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Optimize pricing, promotions, mix, and trade terms with an integrated planning engine

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AI-powered revenue optimization for modern CPG Leaders

Pricing, promotions, pack architecture, and trade investments are often managed in silos, which slows decision-making and obscures true incrementality, cannibalisation, and trade spend performance. Sigmoid iNRM unifies all revenue growth levers into a unified, analytics-driven view of pricing, promotions, mix, and trade terms; enriches it with predictive modelling; and enables cross-lever simulation while optimizing actions. With an agentic AI layer, commercial teams get always-on monitoring, instant diagnostics, and targeted recommendations embedded directly into decision workflows.

Sigmoid iNRM features

Integrated cross-lever framework

Integrated cross-lever framework

Connects pricing, promotions, PPA/assortment, and trade terms under a single analytical logic to eliminate silos and support more effective CPG pricing strategies.

AI-powered end-to-end planning and simulation

AI-powered end-to-end planning and simulation

Brings together CPG-specific models, knowledge code-base, and planning tools to power scenario planning and insights across brands, channels, and retailers.

RGM-ready data foundation

RGM-ready data foundation

Creates a harmonized, NRM-specific semantic layer by integrating POS, finance, promo, trade terms, panel, and digital commerce data with AI-powered matching and standardization.

Agentic AI command centre

Agentic AI command centre

Provides always-on monitoring, automated root-cause diagnostics, and recommended actions, embedded in daily workflows through alerts, dashboards, and conversational interfaces.

Persona-based decision work

Persona-based decision work

Delivers tailored views and actions for RGM, sales, category, finance, and leadership teams, with guardrails and approval flows that align with your business decisions.

Rapid time-to-value deployment

Rapid time-to-value deployment

Accelerate rollout with pre-configured analytics, reusable templates, and guided workflows that reduce implementation time and boost adoption across teams.

Sigmoid iNRM architecture enabling holistic revenue growth management

Sigmoid iNRM is powered by PRISM, a modular, cloud-native and AI-powered RGM suite that unifies diagnostics, simulation, optimization, and actioning into a single, integrated workflow. These four modules help commercial and RGM teams plan, test, and execute revenue strategies with speed, accuracy, and scale.

Model Outputs Dashboard | Diagnostic Layer

Granular model outputs across price, promo, assortment, and trade terms form the analytical foundation of revenue growth management. Teams can benchmark performance, understand demand drivers, and identify where value is being created or lost.

Cross-lever Simulation | Predictive Layer

Run integrated “what-if” scenarios across price changes, promo mechanics, pack shifts, and portfolio actions. The simulation engine quantifies upside, risk, and cross-lever dependencies that enable informed planning before execution.

Cross-lever Optimization | Prescriptive Layer

AI and advanced algorithms optimize revenue, margin, volume, and retailer KPIs within business guardrails. This engine identifies the most profitable combination of pricing, promo, mix, and trade strategies.

Agentic-AI Insights Edge | Intelligence Layer

Always-on monitoring and automated diagnostics deliver real-time alerts, root-cause insights, and recommended next actions, embedded into role-based workflows. This transforms NRM into a proactive, continuous capability.

Sigmoid iNRM pillars that orchestrate integrated planning and execution

Sigmoid iNRM enables commercial, RGM, category, and revenue teams to solve high-impact challenges across the full net revenue management spectrum.

Sigmoid iNRM pillars

Strategic Use-Cases that drive Revenue and Margin Growth

Sigmoid iNRM enables commercial, RGM, category, and revenue teams to solve high-impact challenges across the full net revenue management spectrum.

Pricing Analytics

Strengthen pricing decisions using advanced elasticity modelling, threshold price detection, competitive impact assessment, and portfolio cannibalization analysis. This provides the analytical rigor needed to shape modern CPG pricing strategies across markets, packs, and channels.

Promotion and Trade Optimization

Improve efficiency of promotional investments through baseline decomposition, uplift attribution, ROI evaluation, and long-term impact measurement. iNRM’s predictive and prescriptive capabilities support end-to-end trade promotion optimization and calendar planning with confidence.

Pack Price Architecture (PPA) & Assortment Optimization

Optimize portfolio mix with attribute importance insights, incrementality vs. transferability studies, switching behaviour matrices, and walk-rate analysis. These capabilities help teams design channel-specific PPA frameworks that maximize revenue and reduce duplication.

Trade Fund Management and Efficiency

Strengthen customer-level financial decisions with enhanced P&L visibility, fair-share evaluation, and trade spend benchmarking. iNRM helps identify overspend, under-investment, and true drivers of customer profitability that maximize ROI across trade promotion management and optimization programs.

Customer success story

Other accelerators

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Sigmoid Reconica

Automate data harmonization by extracting attributes, standardizing inputs, and resolving bundles across systems. Reconica creates accurate master datasets that scale across markets and formats, ensuring consistent, analytics-ready data.

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Sigmoid AnalyticsBot

Use natural-language queries to explore complex datasets and get actionable business insights in real time. AnalyticsBot delivers contextual, role-aware analytics through interactive visualisations that help enterprises extract value from data quickly.

FAQs

Sigmoid iNRM is an AI-powered integrated net revenue management platform for mid-to-large CPG and retail organizations with complex multi-SKU portfolios and significant trade investments. It is designed for RGM, sales, category, and finance teams looking to align commercial decisions across pricing, promotions, mix, and trade terms.

Traditional tools focus on single levers in isolation. Sigmoid iNRM applies a cross-lever framework that models interdependencies across price, promo, pack, and trade terms, then uses simulations and optimizations to recommend holistic strategies that maximize growth for both the manufacturer and retailer. The holistic AI-powered revenue management ensures more accurate planning and stronger revenue and margin outcomes.

Yes. iNRM’s data foundation integrates POS, ERP, TPM, finance, panel, and digital commerce data, while APIs and adapters connect to existing planning and reporting tools. Sigmoid’s iNRM uses AI-driven data harmonization to accelerate onboarding. This supports scalable revenue growth management in CPG environments without disrupting existing workflows.

Absolutely. Sigmoid iNRM includes predictive modelling and optimization capabilities for trade promotion optimization, mechanic planning, and long-term ROI improvements, helping CPG or retail brands maximize efficiency of promotion investments.

A typical deployment begins with foundational analytics in 10–12 weeks and scales to full cross-lever optimization within 6–12 months, depending on markets and categories. Pre-built accelerators help organizations achieve faster results in net revenue management CPG programs.