Simba is a no-code Bayesian Marketing Mix Modeling platform that measures media effectiveness, optimizes budgets, and forecasts marketing ROI. Replace spreadsheets, fragmented models, and black-box vendors with one transparent, enterprise-ready platform.
Built on the open-source PyMC-Marketing framework by PyMC Labs, Simba combines the rigor of Bayesian statistics with an intuitive no-code interface — giving marketing teams enterprise-grade marketing mix modeling without writing a single line of code.
Marketing Mix Modeling (MMM) is a statistical technique that measures the impact of marketing activities on business outcomes like revenue and conversions. Unlike last-click attribution or multi-touch attribution (MTA), MMM uses aggregate data to isolate the incremental contribution of each media channel — accounting for diminishing returns, carryover effects, seasonality, and external factors.
Simba makes MMM accessible to marketing teams who need rigorous measurement without hiring a data science team. See What is Marketing Mix Modeling? for a full explanation.
| Challenge | How Simba Solves It |
|---|---|
| Black-box MMM vendors deliver "trust me" results | Fully transparent — inspect every prior, parameter, and assumption |
| Custom MMM models take months to build and maintain | No-code model configuration with smart defaults — first model in 15 minutes |
| Fragmented tools for measurement, planning, and optimization | End-to-end platform: validate data, measure impact, forecast scenarios, optimize budgets |
| One-size-fits-all models ignore domain expertise | Bayesian priors let you encode business knowledge directly into the model |
| Siloed models across brands and markets | Portfolio modeling for cross-brand and cross-market consistency |
Measure the true incremental impact of every marketing channel using Bayesian causal attribution. Integrate lift test results as likelihood observations to calibrate and validate your model. See Incremental Measurement.
Risk-adjusted budget allocation that accounts for saturation (diminishing returns), adstock (carryover effects), and uncertainty. Optimize across channels with configurable risk tolerance. See Budget Optimization.
Test budget scenarios before spending. Single-scenario prediction with uncertainty bands, what-if analysis, and carryover-aware forecasting. See Scenario Planning.
Configure Bayesian priors, saturation curves, and adstock decay through an intuitive UI. Smart defaults auto-generate starting points based on your data and industry benchmarks. See Model Configuration.
An AI-powered Data Validator checks your data across 10 validation categories before modeling — detecting anomalies, missing values, multicollinearity, and data quality issues. See Data Validator.
Cross-brand and cross-client modeling for agencies and multi-brand organizations. Consistent methodology, comparable KPIs, centralized management. See Portfolio Modeling.
Measure long-term brand effects using Bayesian Vector Autoregression — impulse response functions, forecast error variance decomposition, and long-run equilibrium effects. See Long-Term Effects.
Simba provides a complete workflow for marketing mix modeling:
1. Validate — The Data Validator automatically audits your data for quality issues before modeling.
2. Configure — Set up your model using no-code configuration with smart defaults or custom Bayesian priors.
3. Measure — Run the model and get incremental measurement of every channel's contribution to revenue.
4. Optimize — Use budget optimization and scenario planning to allocate spend for maximum ROI.
Simba uses Bayesian Marketing Mix Modeling rather than frequentist regression. This matters because:
- Uncertainty quantification — every estimate comes with a 94% HDI (Highest Density Interval), so you know how confident to be in each channel's ROI
- Prior knowledge — encode domain expertise (e.g., "TV has longer carryover than paid search") directly into the model
- Lift test calibration — integrate experimental results (lift tests, geo tests) as likelihood observations to validate and improve model accuracy
- Small data friendly — Bayesian models produce reliable estimates even with limited historical data
- Fully transparent — built on open-source PyMC-Marketing, so every model component is inspectable and auditable
Learn more: Bayesian Modeling Explained | Priors & Distributions
- What is Simba? — Product overview and positioning
- Quick Start Guide — Build your first marketing mix model in 15 minutes
- Account Setup — Registration, plans, and project configuration
- Platform Overview — UI walkthrough and navigation
- Marketing Mix Modeling — What MMM is and why it matters
- Bayesian Modeling — The Bayesian approach to media measurement
- Incrementality — Causal attribution and incremental measurement
- Saturation Curves — Diminishing returns and response curves
- Adstock Effects — Carryover, memory decay, and lagged impact
- Priors & Distributions — Configuring Bayesian priors
- Seasonality — Seasonal patterns and trend modeling
- Data Validator — Automated data validation and quality scoring
- Model Configuration — Configuring priors, saturation curves, and adstock decay
- Smart Defaults — Auto-generated model starting points
- Incremental Measurement — Channel attribution and contribution analysis
- Budget Optimization — Risk-adjusted budget allocation
- Scenario Planning — Forecasting and what-if analysis
- Long-Term Effects — Bayesian VAR for brand equity modeling
- Data Requirements — What data you need and supported formats
- Data Preparation — Cleaning and formatting best practices
- Data Validation — How the Data Validator audits your data
- Supported Channels — TV, digital, social, OOH, and more
- Brand Marketers — For in-house marketing teams
- Agencies — Multi-client management and portfolio modeling
- Portfolio Modeling — Cross-brand and cross-market analysis
- Retail & E-commerce — Online and omnichannel retail
- Security Overview — AES-256 encryption, TLS 1.3, Cyber Essentials certified, GDPR compliant
- Glossary — Marketing mix modeling and Bayesian statistics terminology
- PyMC-Marketing & Simba — How the open-source project and platform relate
- Further Reading — Papers, articles, and external resources
- FAQ — Frequently asked questions
- Pricing & Plans — See getsimba.ai for current plans
| Capability | Simba | Google Meridian | Meta Robyn | Custom In-House |
|---|---|---|---|---|
| No-code UI | Yes | No (Python) | No (R) | No |
| Bayesian framework | Yes (PyMC) | Yes (lightweight Bayesian) | Ridge regression | Varies |
| Uncertainty quantification | 94% HDI on all outputs | Limited | No | Varies |
| Budget optimization | Built-in, risk-adjusted | Separate | Basic | Build your own |
| Lift test integration | Yes (likelihood observations) | Yes | Yes (calibration) | Build your own |
| Portfolio / multi-brand | Built-in | No | No | Build your own |
| Long-term effects (VAR) | Built-in (Bayesian VAR) | No | No | Build your own |
| Enterprise security | Cyber Essentials, GDPR | Google Cloud | Self-hosted | Self-managed |
| Time to first model | 15 minutes | Days–weeks | Days–weeks | Months |
See full competitor comparison for details.
- GitHub Issues — Bug reports, feature requests, and support questions
- Email: [email protected]
- Website: getsimba.ai
- Book a demo: Schedule a call
Simba is powered by PyMC-Marketing, the leading open-source library for Bayesian marketing analytics. This means:
- Full transparency — the probabilistic models driving your ROI are inspectable and auditable
- Scientific rigor — built on decades of Bayesian statistics research
- No vendor lock-in — your modeling logic is built on open-source foundations
- Community-driven — benefit from continuous improvements by the PyMC community
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Simba — Bayesian Marketing Mix Modeling platform. Built on PyMC-Marketing by PyMC Labs.