Data-intensive pricing intelligence. Multi-sourced data integration, structured diagnostic analysis, metric performance gap comparisons.
Price & margin uplift identification. Algorithm-based methodology to identify misaligned product/customer net pricing and margin, accounting for pocket price and cost-to-serve position.
Rules-based scenario price modeling. Modeled price opportunity, risk profile, and size of prize based on price rule strategies. Sensitivity examples include price elasticity, service drivers, and value attributes.
See how this works in action.
Read our case study on an automotive distributor.
Are you leaving profit on the table?
Find out where your pricing strategy may be falling short.
12 PRICING POINTERS
Pump & Machinery Manufacturer. Complete reassessment of pricing, building a pricing grid tied to perceived feature value
=> 2% initial margin improvement.
Electronics Distribution Business. Combining competitive data and ERP transaction data. Identified where items were over/under-priced, comparing stock share depletion rates and underlying growth
=> 30% ROI.
Automotive Component Distribution. Generated view of price/margin gaps and price sensitivities. Detailed, tailored and specific price adjustments generating
=> +20% margin improvement.
Retail. Price optimization via store price cluster grid optimization using drivers such as POS, demographics, customer perception, relative competitive reference price gap
=> +10% profit improvement.
See how this works in action.
Read our case study on an industrial manufacturer.
See how this works in action.
Read our case study with a leading retailer.
Precision pricing requires a technology, business and data intensive capability, moving beyond summary recommendations to detailed SKU specifics that can be implemented with confidence.
Data enrichment. Relevant data inputs can and should be considered, ranging from ERP, competitive, CRM, web analytics, etc., to build a working pricing model and associated dynamics.
Diagnostic. Benchmark and evaluate net price/margin anomalies and outliers, but also observable pricing behavior. Identify and map full pocket price and cost to serve.
Price Model. Develop price uplift scenarios, sensitivity modeling with rules-based algorithms to define the size of price strategy impact and likely risk profiles.
Execution. Agree implementation options, execution, price monitoring, and results-based adjustments.
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