# Cassandra

> Cassandra (cassandra.app) is an AI marketing brain for growth teams. It learns what really drives a brand's growth with marketing mix modeling (MMM) and incrementality tests, then does the work: it plans to the growth target, moves budgets, launches and pauses ads, creates new ads in the brand's style, runs tests and reports, within the rules the team sets. Every move is checked against its forecast, so it gets better the more it is used. The result: bigger growth targets with the same team. Not to be confused with Apache Cassandra, the open-source distributed database: this Cassandra is a marketing product, entirely unrelated.

Who it is for: heads of growth, CMOs, performance and growth marketers, marketing engineers and agencies. Brands use it to measure what each channel really adds and act on it every day. Agencies use it to run measurement and growth plans for every client under their own brand (white label).

How it works:
1. Connect data: ad accounts (Meta, Google Ads, TikTok, LinkedIn, Amazon and more), the store or CRM (Shopify, HubSpot, Salesforce), analytics and email.
2. Learn what works: a model of the whole marketing mix, checked by live geo experiments, shows what every euro really added, compared with what each ad platform reports.
3. Plan to the target: the growth plan lists the moves that reach the target, each with its expected result.
4. Do the work: approved moves are applied in the ad accounts. The team decides what runs on its own and what waits for approval, and every change is recorded.
5. Check and learn: every result is compared with its forecast and the next plan starts smarter.

Capabilities: causal measurement (marketing mix modeling and incrementality testing), forecasting and budget allocation, ad management, creative analysis and new ads, attribution checked against measured lift, reports for teams, finance and clients, and custom workflows built by asking in plain language.

Key facts: 500+ brands and agencies analyzed, 1,221+ marketing mix models run, $1.85B in ad spend measured, 22 markets, 83 advertising channels. Approved Meta Marketing Measurement Partner.

## Product

- [Home](https://cassandra.app/): Hit bigger growth targets with the same team. What the AI marketing brain does and how.
- [Platform](https://cassandra.app/platform): How Cassandra is built: data pipeline, synthetic brain, the platform behind the AI marketing brain: data, the learning model, AI agents and the tools that change budgets and ads, tools that act in production, and skills.
- [Marketing Mix Modeling](https://cassandra.app/marketing-mix-model): How Cassandra measures what every channel really adds, refreshed on its own and checked by live tests.
- [Incrementality Testing](https://cassandra.app/incrementality-testing): Geo experiments and holdouts designed, launched and read automatically.
- [Always-On Incrementality](https://cassandra.app/always-on-incrementality): Every channel checked against real sales, every day.
- [Performance Incrementality Dashboard](https://cassandra.app/performance-incrementality-dashboard): Platform-reported ROAS next to measured incremental impact, campaign by campaign.
- [Connectors](https://cassandra.app/connectors): Ad platforms, store, CRM and analytics, connected in minutes.
- [Pricing](https://cassandra.app/pricing): Plans that scale with yearly ad spend.
- [For Agencies](https://cassandra.app/for-agencies): A marketing brain for every client, under the agency's brand.
- [For Brands](https://cassandra.app/for-brands): Growth plans and execution for in-house teams.

## Learn: The marketing brain

- [What Is a Marketing Brain?](https://cassandra.app/learn/what-is-a-marketing-brain): An AI system that learns what drives a brand's growth and turns it into decisions and actions.
- [What Is a Marketing Harness?](https://cassandra.app/learn/what-is-a-marketing-harness): How a harness runs many AI marketing agents with shared memory, rules and tools.
- [The Marketing Engineer](https://cassandra.app/learn/marketing-engineer): The role that builds and runs marketing systems: data, automation, agents and incrementality.

## Product Updates

- [Always-On Incrementality Launch](https://cassandra.app/product-updates/always-on-incrementality): Continuous holdout group measurement without periodic experiment setup.
- [GeoMatch Launch](https://cassandra.app/product-updates/geomatch): Automated geo-experiment design and analysis for channel incrementality validation.
- [Meridian Integration](https://cassandra.app/product-updates/meridian): Integration with Google's open-source MMM framework.
- [Performance Measurement Page](https://cassandra.app/product-updates/performance-page): Diagnostic tool that evaluates a trained Marketing Mix Model's reliability for planning. Automatically scores each marketing channel across three dimensions: historical fit, forecast stability, and ROI confidence interval width: and returns a per-channel verdict (scale safely, run a test first, don't optimize yet). When a channel's confidence interval is too wide for defensible budget decisions, it triggers a one-click geo-incrementality experiment designed automatically.

## Case Studies

- [Gina Tricot](https://cassandra.app/case-studies/gina-tricot): Fashion eCommerce: MMM-driven budget reallocation improving incremental ROAS.
- [Charity Water](https://cassandra.app/case-studies/charity-water): Non-profit media optimization using MMM.
- [Spedire.com](https://cassandra.app/case-studies/spedire-com): eCommerce logistics: +13% conversions with same ad spend via MMM budget reallocation.
- [CURA of Sweden](https://cassandra.app/case-studies/cura-of-sweden): DTC beauty brand: +82% conversions through MMM-guided channel optimization.
- [Velasca](https://cassandra.app/case-studies/velasca): Italian luxury footwear: MMM for cross-channel budget allocation.
- [Boscolo Viaggi](https://cassandra.app/case-studies/boscolo-viaggi): Travel brand MMM implementation.
- [Treedom (MMM + Tree Planting)](https://cassandra.app/case-studies/marketing-mix-modeling-and-tree-planting): Sustainability brand MMM case study.

## Industry Pages

- [eCommerce](https://cassandra.app/industry/ecommerce): MMM for eCommerce brands.
- [B2B](https://cassandra.app/industry/b2b): MMM for B2B SaaS and lead generation.
- [Fintech](https://cassandra.app/industry/fintech): MMM for financial services and fintech.
- [Universities](https://cassandra.app/industry/universities): MMM for higher education.
- [Charity](https://cassandra.app/industry/charity): MMM for non-profit organizations.

## Learn: Marketing Mix Modeling

- [Complete Guide to Marketing Mix Modeling 2026](https://cassandra.app/learn/complete-guide-marketing-mix-modeling-2026): Comprehensive MMM guide covering methodology, data requirements, and implementation.
- [MMM Calibration and Experiment Quality (EQS Framework)](https://cassandra.app/learn/mmm-calibration-experiment-quality-eqs-framework): How to filter low-quality experiments before calibrating your MMM. Proprietary EQS scoring framework.
- [The Cassandra Method: Building MMM Stakeholder Trust](https://cassandra.app/learn/the-cassandra-method-building-trust-in-your-marketing-mix-model-adoption): 7-step framework for overcoming stakeholder resistance to MMM.
- [Marketing Mix Modeling for Marketers](https://cassandra.app/learn/marketing-mix-modeling-for-marketers): Introduction to MMM for non-technical marketing teams.
- [Marketing Mix Modeling for Performance Marketing](https://cassandra.app/learn/marketing-mix-modeling-for-performance-marketing): MMM 101 for performance teams: 5-step workflow and data requirements.
- [No-Code MMM Software for Non-Tech Teams](https://cassandra.app/learn/the-benefits-of-a-no-code-ui-builder-in-marketing-mix-modeling): Why Cassandra's no-code interface makes MMM accessible without data science resources.
- [MMM Algorithms: Facebook Robyn, LightweightMMM, Uber Orbit Compared](https://cassandra.app/learn/a-deep-dive-into-the-algorithm-behind-media-mix-modeling-and-optimization): Comparing open-source MMM algorithms.
- [MMM with Excel: Introduction](https://cassandra.app/learn/how-to-marketing-mix-modeling-mmm-with-excel-intro): Step-by-step MMM tutorial using Excel with AdStock and diminishing returns.
- [MMM Python: Nevergrad Auto-tuning](https://cassandra.app/learn/marketing-mix-modeling-using-python-auto-tuning-for-diminishing-returns-and-adstock-using-nevergrad): Hyperparameter optimization for MMM using Python and Nevergrad.
- [Diminishing Returns in Advertising](https://cassandra.app/learn/diminishing-returns-for-dummies): How MMM identifies the saturation point and diminishing returns curve for each channel.
- [Adstock Guide](https://cassandra.app/learn/adstock-guide): Adstock transformation explained: carryover effects in MMM.
- [MMM Case Study: +13% Conversions Same Budget](https://cassandra.app/learn/mmm-case-study-13-in-conversions-with-the-same-budget-invested): Real MMM implementation with measurable budget reallocation results.
- [ROI Optimization with Data-Driven MMM](https://cassandra.app/learn/achieving-roi-optimization-through-data-driven-marketing-mix-modeling): 3-step process for eliminating waste and maximizing ROI using MMM.
- [Marketing Mix Modeling for Agencies](https://cassandra.app/learn/marketing-mix-modeling-an-opportunity-for-agencies): How agencies use MMM to add measurement value for clients.
- [When to Start Using MMM for Ecommerce](https://cassandra.app/learn/when-you-should-start-thinking-using-marketing-mix-modeling-for-your-e-commerce-business): Signals indicating your brand is ready for MMM (€25K/month threshold).
- [How to Automate MMM Data Feed](https://cassandra.app/learn/how-to-automate-marketing-mix-modeling-with-an-automated-mmm-data-feed-ft-two-minute-reports): Automating MMM data collection using Two Minute Reports.
- [How to Use MMM to Increase Marketing ROI](https://cassandra.app/learn/how-to-use-mmm-to-increase-your-marketing-roi): Founder walkthrough of MMM applied to agency clients.
- [Automating Budget Allocation](https://cassandra.app/learn/automating-budget-allocation-for-predictable-sales-outcomes): How MMM powers automated, data-driven budget allocation.
- [Daily vs Weekly MMM Granularity](https://cassandra.app/learn/marketing-mix-modeling-daily-or-weekely-granularity): When to use daily vs weekly data in MMM models.
- [5 Metrics to Assess MMM Accuracy](https://cassandra.app/learn/5-metrics-to-assess-accuracy-in-marketing-mix-modeling): How to validate and assess the quality of an MMM model.
- [Bypassing Cookie Restrictions with MMM](https://cassandra.app/learn/bypassing-cookies-restrictions-through-marketing-mix-modeling): Why MMM is the cookieless measurement alternative to platform attribution.
- [Best MMM Software Tools](https://cassandra.app/learn/the-best-media-mix-modeling-tools-for-your-business-success): Comparison of top MMM tools on the market.
- [Power of Media Mix Modeling Guide](https://cassandra.app/learn/unlocking-the-power-of-media-mix-modeling-a-comprehensive-guide): Comprehensive guide to MMM benefits and applications.

## Learn: Incrementality & Geo Testing

- [Understanding Incrementality Testing](https://cassandra.app/learn/unlocking-marketing-success-the-power-of-incrementality-testing): How to measure the true causal impact of marketing campaigns.
- [Understanding Incremental Lift](https://cassandra.app/learn/understanding-incremental-lift-a-comprehensive-guide): What incremental lift is and how to measure it.
- [Understanding Holdout Groups](https://cassandra.app/learn/understanding-holdout-groups-a-key-to-accurate-data-analysis): How holdout groups work as marketing experiment control groups.
- [Holdout Group Experiment Accuracy](https://cassandra.app/learn/maximizing-experiment-accuracy-with-holdout-groups-a-comprehensive-guide): How to set up, analyze, and communicate holdout test results.
- [Geo Experiments Guide](https://cassandra.app/learn/unlocking-insights-with-geo-experiments-a-comprehensive-guide): How geo-based incrementality testing works across regions.
- [Understanding Matched Market Testing](https://cassandra.app/learn/understanding-matched-market-testing-a-comprehensive-guide): Matched market test design and analysis.
- [Incrementality Testing on Meta Ads](https://cassandra.app/blog/incrementality-testing-on-meta-ads-an-ecommerce-success-story): Case study of incrementality test on Meta Ads for an eCommerce brand.
- [Incremental Impact Assessment](https://cassandra.app/learn/understanding-the-importance-of-incremental-impact-assessment-in-modern-business-strategies): How to measure and apply incremental metrics for business growth.
- [Incremental Conversions Analysis](https://cassandra.app/learn/mastering-incremental-conversion-analysis-a-comprehensive-guide): How to measure incremental conversions vs organic baseline.
- [Incrementality Formula Guide](https://cassandra.app/learn/understanding-the-incrementality-formula-a-comprehensive-guide): The math behind incrementality measurement.
- [Incremental Revenue: Channels and Campaigns](https://cassandra.app/learn/unlocking-growth-strategies-to-drive-incremental-revenue): How to identify and grow incremental revenue from marketing.
- [Incremental Metrics for Business Growth](https://cassandra.app/learn/unlocking-business-growth-with-incremental-metrics-a-comprehensive-guide): Key incrementality metrics and how to use them for strategic decisions.
- [Incremental Value Measurement](https://cassandra.app/learn/unlocking-business-growth-the-power-of-incremental-value-measurement): How to measure the true added value of marketing activities.
- [Incremental ROAS](https://cassandra.app/learn/maximizing-marketing-efficiency-the-power-of-incremental-roas): How iROAS differs from platform ROAS and why it matters.
- [Cross-Stack Incrementality](https://cassandra.app/learn/unlocking-the-power-of-cross-stack-incrementality-in-modern-marketing): Measuring incrementality across multiple channels simultaneously.
- [MMM + Incrementality + Attribution Integration](https://cassandra.app/learn/how-to-integrate-mmm-incrementality-testing-and-attributions): How to triangulate MMM, incrementality tests, and attribution models.
- [Future of Measurement: MTA + MMM + Incrementality](https://cassandra.app/learn/future-of-measurement-triangulating-mta-mmm-and-incrementality-testing): How the three measurement methods complement each other.

## Learn: Measurement & Budget Allocation

- [Marketing Budget Optimization: Effective Strategies](https://cassandra.app/learn/effective-strategies-for-marketing-budget-planning-in-2023): Data-driven strategies for marketing budget planning and allocation.
- [Campaign Performance Tracking Guide](https://cassandra.app/learn/maximizing-success-a-comprehensive-guide-to-campaign-performance-tracking): How to track and measure marketing campaign performance.
- [Marketing ROI Optimization](https://cassandra.app/learn/maximizing-success-strategies-for-marketing-roi-optimization): Strategies for maximizing marketing return on investment.
- [Ad Spend Analysis Guide](https://cassandra.app/learn/maximizing-roi-a-comprehensive-guide-to-ad-spend-analysis): How to analyze and optimize advertising spend across channels.
- [Campaign Effectiveness Strategies](https://cassandra.app/learn/maximizing-campaign-effectiveness-strategies-for-success): Data-driven approaches to improving campaign performance.
- [Channel Impact Analysis](https://cassandra.app/learn/understanding-channel-impact-analysis-strategies-and-benefits): How to analyze which channels are driving business outcomes.
- [Privacy-First Measurement](https://cassandra.app/learn/unlocking-the-future-of-data-with-privacy-first-measurement): Measurement frameworks that work without user-level tracking.
- [Marketing Measurement Techniques](https://cassandra.app/learn/mastering-marketing-measurement-techniques-a-comprehensive-guide): Key methods for measuring marketing effectiveness.
- [Digital Marketing Metrics Guide](https://cassandra.app/learn/mastering-digital-marketing-metrics-key-indicators-for-success): Key performance indicators for digital marketing measurement.
- [Conversion Lift Analysis](https://cassandra.app/learn/maximizing-your-marketing-roi-with-conversion-lift-analysis): How to use conversion lift to measure true campaign impact.
- [Marketing Portfolio Incrementality](https://cassandra.app/learn/maximizing-roi-understanding-marketing-portfolio-incrementality): How to measure and optimize incrementality across your full marketing portfolio.
- [Future of Marketing Measurements](https://cassandra.app/learn/the-future-of-marketing-measurements): How MTA, MMM, and incrementality testing will evolve.
- [Google Analytics Tracking Limitations](https://cassandra.app/learn/how-does-google-analytics-tracking-work-and-why-it-will-become-worse-over-time): Why GA4 tracking accuracy is declining and what to use instead.
- [Cross-Channel Measurement Strategies](https://cassandra.app/learn/mastering-cross-channel-measurement-strategies-for-effective-marketing-analytics): How to measure marketing effectiveness across all channels.

## Learn: Attribution

- [Why Attribution Misleads Budget Decisions](https://cassandra.app/learn/why-attribution-misleads-budget-decisions): Real MMM case showing how last-click attribution skews channel ROAS by up to 19x.
- [Attribution vs Incrementality](https://cassandra.app/learn/understanding-attribution-vs-incrementality-key-differences-and-applications): Key differences and when to use each measurement approach.
- [Marketing Attribution Models](https://cassandra.app/learn/understanding-the-different-types-of-marketing-attribution-models): First-touch, last-touch, linear, time decay, position-based, and data-driven models explained.
- [Data-Driven Attribution](https://cassandra.app/learn/unlocking-marketing-success-with-data-driven-attribution): How data-driven attribution uses machine learning to assign credit.
- [Real-Time Attribution](https://cassandra.app/learn/unlocking-marketing-success-with-real-time-attribution-a-comprehensive-guide): How to implement and optimize real-time attribution for marketing decisions.
- [Revenue Attribution Guide](https://cassandra.app/learn/unlocking-business-growth-the-ultimate-guide-to-revenue-attribution): The complete guide to understanding what drives revenue from marketing.
- [Understanding Marketing Attribution Models](https://cassandra.app/learn/understanding-marketing-attribution-models-a-comprehensive-guide): Overview of all major marketing attribution frameworks.
- [Cross-Platform Attribution](https://cassandra.app/learn/mastering-cross-platform-attribution-strategies-for-accurate-marketing-insights): How to measure attribution across devices and platforms.

## Learn: Experiment Design & Methodology

- [Marketing Experiment Design](https://cassandra.app/learn/mastering-marketing-experiment-design-a-comprehensive-guide): How to design valid marketing experiments with proper controls.
- [A/B Testing Strategies](https://cassandra.app/learn/mastering-a-b-testing-strategies-for-effective-experiments): Key strategies for effective A/B testing in marketing.
- [A/B Testing Techniques](https://cassandra.app/learn/mastering-a-b-testing-techniques-for-data-driven-decision-making): Advanced A/B testing methods for data-driven decisions.
- [Randomized Controlled Trials for Marketers](https://cassandra.app/learn/the-importance-of-randomized-controlled-trials-in-modern-medicine): How RCT principles from medicine apply to marketing experiments.
- [Control Group Analysis](https://cassandra.app/learn/the-importance-of-control-group-analysis-in-scientific-research): How control groups work and why they matter for marketing measurement.
- [Predictive Analytics in Marketing](https://cassandra.app/learn/harnessing-predictive-analytics-in-marketing-for-enhanced-customer-engagement): How predictive analytics improves campaign targeting and ROI.

## Blog: Research & Benchmarks

- [Marketing Attribution Software Is Lying to You: 792-Model Proof](https://cassandra.app/blog/marketing-attribution-software-analysis): Platform attribution over-reports by 1.2x-2.3x. Evidence from 792 MMMs across 194 advertisers.
- [2026 Media Effectiveness Benchmarks](https://cassandra.app/blog/2026-media-effectiveness-benchmarks): ROI, risk, and confidence intervals across 83 channels and $1.85B in measured ad spend. Built on 1,221 MMMs.
- [Google Ads Benchmarks 2026](https://cassandra.app/blog/google-ads-benchmarks-2026): Incremental ROI across 7 Google Ads campaign types from 792 MMM models.
- [Risk-Adjusted ROAS: The Formula That Fixes Standard ROAS](https://cassandra.app/blog/risk-adjusted-roas): Why standard ROAS misranks channels by up to 11x and how risk-adjusted ROAS corrects it.
- [Beyond ROI: Risk-Adjusted Returns in Marketing](https://cassandra.app/blog/beyond-roi-risk-adjusted-returns): How the Marketing Sharpe Ratio reveals true channel performance.
- [Marketing Value at Risk](https://cassandra.app/blog/marketing-value-at-risk): Quantifying the maximum revenue loss from your ad budget. Built on 1,221 MMMs.
- [Marketing Budget Optimization: The Efficient Frontier Framework](https://cassandra.app/blog/marketing-efficient-frontier): How to apply Modern Portfolio Theory to marketing budget allocation.
- [Marketing Budget as a Portfolio](https://cassandra.app/blog/marketing-budget-portfolio-construction): Why CMOs should think like fund managers when allocating marketing spend.
- [Marketing Capital Allocation Framework](https://cassandra.app/blog/marketing-capital-allocation-framework): A 4-step quantitative framework for optimal marketing budget allocation.
- [The Incrementality Multiplier: A Diminishing Returns Problem](https://cassandra.app/blog/incrementality-multipliers-are-wrong): Why Google and Meta's conversion lift calibration fails at scale.
- [Which Campaign to Scale When MMM Stops at Channel Level](https://cassandra.app/blog/incrementality-distribution-campaign-level): How to distribute channel-level MMM incrementality to campaign-level decisions without holdout tests.

## Blog: Company & Insights

- [Cassandra Named Top Marketing Analytics Tool to Watch in 2025](https://cassandra.app/blog/cassandra-top-marketing-analytics-tools-to-watch-in-2025): Tekpon recognition of Cassandra's unified MMM and incrementality approach.
- [Cassandra $2M Seed Round](https://cassandra.app/blog/cassandra-2m-seed-round): Seed funding announcement for Cassandra.
- [eCommerce MMM Case Study: +13% Conversions, Same Ad Spend](https://cassandra.app/blog/case-study-how-to-increase-13-in-conversions-using-the-same-budget-with-cassandra): Full case study of MMM-driven budget reallocation delivering +13% conversions and +42% profit.

## Company and hubs

- [About Cassandra: The Marketing Brain Company](https://cassandra.app/about-us): Cassandra builds the marketing brain: measurement, planning and AI agents that help marketing teams grow without adding headcount. Meet the team.
- [The Cassandra Manifesto: Marketing That Learns](https://cassandra.app/manifesto): Why marketing should learn from every move: evidence before action, a forecast before the result, and a marketing brain that checks its own work.
- [MMM and Incrementality Academy](https://cassandra.app/academy): Explore lessons on marketing mix modeling, incrementality testing and evidence-based budget decisions.
- [Marketing Measurement and AI Ads Tools Compared](https://cassandra.app/compare): Compare AI ads tools, MMM, incrementality and attribution platforms: what each says it does, where its decisions come from and who it suits, side by side.
- [Marketing Decision and Execution Use Cases](https://cassandra.app/use-cases): Explore practical marketing use cases, from causal measurement and budget decisions to supported actions and outcome checks.
- [Cassandra Customers | Marketing Measurement](https://cassandra.app/logos): Explore brands using Cassandra for marketing measurement. Read customer stories for the methods, outcomes and conditions behind the results.

## Compare: platforms and methods

- [Attribution vs Contribution: The Difference and When to Use Each](https://cassandra.app/compare/attribution-vs-contribution): Attribution credits the touchpoints on tracked conversion paths. Contribution measures how much each channel added to total revenue. How each works, and which question each one answers.
- [Bayesian vs Frequentist MMM: Differences and When to Use Each](https://cassandra.app/compare/bayesian-vs-frequentist-marketing-mix-modeling): How Bayesian and frequentist marketing mix models treat parameters, prior knowledge and uncertainty, what each outputs, and when each approach is the better choice.
- [Best AI Marketing Tools for Ads in 2026: Six Compared](https://cassandra.app/compare/best-ai-marketing-tools): Six AI marketing tools for ads in 2026: how each automates ads, where its decisions come from, how it is priced and who it suits. We are one of the six.
- [Best Incrementality Testing Tools 2026: Five Compared](https://cassandra.app/compare/best-incrementality-testing-tools): Five incrementality testing tools whose sites describe geo experiments, the free open-source options, who each suits and how to choose. We are one of the five.
- [Best Marketing Mix Modeling Software 2026: 5 Platforms](https://cassandra.app/compare/best-marketing-mix-modeling-software): Five marketing mix modeling platforms and three free libraries, Robyn, Meridian and PyMC-Marketing: what each says it does, who it suits, how to choose.
- [Build vs Buy Marketing Mix Modeling: Costs, Trade-offs, How to Decide](https://cassandra.app/compare/build-vs-buy-marketing-mix-modeling): What building a marketing mix model in-house involves, what buying MMM software includes, how the costs compare over time, and when each path is the better choice.
- [Cassandra vs AdCreative.ai: AI Ad Creative or Growth Brain](https://cassandra.app/compare/cassandra-vs-adcreative-ai): Cassandra vs AdCreative.ai: AdCreative.ai generates AI ad creatives and scores them before launch. Cassandra measures what each ad added and acts on it.
- [Cassandra vs Birch (Revealbot): AI Ad Automation Compared](https://cassandra.app/compare/cassandra-vs-birch): Cassandra vs Bïrch: Bïrch runs AI-assisted ad rules your team writes across Meta, Google, Snap and TikTok. Cassandra decides from causal measurement.
- [Cassandra vs Haus: Incrementality or Marketing Brain](https://cassandra.app/compare/cassandra-vs-haus): Cassandra vs Haus: Haus leads with incrementality experiments and a Causal MMM. Cassandra, an AI marketing brain, also does the work. Who each one suits.
- [Cassandra vs Lifesight: Measurement or Marketing Brain](https://cassandra.app/compare/cassandra-vs-lifesight): Cassandra vs Lifesight: Lifesight unifies causal MMM, testing and attribution. Cassandra, an AI marketing brain, also does the work. Who each one suits.
- [Cassandra vs LiftLab: Agile MMM or Marketing Brain](https://cassandra.app/compare/cassandra-vs-liftlab): Cassandra vs LiftLab: LiftLab loops agile MMM with incrementality tests. Cassandra, an AI marketing brain, also does the work. How they differ, who each suits.
- [Cassandra vs Madgicx: AI Ads Tool or AI Marketing Brain](https://cassandra.app/compare/cassandra-vs-madgicx): Cassandra vs Madgicx: an agentic Meta ads platform with an AI Marketer, rules and tracking, or an AI marketing brain that automates ads and measures what works.
- [Cassandra vs MASS Analytics: MMM or Marketing Brain](https://cassandra.app/compare/cassandra-vs-mass-analytics): Cassandra vs MASS Analytics: MASS runs an always-on MMM in your warehouse. Cassandra, an AI marketing brain, also does the work. Who each one suits.
- [Cassandra vs Measured: Measurement or Marketing Brain](https://cassandra.app/compare/cassandra-vs-measured): Cassandra vs Measured: Measured pairs tests with media mix modeling. Cassandra, an AI marketing brain, also does the work. How they differ, who each suits.
- [Cassandra vs Northbeam: Measurement or Marketing Brain](https://cassandra.app/compare/cassandra-vs-northbeam): Cassandra vs Northbeam: Northbeam sells attribution, MMM and lift tests in one suite, and Cassandra, an AI marketing brain, also does the work. Who each suits.
- [Cassandra vs Recast: Bayesian MMM or Marketing Brain](https://cassandra.app/compare/cassandra-vs-recast): Cassandra vs Recast: both pair a Bayesian model with geo experiments. Recast offers a planning toolset; Cassandra, an AI marketing brain, also does the work.
- [Cassandra vs Rockerbox: Attribution, MMM and Testing](https://cassandra.app/compare/cassandra-vs-rockerbox): What Rockerbox publishes, what Cassandra is, how each combines attribution, marketing mix modeling and testing, what each says about price, and who each suits.
- [Cassandra vs SegmentStream: Methods and Fit Compared](https://cassandra.app/compare/cassandra-vs-segmentstream): What SegmentStream says it does, what Cassandra is, how each turns measurement into a budget decision, what each says about pricing, and which team each suits.
- [Cassandra vs Sellforte: MMM Platform or Marketing Brain](https://cassandra.app/compare/cassandra-vs-sellforte): Cassandra vs Sellforte: Sellforte unifies MMM, testing and attribution for retail. Cassandra, an AI marketing brain, also does the work. Who each one suits.
- [Cassandra vs Smartly: AI Ads Automation Compared](https://cassandra.app/compare/cassandra-vs-smartly): Cassandra vs Smartly: Smartly automates creative, media and budgets for enterprise teams. Cassandra, an AI marketing brain, also learns from causal measurement.
- [Cassandra vs Triple Whale: AI Ads Agents and Measurement](https://cassandra.app/compare/cassandra-vs-triple-whale): Triple Whale's Moby AI ads agent and Compass measurement, next to Cassandra, an AI marketing brain that does the work from a model checked by geo tests.
- [First Touch vs Last Touch vs Multi Touch Attribution: How to Choose](https://cassandra.app/compare/first-touch-vs-last-touch-vs-multi-touch-attribution): How first-touch, last-touch and multi-touch attribution assign credit, what each is best used for, the drawback of each, and how to choose between them.
- [Free Marketing Mix Modeling Software: Open-Source Options and Real Costs](https://cassandra.app/compare/free-marketing-mix-modeling-software): The free marketing mix modeling options: Robyn, Meridian and PyMC-Marketing, what each needs to run, how rare vendor trials are, and when free is the better choice.
- [Geo Experiments vs Holdout Tests: Differences and When to Use Each](https://cassandra.app/compare/geo-experiments-vs-holdout-tests): What a geo experiment is, what a holdout test is, where a geo holdout fits between them, how they differ, and which design suits which channels.
- [Transparent vs Black Box MMM: What to Check Before You Trust a Model](https://cassandra.app/compare/glass-box-vs-black-box-mmm): What makes a marketing mix model transparent or a black box, six things to check for, four questions to ask in a demo, and when a black box is acceptable.
- [Hierarchical MMM: How Partial Pooling Works and When to Use It](https://cassandra.app/compare/hierarchical-vs-non-hierarchical-mmm): What a hierarchical marketing mix model is, how partial pooling links markets or product lines, how it compares with one model per market, and when each approach is the better choice.
- [Incrementality Testing vs A/B Testing: Differences and When to Use Each](https://cassandra.app/compare/incremental-lift-testing-vs-a-b-testing): An A/B test compares two versions of an ad or a page. An incrementality test compares advertising against no advertising. How each works, how to calculate lift, and when to use each.
- [MMM Agency vs MMM Software: What Each Offers and How to Choose](https://cassandra.app/compare/marketing-mix-modeling-agency-vs-software): What a marketing mix modeling agency or consultancy provides, what MMM software provides, how they differ on cost, speed, control and independence, and when each is the better choice.
- [MMM vs Incrementality Testing: Differences and When to Use Each](https://cassandra.app/compare/marketing-mix-modeling-vs-incrementality-testing): How marketing mix modeling and incrementality testing work, how they differ, when each is the better choice, and how test results calibrate a model.
- [MMM vs Multi-Touch Attribution: Differences and When to Use Each](https://cassandra.app/compare/marketing-mix-modeling-vs-multi-touch-attribution): How marketing mix modeling and multi-touch attribution work, where they differ, when each is the better choice, and how far apart they land on the same weeks.
- [Measured Alternatives: 8 MMM and Incrementality Platforms](https://cassandra.app/compare/measured-alternatives): Eight alternatives to Measured that pair a marketing mix model with incrementality testing: what each says it does, how it is bought, and who it suits.
- [Robyn vs Meridian vs PyMC-Marketing: Open-Source MMM Compared](https://cassandra.app/compare/robyn-vs-meridian-vs-pymc-marketing): How Meta's Robyn, Google's Meridian and PyMC-Marketing differ in statistical approach, language, uncertainty, experiment calibration and geo modeling, and which suits which team.
## Use cases

- [Advising clients beyond the channels an agency manages](https://cassandra.app/use-cases/advising-beyond-managed-channels): A model built on a client's whole media mix, not just the channels an agency buys, so advice on promo, lifecycle, dealer-tier or offline spend rests on evidence instead of an educated guess.
- [Allocating fintech budget across product lines and markets](https://cassandra.app/use-cases/allocating-across-product-lines-and-markets): How fintech platforms with several product lines, and other multi-market businesses, split budget across lines and markets by modeling each one with the others' spend included as context rather than guessed.
- [Attributing direct mail, DRTV and face-to-face fundraising](https://cassandra.app/use-cases/attributing-direct-mail-and-drtv-fundraising): A channel-level read that puts direct mail, DRTV, face-to-face and telefundraising into the same model as digital, without waiting on a click that never happens.
- [Budget reallocation when measurement numbers disagree](https://cassandra.app/use-cases/budget-reallocation-when-numbers-disagree): How marketing leaders arbitrate between contradictory measurement numbers and move budget on a causal read that no platform can grade in its own favor.
- [Building a standing incrementality testing program](https://cassandra.app/use-cases/building-a-standing-incrementality-testing-program): How to sequence incrementality tests by risk and priority, agreed as a program before the first one runs, so no single result is a solo bet.
- [Building in-house measurement capability at an agency](https://cassandra.app/use-cases/building-in-house-measurement-capability): How agencies move from routing every measurement question through a vendor to running scenarios and answering clients with their own trained team.
- [Assessing data readiness before committing to MMM](https://cassandra.app/use-cases/can-data-support-mmm-yet): A go or no-go check on channel history, spend, and variation, with a staged path when a marketing mix model is not the right next step yet.
- [Checking marketing forecast accuracy against actuals](https://cassandra.app/use-cases/checking-marketing-forecast-accuracy): How to pin a prediction down before the outcome is known and keep the comparison on record, so a wrong call is visible rather than explained away afterwards.
- [Consolidating a fragmented marketing measurement stack](https://cassandra.app/use-cases/consolidating-a-fragmented-measurement-stack): How brands stop reconciling disagreeing tools by hand, and how agencies fold per-client vendor costs into one system without losing their seat at the table.
- [De-risking a new channel before committing budget](https://cassandra.app/use-cases/de-risking-a-new-channel-before-committing-budget): How brands and their marketing leads test a new channel at bounded exposure before scaling spend, instead of committing budget on an unproven read.
- [Defending brand spend when payback is weeks away](https://cassandra.app/use-cases/defending-brand-spend-when-payback-is-weeks-away): How to settle the recurring argument over upper-funnel spend with a number sized to its real payback window, instead of re-litigating the same case every meeting.
- [Defending brand spend when payback takes months in fintech](https://cassandra.app/use-cases/defending-brand-spend-when-payback-takes-months): How fintech marketing leaders size and defend a brand or top-of-funnel budget whose payback lags months, with a plateau attached to the number instead of a belief.
- [Getting marketing measurement used instead of shelved](https://cassandra.app/use-cases/getting-measurement-used-instead-of-shelved): Why a technically sound measurement model still gets ignored, and what changes when a decision owner and a change-management path are built in before the first output is delivered.
- [Growing efficiency and new enrollments at once](https://cassandra.app/use-cases/growing-efficiency-and-new-enrollments-at-once): How education businesses read cost efficiency and new-enrollment growth from one number instead of two that can move in opposite directions.
- [Growing inside a marketing efficiency ratio (MER) target](https://cassandra.app/use-cases/growing-inside-a-marketing-efficiency-ratio-target): What a blended efficiency ratio hides, a margin shift or dead spend underneath it, read from a brand's own history instead of waiting for the quarter to close.
- [Growing ROAS and new customer acquisition](https://cassandra.app/use-cases/growing-roas-and-new-customer-acquisition): How to see whether a spend decision is winning genuinely new customers or just recycling existing ones at a better rate, before the two goals get declared in conflict.
- [Testing a dominant search channel for incrementality](https://cassandra.app/use-cases/is-a-dominant-search-channel-incremental): Testing whether a channel that dominates the mix is creating growth or capturing demand that already existed, without pausing it to find out.
- [Testing retargeting and brand search for incrementality](https://cassandra.app/use-cases/is-retargeting-and-brand-search-incremental): Separating retargeting and brand-search spend that creates demand from spend that only catches demand already on its way, before anything gets cut.
- [Proving agency-managed media spend is working](https://cassandra.app/use-cases/keeping-clients-by-proving-managed-spend-works): A causal read of managed spend that holds up under a client's own scrutiny, when platforms disagree on revenue, and when blame for someone else's miss lands on the account team.
- [Making the business case to finance](https://cassandra.app/use-cases/making-the-business-case-to-finance): How marketing leaders and agencies translate campaign performance into incremental revenue, cost savings, and payback figures that reconcile with the books.
- [Marketing measurement ready when fintech planning starts](https://cassandra.app/use-cases/marketing-measurement-that-is-ready-when-planning-starts): How fintech marketing teams keep a model current enough to inform the plan before the planning window closes, instead of waiting for an answer that arrives after the decision is made.
- [Measuring offline and event media with no clicks in fintech](https://cassandra.app/use-cases/measuring-offline-and-event-media-with-no-clicks): How fintech brands, and the agencies serving them, read billboards, events, print, and other channels with no click trail against the outcomes those channels are meant to drive.
- [Measuring offline and marketplace sales lift from paid media](https://cassandra.app/use-cases/measuring-offline-and-marketplace-sales-lift): A read on how much marketplace and in-store revenue paid media already creates, when the sale never touches the site and reporting never sees it.
- [Measuring TV and radio against dealer visits in automotive](https://cassandra.app/use-cases/measuring-tv-and-radio-against-dealer-visits): How a dealer-network agency brings offline media into the same read as digital lead generation, so TV and radio spend gets checked against dealer visits instead of the vendor's own reporting.
- [Passing a client's methodology audit on a marketing model](https://cassandra.app/use-cases/passing-a-client-methodology-audit): What it takes to answer a client's own analysts when they ask how a marketing mix model was built, without deferring to a vendor nobody can explain.
- [Pricing measurement as an agency service line](https://cassandra.app/use-cases/pricing-measurement-as-an-agency-service-line): How agencies build a resellable measurement service line: one plan covering several client models, priced on spend, on a six-month commitment rather than a year.
- [Promotion incrementality measurement](https://cassandra.app/use-cases/promotion-incrementality-measurement): Separating the revenue a discount actually created from the revenue it only discounted, read from a brand's own promotional history.
- [Quantifying a marketing baseline before measuring lift](https://cassandra.app/use-cases/quantifying-a-marketing-baseline): How marketing leaders separate what spend actually caused from demand that would have arrived anyway, using historical variation instead of a guess.
- [Re-measuring when the target shifts to profitability](https://cassandra.app/use-cases/re-measuring-when-the-target-shifts-to-profitability): How a model built for a growth objective gets redirected at a profitability or payback target without restarting the measurement programme.
- [Rebuilding trust in measurement after a failed vendor](https://cassandra.app/use-cases/rebuilding-trust-after-a-failed-measurement-vendor): How to give a burned organization a timestamped record no vendor can quietly revise, so the next model earns trust through auditability rather than a promise of accuracy.
- [Recommending budget allocation to an agency client](https://cassandra.app/use-cases/recommending-budget-allocation-to-a-client): How agencies turn a contested budget question, contradictory platform numbers or a brand-versus-non-brand debate, into a modeled recommendation instead of an educated guess.
- [Recurring donations and donor lifetime value by channel](https://cassandra.app/use-cases/recurring-donations-and-donor-lifetime-value): How nonprofits separate recurring pledge income from one-off gifts and weigh acquisition channels by donor lifetime value against a payback window.
- [Replacing a quarterly agency model with in-house reads](https://cassandra.app/use-cases/replacing-a-quarterly-agency-model-with-in-house-reads): How brands stop waiting on an external quarterly deliverable, or on the agency buying the media, for a number they need on their own trading calendar.
- [Retail demand forecasting by product category](https://cassandra.app/use-cases/retail-demand-forecasting-by-product-category): How to commit category media budget before sell-through data exists, using comparable prior launches instead of a split set by feel.
- [Running incrementality tests around the academic calendar](https://cassandra.app/use-cases/running-incrementality-tests-around-the-academic-calendar): How to replace a failed do-it-yourself pause test with a designed experiment timed to enrollment peaks, and carry each season's result into the next.
- [Running measurement across a whole agency client book](https://cassandra.app/use-cases/running-measurement-across-a-whole-client-book): How agencies keep one measurement team ahead of a growing client roster, from onboarding a new account's data to modeling a dealer network's media without adding headcount for every win.
- [Testing saturation on a dominant fintech acquisition channel](https://cassandra.app/use-cases/saturation-on-the-biggest-acquisition-channel): How fintech marketing leaders test whether their single dominant acquisition channel still has room to grow before committing the next budget cycle to a guess.
- [Separating marketing results from market noise](https://cassandra.app/use-cases/separating-marketing-results-from-market-noise): How marketing leaders test whether a competitor move or an unrelated market event explains a swing in performance, instead of guessing either way.
- [Shifting fundraising budget from direct mail to digital](https://cassandra.app/use-cases/shifting-fundraising-budget-from-direct-mail-to-digital): The channel-level evidence nonprofits use to move budget out of a legacy offline channel, including credit for leads that close somewhere else.
- [Steering a funnel that pays back months later in fintech](https://cassandra.app/use-cases/steering-a-funnel-that-pays-back-months-later): How fintech and b2b-saas marketing teams read funnel efficiency long before a funded account, activated merchant, or closed deal shows the actual result.
- [Testing incrementality without hurting live performance](https://cassandra.app/use-cases/testing-incrementality-without-hurting-performance): Proving which channels are incremental without splitting the account or pausing spend, with exposure agreed before the test starts and historical variation read as an experiment that already happened.
- [Testing a platform beta program before funding it](https://cassandra.app/use-cases/testing-platform-beta-programs-before-funding-them): How an agency reads a platform's own proposed programme on the client's account before passing a recommendation on.
- [Timing ecommerce spend around peak trading season](https://cassandra.app/use-cases/timing-spend-around-peak-trading-season): How marketing leaders and their agencies size the lead time a channel needs before a major sales season, using past cycles instead of a rule of thumb.
- [Timing education spend around the enrollment calendar](https://cassandra.app/use-cases/timing-spend-around-the-enrollment-calendar): How to time a cut-or-scale read backward from the enrollment peak, and build exam or funding-calendar shocks into the read instead of treating them as surprises.
- [Turning measurement into recommendations across a book](https://cassandra.app/use-cases/turning-measurement-into-recommendations-across-a-book): How agencies move from backward-looking readouts to tested, per-client scenario recommendations they can defend across an entire book of accounts.
- [Weekly budget allocation with incrementality correction](https://cassandra.app/use-cases/weekly-budget-allocation-with-incrementality): How to move budget between campaigns each week on a figure corrected for known platform overstatement, at campaign level rather than ad-set or intraday depth.
- [Delivering measurement between an agency's quarterly models](https://cassandra.app/use-cases/what-to-deliver-between-quarterly-models): The recurring monthly work, delta readouts, forecast checks, and the quarterly budget reforecast, that fills the gap between full model refreshes on a client retainer.
- [Acting on paid media that has hit saturation](https://cassandra.app/use-cases/when-paid-media-hits-saturation): How marketing leaders find the point where more paid media spend stops returning more, before committing the next tranche of growth budget.
- [Winning agency pitches with measurement capability](https://cassandra.app/use-cases/winning-pitches-with-measurement): What it takes to quote, price, and back up a measurement capability inside a new-business pitch, from a commoditized media market to a client-initiated mandate.