Sales Forecasting Software for Predictable Revenue and Demand
We design, build, and operate sales forecasting software that turns your historical sales, live pipeline, and external market signals into accurate, explainable forecasts across SKUs, regions, channels, and customers. From first pilot to production, we integrate with your ERP, CRM, and data platform so finance, commercial, and supply-chain leaders work from one forecast they trust.
Cut forecast error, align S&OP, and free your analysts from spreadsheet firefighting.
- Machine-learning sales forecasting models: gradient boosting, hierarchical time series, deep learning
- Native integration with SAP, Oracle, Salesforce, Dynamics, NetSuite, Snowflake, and Databricks
- Multi-level forecasting: SKU x store x week, customer x product, channel x region
- Explainability, scenario simulation, and automated reconciliation with finance targets
- Cloud-native, MLOps-backed deployment with monitoring and drift detection
Why are your sales forecasts always wrong when you need them most?
Most commercial and supply-chain teams run sales forecasting in spreadsheets updated by hand, fed by stale data, and adjusted by gut feel. The result: double-digit forecast error, excess stock, missed revenue, painful S&OP cycles, and finance numbers that never match what the field commits to.
Architecture built for accuracy, scale, and trust
Unified feature store on Snowflake, Databricks, or BigQuery. ERP, CRM, POS, syndicated, weather, and macro signals harmonized at SKU and customer grain.
Champion/challenger framework with LightGBM, XGBoost, Prophet, N-BEATS, and TFT, plus hierarchical reconciliation (MinT, bottom-up, top-down).
Low-latency APIs and batch forecasts for ERP, TMS, planning, and BI consumption.
Automated retraining, drift detection, backtesting, and accuracy tracking (MAPE, WAPE, bias) per segment.
Lineage, versioning, audit logs, and role-based access across finance, sales, and supply.
SHAP-based driver analysis surfaced directly in Power BI, Tableau, or embedded apps.
How we deploy sales forecasting software in your business
We audit current forecast accuracy (WAPE, bias), data availability, and business cycles. Output: baseline accuracy report, prioritized use case, and target KPIs. (1-2 weeks)
We connect ERP, CRM, POS, and external sources, build the feature store, and reconcile hierarchies. Output: clean, versioned dataset ready for modeling. (2-3 weeks)
We train, benchmark, and reconcile candidate sales forecasting models. Output: production-candidate model with backtested accuracy per segment and explainability reports. (3-4 weeks)
We deploy forecasts into your ERP, planning, and BI tools with MLOps, monitoring, and user workflows. Output: live forecasts consumed by S&OP and FP&A. (2-3 weeks)
We monitor drift, retrain on fresh data, and expand to new SKUs, markets, and business lines. Output: sustained accuracy improvement and expanded coverage. (ongoing)
Measurable business impact
20-40% reduction in forecast error (WAPE) vs. spreadsheet and legacy statistical baselines
15-30% reduction in inventory and working capital tied to demand forecasting errors
5-15% revenue uplift from better promo, price, and allocation decisions
50-80% less time spent on manual forecast preparation and reconciliation
3-6 month payback on most retail and CPG deployments
Who gets the most value from our sales forecasting software
From spreadsheet guesswork to a production-grade sales forecasting platform
We build sales forecasting that fits how your business actually plans, from SKU-level retail demand to B2B pipeline scoring, all reconciled into one number finance, sales, and supply chain share.
Frequently Asked Questions
Typically 20-40% more accurate. In most engagements, WAPE drops from the 30-40% range (spreadsheets and naive statistical methods) to 15-20% at SKU or customer grain, because ML models capture promotions, seasonality, price elasticity, and external signals your Excel cannot.
At minimum, 24 months of transactional sales history at the grain you want to forecast (SKU, customer, store, channel), plus a product and customer master. CRM pipeline, price and promo history, and external signals (POS, weather, macro) improve accuracy further but are not required to start.
Yes. Our platform supports hierarchical forecasting across SKU x store x week for retail and SKU x customer x channel for CPG demand forecasting. The same engine reconciles bottom-up operational forecasts with top-down financial plans.
No. We integrate with your existing SAP, Oracle, Dynamics, NetSuite, Salesforce, Kinaxis, o9, or Anaplan stack. Our software publishes forecasts into those systems via APIs or batch files, so your planners keep their familiar workflows.
Most clients have a production forecast live in 8-12 weeks and measurable accuracy gains within one business cycle. Full ROI (inventory release, reduced stockouts, better promo decisions) typically appears within 3-6 months.
We use analog-based and attribute-based models that predict demand for new SKUs by mapping them to similar products, combined with early-signal learning that retrains the model as the first weeks of real data arrive. This matters in retail and CPG, where 15-30% of SKUs can be new each year.
Every forecast ships with driver-level explanations (SHAP values, feature contributions) showing why the model predicted a given number: price, promotion, seasonality, macro, or trend. Planners and finance can challenge, override, and audit forecasts, and all overrides are tracked.
Get a forecast your CFO, sales, and supply chain all trust
Book a 30-minute, no-obligation sales forecasting assessment. We will review your current accuracy, identify the fastest path to impact, and share a data-backed roadmap, whether or not you choose to work with us.
Discovery call
We review your current forecast accuracy and the data you already have.
Forecast diagnostic
We identify the fastest path to impact and the highest-value use case to start with.
Roadmap
You get a data-backed roadmap to production, whether or not you work with us.