Next-Generation Marketing Mix Modeling

Budgets effizienter

einsetzen

Die gängigen Methoden zur Ermittlung von Kanal-Effizienz ignorieren wesentliche Aspekte, wie z.B. View-Through-, Branding-, oder Channel-Spillover-Effekte.In der heute vielfältigen Kampagnen-Landschaft - von Online über TV bis hin zu Marktplätzen - ist die Berücksichtigung solcher Faktoren aber entscheidend, um die Werbeeffizienz nachhaltig zu steigern. 
PRISMA ist ein MMM der nächsten Generation

Für DTC-Marken mit

Omni-Kanal Ambitionen

PRISMA lernt aus historischen Daten und prognostiziert die Effizienz jedes bespielten Kanals. Fortschrittlichste Machine-Learning-Methoden bestimmen hierbei den jeweiligen Einfluss der Marketing-Kanäle, umgehen die Einschränkungen von Tracking- und Attributionsansätzen, und liefern konkret umsetzbare Empfehlungen für eine optimale Kanal-Effizienz.
KI Basiertes Marketing-Mix-Modeling

Kanal-Einfluss

besser verstehen

Erhalte einen umfassenden Einblick in den Einfluss jedes Kanals mit PRISMA's adaptivem Marketing-Mix-Modell.
Szenario Planung

Erweiterte

Strategie-Planung

PRISMA's Szenario-Planung bietet Prognosen für verschiedene Strategien.
Keine Bauchentscheidungen mehr

Effiziente

Budget-Allokation

Die dynamische Budgetoptimierung prognostiziert die effizienteste Verteilung deines Budgets.
PRISmA Fusion Attribution

Präziseres

Media-Buying

Die Fusion von Tracking, Attribution und Umfrage-Daten mit PRISMA bietet genauere Zahlen zur Anzeigenleistung und hilft  im Media-Buying bessere Entscheidungen zu treffen

Kunden

100% GDPR und CCPA konform
Individuelle Data-Science Beratung auf Anfrage
Nächtliches Model Training
Über 100 DTC-Brands sind bereits profitabler

Volles Vertrauen in deine Daten.

Alles an einem Ort. Immer aktuell. Speziell für E-commerce Teams.

Marketing Mix Modeling & PRISMA: Your Strategy Questions Answered

Wie viel genauer ist das Admetrics-Tracking im Vergleich zu Adtribute?
Traditional pixels often over-report or miss the impact of content networks. PRISMA uses machine-learning to correlate spend across native networks and social channels simultaneously. By analyzing historical activity and "channel-spillover" effects, it identifies exactly how much revenue each network is contributing, ensuring your data is deduplicated against Meta and Google.
Can I measure the true incrementality of my Meta Ads without running expensive and complex split tests?
Yes. Our adaptive modeling analyzes your historical data, spend fluctuations, and promotional events to determine your "baseline" sales. It calculates the true lift provided by your ads—meaning you can see which sales were actually driven by your marketing versus those that would have happened anyway, all without pausing your best campaigns.
What is the best way to measure the indirect "halo effect" of TikTok and Pinterest ads on my Google Search and Direct traffic?
TikTok and Pinterest often act as discovery engines that don't get the "last click." PRISMA captures these branding effects by identifying the correlation between your spend on these platforms and the resulting spikes in Shopify’s organic search and direct traffic. This gives you a unified view of how top-of-funnel discovery fuels bottom-funnel conversions.
How does "Fusion Attribution" help me filter my top ads by New Customer ROAS (ncROAS) vs. Total ROAS?
By merging MMM with our best-in-class tracking and calibrating it with 0-party survey data, we refine your attributed KPIs. This "Fusion" approach allows you to see which channels are driving genuine new growth (ncROAS) versus those that are simply retargeting existing customers, preventing you from over-spending on your current audience.
Does PRISMA provide weekly spend recommendations for Meta versus Google PMax?
Our dynamic budget optimization provides a clear roadmap for your budget. By analyzing channel behavior and saturation points, the system predicts the most efficient distribution of your funds, offering actionable insights for your weekly media planning between Meta, Google PMax, and other omni-channel platforms.
How do I attribute "View-Through" conversions from YouTube Shorts and CTV without over-reporting in my blended MER?
PRISMA accounts for view-through and branding effects that traditional models miss. By analyzing spend and revenue patterns rather than just clicks, it assigns a realistic value to video views and Connected TV (CTV) impressions. This refined data is then layered over your attribution to give you a precise ROAS that doesn't artificially inflate your blended metrics.
How does the platform account for "Channel Saturation" so I don’t waste budget scaling a winner too far?  
Our approach factors in diminishing returns. As you increase spend, PRISMA monitors for signs of channel saturation, the point where an extra dollar spent results in a lower marginal return. Our budget optimizer alerts you when a channel is reaching its peak efficiency so you can pivot your budget to the next growth opportunity.
How do I distinguish between organic and paid sales when multiple marketing touchpoints are involved?
By fusing MMM with multi-touch attribution and calibrating it through 0-party data, we create a "Source of Truth" that accounts for both direct clicks and indirect influence. This ensures that your organic baseline is protected and you aren't paying for sales that were already destined to happen through your SEO or brand word-of-mouth.
Can I plan and forecast different marketing strategies for upcoming sales events like BFCM?
Yes. The PRISMA media planning tool allows you to forecast different strategies and "what-if" scenarios. You can model the impact of different budget distributions across online and offline channels to see which strategy is predicted to yield the highest orders, revenue, or POAS before you commit your budget.
How does PRISMA help maintain data accuracy amidst evolving privacy laws and iOS updates in 2026?
MMM is inherently privacy-first because it relies on aggregated data rather than individual user-level tracking. This allows you to sidestep typical tracking pitfalls and privacy limitations like GDPR and CCPA, providing a reliable measurement framework that stays accurate regardless of how browsers or phone OS updates change.
How does the "Fusion" approach refine my POAS (Profit on Ad Spend) for more informed media buying?
We merge machine-learning predictions with granular attribution data and your specific store costs (COGS, shipping). This allows us to refine your attributed KPIs, giving you highly accurate POAS metrics that reflect the actual profit generated by every channel, including TV, marketplaces, and social media.
Can I use this data to build a rolling 90-day cash flow and growth forecast?
By using our advanced strategy and media planning features, you can project future revenue based on current channel efficiency and planned spend. This dynamic forecasting helps you align your marketing strategy with your inventory lead times and cash flow requirements.
How does PRISMA handle "Channel-Spillover" between my online ads and physical marketplaces?
For brands with omni-channel ambitions, we track how your digital spend (like Meta ads) influences sales on other marketplaces or even offline. PRISMA captures these spillover effects, giving you a comprehensive insight into each channel’s true influence across your entire sales ecosystem.
How long does it take for the machine-learning model to learn from my historical data?
The system begins correlating your activity, spend, and promotional events the moment you connect your platforms. Because it learns from your historical data, you can see actionable insights for optimal channel efficiency almost immediately, helping you make data-driven decisions from day one.