We deliver across pricing assessments, opportunity sizing, pricing tools, enterprise-grade dashboarding, and advanced ML-driven pricing analytics.
Our experience spans clients from $20M to $100B+ revenue across the US, Europe, Australia, and global markets.
We provide embedded support across the full engagement lifecycle, from stakeholder alignment and model development to executive-ready outputs and post-project continuity.
The projects are led by pricing analytics specialists with experience across leading consulting environments.
CommercialIQ delivers structured analytical support across the full lifecycle of pricing engagements, from opportunity identification and segmentation to modelling, optimisation, and pricing structure design.
We assess pricing structures, quantify leakage, and identify revenue opportunities through data analysis, benchmarking, commercial diagnostics, and price waterfall analysis, surfacing 1,000+ quick wins and strategic levers aligned to client priorities and constraints.
We design value-based pricing frameworks across products, features, and customer segments, supporting end-to-end execution from feature identification and conjoint/WTP analysis to pilot testing, implementation, and business-aligned recommendations.
We model demand sensitivity to price changes across use cases, from high-level elasticity estimation to advanced, deployable ML-based models, leveraging transaction data, surveys, or secondary datasets based on the objective and data environment.
We analyze discount structures by creating like-for-like cohorts, identifying unjustified variability, and quantifying opportunity, followed by development of discount request tools, tracking KPIs, and audit dashboards to enable governance and ongoing performance monitoring.
We set up hard and soft price floors using analytical methods such as percentiling based on margins and discounts, along with hierarchical roll-ups to establish floors for new SKUs or SKUs with limited sales.
We segment customers across commercial, behavioral, and value-based dimensions using advanced ML on survey and usage data, as well as qualitative segmentation based on customer interviews.
We analyze historical sales data using ML algorithms (association rule learning and clustering) to identify cross-sell and upsell opportunities, uncovering product combinations frequently sold together to inform sales and bundling strategies.
We evaluate competitor pricing structures, positioning, and price corridors across segments to contextualize internal pricing decisions and identify gaps or opportunities.
We build margin scenario models, price waterfall analyses, and discount governance structures to quantify revenue leakage and evaluate optimization scenarios.
In addition to core pricing capabilities, CommercialIQ supports adjacent analytical workstreams that strengthen pricing models and broader commercial initiatives.
We also incorporate secondary data sources and external market intelligence to validate assumptions and enhance analytical robustness.
These inputs are aligned with pricing frameworks to ensure consistency across models, recommendations, and client deliverables.
Pricing Strategy
Development of price value architecture, segmentation frameworks, and structured pricing models across products and markets.
Price Setting
Model-driven support for establishing and revising pricing across segments, products, and geographies.
Price Getting
Execution-focused support including discount governance, deal guidance, and tools to improve price realization at the transaction level.
Margin Optimization
Identification of margin improvement opportunities through leakage analysis, price waterfalls, and scenario modelling.
Pricing Analytics Enablement
Development of analytical systems, dashboards, and models to enable scalable, data-driven pricing decisions.
Reach out to CommercialIQ to support your next GTM engagement with structured analytical delivery aligned to your scope, timelines, and client requirements.
We developed elasticity-based discount guidance across SKU bands, audited cross-channel incentives and promotions, and identified leakage opportunities tied to price-insensitive SKUs.
We developed a laddered price floor framework using historical invoice data and built 800+ machine learning models to assess elasticity across 1,500+ SKUs representing approximately 96% of revenue.
