Where should your next dollar go?
Let the evidence guide you.
Understand channel contributions, compare budget scenarios, and inspect uncertainty. Start with an interactive example, then validate with your own data.
What changes when your budget moves?
Explore how diminishing returns affect allocation. Change a channel budget and compare the result.
One platform, the full MMM workflow
From data preparation to model validation, budget scenarios and reports, inspect the evidence at each step.
From prepared data to an analysis you can inspect
Instant Mode simplifies configuration and analysis. Runtime depends on the engine, data volume and environment. Interpret results alongside data quality and validation evidence.
Data quality check + auto column recognition
SparkX automatically detects spend, channel, and outcome columns, runs data quality checks, and generates exploratory analysis — no manual field mapping needed.
- Auto field mapping & validation
- Spend / channel / outcome auto-recognition
- EDA: trends, correlations, distributions
- Missing data & outlier detection
Channel ROI & contribution attribution
AI selects and runs the optimal engine based on your data profile. You get channel-level ROI, contribution decomposition, and saturation/response curves — all transparent and traceable.
- Auto-selected engine (see smart logic below)
- Channel ROI & contribution decomposition
- Saturation & response curves
- Bootstrap confidence intervals
AI insights + downloadable report
Claude generates an executive summary, risk alerts, and an action checklist. Ask follow-up questions in natural language. Download a standalone HTML report to share with your team.
- AI executive summary (CN/EN bilingual)
- Risk alerts & action items
- Conversational follow-up Q&A
- Standalone HTML report download
// Smart Engine Selection Logic
Instant Mode analyzes your data profile and automatically picks the engine that fits best — instead of always defaulting to one.
One platform, every engine you need
Different scenarios call for different engines. SparkX MMM lets you choose — or let AI choose for you — without being locked into a single methodology.
Manual engine selection & fine-tuning
- 8 engines, configurable adstock & saturation
- Side-by-side model comparison
- Champion selection & re-run history
Run engines in parallel, auto-pick the best
Submit once, run Ridge + PyMC (or any combo) in parallel. Champion Mode selects the best model by R² / MAPE — and every downstream tool (optimizer, AI insights, scenarios) follows the Champion automatically.
- One submit, parallel multi-engine run
- Auto-picks champion by R² / MAPE
- Drift detection when new data diverges from champion
- Full run history, re-run from any snapshot
Optimize the next dollar, not the past
Based on Hill response curves, SparkX calculates each channel’s marginal incremental ROAS (miROAS) — the return on your next dollar. Drag sliders to see projected incremental revenue in real time. Optimize both directions: maximize incremental revenue for a given budget, or minimize spend for a revenue target.
miROAS slider + response curves
Drag budget sliders per channel and watch projected incremental revenue update instantly. See the miROAS of every channel — where the next dollar earns the most. Save scenarios, compare side by side, and export the winning allocation.
- Drag sliders, see revenue in real time
- Maximize revenue for given budget
- Minimize spend for revenue target
- Save & compare scenarios
Per-channel reallocation & projected lift
See exactly how much each channel gains or loses under the optimized plan, with before/after comparisons and projected lift.
Turn model outputs into a next step your team can discuss
AI helps summarize channel performance, risks and candidate actions. Review recommendations against model evidence, business constraints and experiments before changing spend.
From model output to business language
Claude distills the full model into a concise executive summary: what's working, what's oversaturated, where to cut, where to invest. Ask follow-up questions and get answers grounded in your data.
- Executive summary (CN/EN bilingual)
- Risk alerts: saturation, diminishing returns
- Conversational follow-up Q&A
A checklist you can hand to your team
Each insight comes with a prioritized action item: reallocate X from channel A to channel B, expected lift Y%. Copy, share, and execute.
One-click PPT & HTML export
Auto-generate an 8-slide PowerPoint with ROI charts, contribution breakdown, response curves, budget recommendations, and backtest results. Or export a standalone HTML to share with any stakeholder.
- 8-slide PowerPoint, auto-generated (CJK font-safe)
- Standalone HTML report, shareable offline
- Covers: ROI, contribution, response curves, optimization & backtest
Diagnostics your data scientists will trust
Full transparency for the technically rigorous: actual vs predicted time series, residual analysis, posterior distributions, and channel decomposition. Everything an auditor would ask for.
Actual vs predicted & residual analysis
See how well the model fits: actual vs predicted time series, residual diagnostics, and parameter transparency. Bootstrap confidence intervals show the uncertainty range.
- Actual vs predicted time series
- Residual diagnostics & parameter transparency
- Bootstrap confidence intervals
- Channel decomposition (time-series contribution)
Full posterior, not just point estimates
For Bayesian engines, inspect the full posterior distribution of every parameter. Understand uncertainty at the parameter level, not just the aggregate.
Instant Mode vs Expert Mode
Whether you're a marketing manager or a data scientist, SparkX MMM has a mode that fits your workflow.
Instant Mode
Expert Mode
How SparkX MMM compares
Faster, more transparent, more flexible — without the open-source complexity.
| Feature | SparkX MMM | Meta Robyn (OSS) | Google Meridian (OSS) | Stan (OSS) |
|---|---|---|---|---|
| Config time | 0 sec (Instant) | Days | Days | Weeks |
| Results in | < 2 min | Hours | Hours | Min-hours |
| Needs data scientist | No | Must | Must | Must |
| Process transparency | 9 steps, fully visible | Read the code | Read the code | Read the code |
| Multi-engine | 8 engines | 1 engine | 1 engine | 1 engine |
| Zero-config Instant Mode | Yes, < 2 min | No | No | No |
| Bidirectional optimization | Yes | No | Basic | No |
| What-if scenarios | Real-time sliders | Manual | Manual | No |
| Chinese support | Native bilingual | No | No | No |
| AI insights | Claude-powered | No | No | No |
| Web UI | Full UI | CLI | CLI | CLI |
All open-source tools are free to use but require programming skills and significant setup time. SparkX MMM provides the same methodology with zero configuration.
Start with your business question
Align spend and sales windows, check promotions, prices and stockouts, then compare budget scenarios.
Explore this scenario →Compare current and proposed allocation, historical support, model validation and changes requiring review.
Explore this scenario →Explain data sources, assumptions, diagnostics and next steps so clients understand the evidence.
Explore this scenario →Assess holdout validation, attribution suitability and experiments. Distinguish predictive explanations from incrementality evidence.
Explore this scenario →Explore, validate, then decide
The no-login demo explains the method. Real budget decisions also require your data, model validation and business judgment.
Prepare data
Align channel spend and outcomes, including promotions, prices and stockouts.
Inspect evidence
Review diagnostics, holdout validation and uncertainty to understand where conclusions apply.
Compare scenarios
Compare constrained budget allocations, export a report and plan follow-up validation.
Connect your data, whatever the source
Upload spend and sales data from any platform via CSV / Excel. Direct API connectors for ad platforms are on the roadmap.
Make methods, evidence and limitations visible
Use established MMM components alongside diagnostics and experimental evidence. Shared components alone do not establish a reliable causal conclusion.
// Engines
8 engines covering the full spectrum: regularized regression (Ridge), full Bayesian posteriors (PyMC/Stan/Meridian), time-varying ROI (SparkX TVP/Expert), evolutionary search (Nevergrad), tree-based (LightGBM). Meridian adds native geo-level pooling for geo-experiment calibration.
// Methodology
Built on the same peer-reviewed MMM methodology as Robyn and Meridian, using academically established components.
// Calibrate, Don’t Replace
Calibrate models with experimental evidence and retain experiment and refit provenance. Check the scope of the experiment and model separately.
// Data Security
The product uses organization-level access controls. Review data handling, AI services and deployment requirements in the security guide.
Start free
Explore the no-login demo, then request product access for your team. Features and limits depend on the provisioned plan.
Free
- Full Instant Mode experience
- 5 analyses per month
- AI insights (basic)
- HTML report download
Pro
- 50 analyses per month
- Expert Mode + all 8 engines
- AI insights (full 6 tabs)
- Budget optimization + scenario saving
- Priority support
Enterprise
- Multi-user collaboration
- API access
- Data connectors
- Custom model configuration
- Dedicated customer success
Questions, answered
What is MMM and why do I need it?
Do I need a data scientist?
How fast can I get results?
What data do I need to upload?
How is MMM different from attribution?
Is my data secure?
What is the difference between Instant Mode and Expert Mode?
Can I download and share reports?
Start with an interactive budget example
No account or real data needed. Understand the method before applying it to your business.
Explore the budget demo →