Dynamic

Pricing

Dynamic Core Pricing (DCP) leverages a cutting-edge and innovative neural network topology to analyze millions of input signals and recommend a base product pricing to optimize revenue

$200m

net lift in actualized revenue.

Dynamic Pricing leverages a cutting-edge type of machine learning.

See Use Cases

Best for businesses with strong competition, pricing complexity and margin opportunities.

Typical platform deployment prices up to 500,000 products, variants, and services.

Supports online and offline pricing; best when combined with Competitive Intelligence.

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10 to 200,000 product prices fully managed given the right circumstances

Optimization

Our proprietary model topology was architected to detect and optimize causal effects of pricing decision-making on revenue outcomes for the business.

Insight

Tens of thousands of product variants can be priced daily, bottoms up incorporating product, demand, market, and competitive signals into neural net topology.

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Implementation

scenario

01

Our end-to-end technology platform allows us to rapidly deploy the concept, capture high-granularity data and iteratively enhance the capabilities.

02

Elasticity learnings have been cross-utilized in pricing, revenue management and direct marketing areas for much wider impact within our client organizations.

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Analytics

Our analytics reveal the future responsiveness of each individual customer to marketing offers.

Adaptive Marketing optimizes offers to the individual rather than broad standardization

Adaptive
Marketing
Offer Richness

Based on predicted future customer sensitivity and engagement propensity impacted by offer.

Offer Mix

Optimized to produce maximal response based on each individual’s profile.

Offer Cadence

Determines and targets optimal re-engagement window for each customer.

Traditional
Marketing Analytics
Standard Offer

Based on historical outcomes.

Standard Offer

Hand-assembled based on organizations habits and standardized historical templates.

Standard Offer

Standard schedule that applies to all customers.

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Case studies

See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.

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10 to 200,000 product prices fully managed given the right circumstances

Optimization

Our proprietary model topology was architected to detect and optimize causal effects of pricing decision-making on revenue outcomes for the business.

Insight

Tens of thousands of product variants can be priced daily, bottoms up incorporating product, demand, market, and competitive signals into neural net topology.

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Results have been

exceptional.

10-20%

increases in Website Revenue
01

Our end-to-end technology platform allows us to rapidly deploy the concept, capture high-granularity data and iteratively enhance the capabilities.

02

Elasticity learnings have been cross-utilized in pricing, revenue management and direct marketing areas for much wider impact within our client organizations.

Dynamic Core Pricing is a fundamental shift in pricing capabilities from traditional methods

Conduct

millions

of pricing experiments

AP conducts millions of pricing experiments to assess discount impact & discover elasticity patterns.​

AP leverages elasticity patterns to maximize revenue capture for each individual customer

While pricing most directly impacts revenue, indirect drivers of revenue include:

Customer Attributes
Behavioral Patterns
Product Attributes
Temporal Trends

Generally, traditional pricing only considers product attributes and temporal trends to set a flat price for all customers.

AP is able to incorporate customer attributes and behavioral patterns to determine elasticity and recommend pricing for each customer

  • Offering individualized pricing optimizes conversion and maximizes revenue.

AP is a turnkey, real-time price personalization

engine that is

deployable within weeks

AP is a turnkey, real-time price personalization engine that is deployable within weeks

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Case studies

See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.

0/1
10 to 200,000 product prices fully managed given the right circumstances

Optimization

Our proprietary model topology was architected to detect and optimize causal effects of pricing decision-making on revenue outcomes for the business.

Insight

Tens of thousands of product variants can be priced daily, bottoms up incorporating product, demand, market, and competitive signals into neural net topology.

0/2
5 - 15% revenue uplift achieved across the board in most implementations

Intuition

The approach fundamentally avoids traditional challenges of pricing optimization under revenue management: explicit forecasts, inaccurate market elasticity assumptions, planning layers as input to improve correctness, response to abrupt changes, etc.

Flexibility

DCP can be used for any sales channel, online or offline, manual or automated.

DCP continuously assesses forward revenue impact of pricing & rapidly integrates feedback into decisions.

For each product and location combination, DCP determines the optimal daily price based on a causal analysis of N-day (eg 30 days) forward revenue for each price option.

Every day pricing outcomes are fed back to DCP to refine predictive modeling and improve price recommendations.

DCP executes under complete self-learning and self-reinforcing machine learning automation; thus no manual demand forecasts nor human adjustments are required

In this example, DCP has determined $90 price would sell quickly short-term but lower inventory would miss the demand surge in two weeks; thus, $110 is the recommended price.

Example historical analysis against a traditional pricing system

DCP naturally identifies deficiencies in revenue capture and opportunities for additional revenue capture on a daily basis.

Dynamic Core Pricing is a

fundamental shift

in

pricing capabilities from traditional methods

Dynamic Core Pricing is a fundamental shift in pricing capabilities from traditional methods

Dynamic Core Pricing
Analytical Basis

Complete self-learning and self-reinforcing machine learning automation leveraging real-time feedback of revenue outcomes

Manual Adjustment

Minimal manual intervention required, though client can set basic pricing parameters and rules

Updates

Continuous daily test and control experiments to adjust pricing strategy to changing customer, competitor, and macroeconomic conditions

Traditional Core Pricing
Standard Offer

Infrequently-updated demand forecasts are fed through an analytical tool with the pricing outputs being substantially adjusted manually

Standard Offer

High-cost, complex, and bureaucratic manual review of pricing leading to slow turnaround times and leaving revenue on the table

Standard Offer

Infrequent, project-based updates to pricing logic only after the previous strategy’s performance has diminished

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Case studies

See our highly tailored project approach that produces significant and sustainable competitive advantage for our clients.