Understands your data like no dashboard can

Nylo's MMM uses Bayesian inference (PyMC) to learn how each marketing channel actually drives revenue. Not correlation. Causation, with uncertainty quantified.

What you get:

• ROI per channel with confidence intervals. Not a single number. A range you can trust. "Google Ads drives $3.20-$4.10 per dollar spent (95% credible interval)."
• Saturation curves. See exactly where each channel hits diminishing returns. Know when more budget stops producing more results.
• Budget optimizer. "Shift $15K from Meta prospecting to Google brand. Expected revenue impact: +8.2%." With statistical backing, not a gut call.
• Trained on your data. Not industry benchmarks. Your spend history, your revenue, your seasonal patterns. The model learns your business.

An e-commerce brand spending $120K/month across Meta, Google, and TikTok ran Nylo's MMM and discovered that TikTok prospecting had 40% higher ROI than Meta at current spend levels, but was hitting saturation at $25K/month. The budget reallocation recommendation saved $18K/month in wasted spend while maintaining the same revenue.

Understand which customers come back and which don't. Track retention, lifetime value, and profitability by acquisition cohort.

What you get:

• Retention heatmaps. See how each monthly cohort retains over time. Spot whether retention is improving or declining across cohorts.
• CAC-to-LTV profitability. Connect acquisition cost to lifetime value. Know which segments break even in 90 days and which never do.
• Segment comparison. Compare cohorts by first product purchased, acquisition channel, country, or UTM source. Find the segments that produce repeat buyers.
• Repurchase patterns. Average time to second order. Repurchase rate by segment. AOV trends over the customer lifecycle.

A Shopify brand compared cohorts by acquisition channel and discovered that customers acquired via email had 3x higher 365-day LTV than those from paid social, despite a lower first-order AOV. They shifted retention budget accordingly and increased overall customer profitability by 22%.

Predict next week's performance before it happens. Prophet and ARIMA models trained on your historical data.

What you get:

• Time-series forecasting with uncertainty bands. Not a single prediction line, but a range showing best-case and worst-case.
• Seasonal pattern detection. The model learns your weekly, monthly, and yearly patterns automatically.
• Gap detection. When actual performance deviates significantly from the forecast, Nylo flags it immediately. "Revenue is 15% below forecast this week. Here's what changed."

Four statistical methods work together to catch performance shifts early. Not simple thresholds. Pattern-aware detection trained on your data.

The four methods:

• Spike detection (standard deviation). Catches sudden jumps or drops. Configurable sensitivity.
• Trend detection (moving average). Smooths noise and finds when the underlying trend shifts.
• Recent change tracking (exponential smoothing). Weights recent data heavier. Catches emerging shifts before they become obvious.
• Pattern monitoring (seasonal decomposition). Learns your repeating weekly and monthly patterns. Alerts when the pattern breaks.

The combination matters. A single method produces noise. Four methods together, calibrated to your data, produce signal.

Statistical significance testing built in. No more "I think this variant won."

• Chi-square testing for comparing campaign variants. Configurable confidence levels.
• Difference-in-differences for measuring the impact of changes over time. Requires a control group and a treatment group. Shows causal impact, not just correlation.

Run MMM and cohort analysis across all your clients from one platform. Show each client their channel ROI with confidence intervals. Present budget recommendations backed by Bayesian statistics, not a slide deck with gut feelings.

Cohort analysis reveals which acquisition channels bring customers who actually come back. MMM shows where your next dollar should go. Forecasting catches revenue gaps before the month ends. Present ROI to your CFO with credible intervals, not estimates.