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Dynamic Pricing Optimization

AI-powered pricing engine that optimizes for margin, volume, competitive position, and customer lifetime value with real-time market adaptation and guardrail enforcement.

Business Outcome

time reduction in quote generation (from 15-30 minutes to 7-15 minutes per quote).

Complexity:

Medium

Time to Value:

3-6 months

Why This Matters

What It Is

AI-powered pricing engine that optimizes for margin, volume, competitive position, and customer lifetime value with real-time market adaptation and guardrail enforcement.

Current State

(Traditional)

1. Rep manually looks up list price from price book or spreadsheet. 2. Rep applies standard discount (10-20%) based on volume. 3. Rep checks with manager for approval if discount >15%. 4. No consideration of competitive prices, Inventory Management levels, or customer value. 5. Static pricing remains until annual price book update.

Characteristics

• ERP (e.g., SAP, Oracle, Microsoft Dynamics)
• CPQ (e.g., Salesforce CPQ, PROS Smart CPQ)
• CRM (e.g., Salesforce, Microsoft CRM)
• Excel/Spreadsheets
• Email
• Pricing Analytics Tools (e.g., Revology, Competera)

Pain Points

⚠ Heavy reliance on manual processes increases error rates and slows down quoting.
⚠ Data silos lead to inconsistencies and difficulties in maintaining accurate pricing.
⚠ Limited real-time price adjustments due to system constraints.
⚠ Complex product configurations challenge automation and require manual intervention.

Future State

(Agentic)

AI analyzes: cost, target margin, customer segment, competitive pricing, Inventory Management levels, demand forecast.
Agent applies: volume discounts, contract pricing, promotional offers, loyalty bonuses.
Agent optimizes for: deal profitability vs win probability (margin-maximizing or volume-maximizing strategies).
Agent provides pricing guardrails: floor prices (below which requires approval), ceiling prices (market-competitive).
Agent tracks effectiveness: win rates by price level, margin vs volume trade-offs.

Characteristics

• Cost and margin data
• Customer value and segment data
• Competitive pricing intelligence
• Historical win/loss data by price point
• Real-time Inventory Management levels
• Demand forecasts

Benefits

✓ 2-5 percentage point margin improvement through optimization
✓ 95% pricing consistency across reps and deals
✓ Real-time competitive pricing reduces lost deals
✓ Win probability optimization increases conversion 10-20%
✓ Dynamic pricing adapts to inventory and demand in real-time

Is This Right for You?

39% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

• Applicable across multiple industries
• Higher complexity - requires more resources and planning
• Moderate expected business value
• Time to value: 3-6 months
• (Score based on general applicability - set preferences for personalized matching)

You might benefit from Dynamic Pricing Optimization if:

You're experiencing: Heavy reliance on manual processes increases error rates and slows down quoting.
You're experiencing: Data silos lead to inconsistencies and difficulties in maintaining accurate pricing.