other diagnostics to compare options before the plan is approved.
Proof
Two companies that used the Marketing Mix Model to work out where the next euro of budget should go: one finished, one still under way.
With Google Ads close to saturation, a seasonality-aware plan spread a full quarter of summer-course advertising across four channels, week by week.
Read the case → Sport equipment 3markets in one modelA European maker of technical sport equipment reads France, Spain and Japan in one structure, to see where the three markets behave alike and where each follows its own dynamics.
Read the case →Next steps
MMM is usually shaped around the number of channels, markets, integrations, historical depth and support required. The proposal is therefore configured around the actual operating context, not around a generic package.
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