Econometric Marketing Mix Modeling for Enterprises

Problem Context

Marketing teams at mid-large enterprises need to allocate €10M–€30M+ budgets across 5+ markets and channels. They rely on marketing mix modeling (MMM) to measure campaign impact, but existing tools either lack econometric rigor or require heavy custom development. Current solutions (e.g., Funnel.io) treat MMM as an afterthought, leading to unreliable insights and misallocated spend.

Pain Points

Teams waste weeks manually cleaning data, fighting with generic dashboards that don’t account for marketing-specific variables (e.g., channel interactions), and dealing with 'early stage' MMM features that produce inconsistent results. They’ve tried workarounds like hiring consultants for custom models or patching together Excel + Tableau, but these are slow, expensive, and don’t scale. The risk of misallocating €30M across markets is too high to tolerate.

Impact

Poor MMM decisions lead to wasted ad spend (e.g., over-investing in underperforming channels), lost revenue from suboptimal budget allocation, and frustrated stakeholders who demand data-driven justifications. Teams also lose time to manual analysis that could be automated, and the lack of trust in their models creates internal politics around marketing ROI. For €30M+ budgets, even a 1% improvement in allocation accuracy saves €300K/year.

Urgency

This is a quarterly/annual crisis: budget reallocations happen every 3–12 months, and ad-hoc campaign analysis is needed monthly. If the MMM tool fails, teams scramble to justify spend to CFOs or risk cutting high-impact channels. The user’s mention of Funnel.io’s 'early stage' MMM suggests they can’t wait—they need a reliable solution now to avoid another failed allocation cycle.

Target Audience

Marketing operations managers, performance marketing leads, and data analysts at mid-large enterprises (€10M–€100M+ annual marketing budgets) in industries like CPG, tech, or retail. These teams already use tools like Funnel.io, Tableau, or Google Analytics 360 but lack a specialized MMM solution. They’re also the buyers of enterprise SaaS (no IT sign-off needed for analytics tools).

Solution Approach

A no-code SaaS platform that delivers *pre-built, marketing-specific econometric models- with zero-setup integrations for ad platforms (Google Ads, Meta), CRMs, and CDPs. Users upload their data (via API or CSV), and the platform automatically runs Bayesian structural time-series models with marketing-specific priors (e.g., channel interaction effects). Results include actionable insights like 'Reduce Facebook spend by 15% and reallocate to SEO for +20% ROI.'

Key Features

1. No-code integrations: Connects to 10+ ad platforms and CRMs in <1 hour via pre-built connectors or CSV uploads.
2. Recurring insights: Monthly model updates + scenario testing (e.g., 'What if we cut TV by 20%?').
3. Collaboration hub: Share reports with stakeholders and track budget allocation decisions over time.

User Experience

Users start by connecting their data sources (e.g., Google Ads, CRM) via a 5-minute setup flow. The platform then runs the MMM analysis in the background and delivers a dashboard with channel ROI, interaction effects, and optimization recommendations. They can drill down into specific markets or campaigns, test 'what-if' scenarios, and export reports for stakeholder meetings—all without writing code or hiring consultants.

Differentiation

Unlike generic dashboards (Funnel.io) or statistical tools (R/Python), this focuses *exclusively on marketing-specific econometrics- with pre-built models that account for channel interactions, seasonality, and market-specific effects. It’s also *10x faster to set up- than custom solutions (no need to hire data scientists) and more accurate than Excel/Tableau hacks. The recurring model updates ensure insights stay relevant as market conditions change.

Scalability

Starts with a single-seat plan ($199/month) for small teams, then scales to enterprise plans ($499+/seat) with features like multi-market analysis, custom model training, and API access for data teams. Users can add seats as their team grows or expand to additional markets without reconfiguring the entire model. Over time, the platform adds modules (e.g., attribution modeling, media mix optimization) to increase stickiness.

Expected Impact

Users save *20+ hours/month- on manual analysis and avoid **€100K–€1M/year in misallocated spend*- by making data-driven budget decisions. Stakeholders gain trust in marketing ROI, and teams can focus on strategy instead of fighting with tools. The platform also reduces consultant costs (replacing $50K/year custom model builds) and provides a single source of truth for cross-channel performance.