13 revenue analytics goals powered by ARIMA+ forecasting with FRED economic regressors, price elasticity modeling, scenario simulation, and recovery playbook generation.
Monthly Revenue
Elasticity
-1.24
Margin Cliff
3 SKUs
Scenario
Best
E-commerce teams make critical revenue decisions on fragmented data, static forecasts, and spreadsheets that ignore macroeconomic reality.
Revenue data lives across Shopify, Amazon, WooCommerce, and spreadsheets. Without a unified warehouse, teams operate on different numbers and miss the full picture.
Which products drive profit? Which categories have negative contribution margin? Without price elasticity and margin cliff detection, pricing decisions are blind guesses.
Static trend-line forecasts miss the impact of inflation, consumer confidence shifts, and seasonal weather patterns. Your revenue predictions are outdated before they ship.
Discount addiction, revenue concentration risk, declining AOV segments, and margin erosion go undetected until they compound into real damage.
TerraBog ingests data from 27 sources, enriches it with FRED economic indicators, and runs ARIMA+ forecasting and price elasticity models in BigQuery ML -- giving your team revenue intelligence that accounts for macroeconomic shifts.
13 revenue analytics goals, operational today
Sales forecasting, AOV tracking, geographic revenue, channel attribution, discount impact, profit margins, pricing optimization, return analysis, AOV segmentation, discount addiction, contribution margin, margin cliff detection, and revenue concentration risk.
ARIMA+ forecasting with FRED economic regressors
Revenue forecasts incorporate CPI, consumer confidence, unemployment rate, and retail sales data from the Federal Reserve. Not static trend lines -- forecasts that understand the economy.
Scenario simulation and recovery playbooks
Run best/worst/expected case scenarios. When forecasts miss, the system generates actionable recovery plans with pricing, retention, and channel reallocation strategies.
Data Sources
Revenue Intelligence
ARIMA+ revenue forecasting, price elasticity modeling, scenario simulation, and recovery playbook generation -- all operational and trained on your data.
From AOV segmentation to margin cliff detection, each goal is powered by dbt marts, BigQuery ML models, and FRED enrichment data.
Sales Forecasting
ARIMA+ with economic regressors
Average Order Value
AOV tracking and segmentation
Geographic Revenue
Revenue by region and country
Return Policy Analysis
Return rate impact on revenue
Revenue by Channel
Channel-level revenue attribution
Discount & Promotion Impact
Discount effect measurement
Profit & Margin Analytics
Gross and net margin tracking
Pricing Optimization
Price elasticity ML modeling
AOV Segmentation
Customer spend tier bucketing
Discount Addiction
Customers who only buy on sale
Contribution Margin
Per-product profit contribution
Margin Cliff Detection
Products with eroding margins
Revenue Concentration
Customer/product dependency risk
Revenue actuals vs. ML forecast, enriched with CPI, consumer confidence, and unemployment data from the Federal Reserve.
13 revenue goals, ARIMA+ forecasting with FRED regressors, price elasticity modeling, and scenario simulation -- production-ready from day one.