Our GEN.4 AI will blow your neural network

PROS created the Profit Optimization Software category, and we remain the undisputed leader in the space. With the launch of the PROS Platform, we now have a flexible and modular platform which future proofs customers who want to build organic margin by optimizing costs and revenue in existing operations.

PROS TODAY

GEN. 4

2022

Neural Network

Leveraging the last AI advances in ML and neural networks to improve modeling techniques and eliminate history.​

HOW IT WORKS

Segmentation: Neural network eliminated and replaced segmentation approach with request- and customer-specific price recommendations that use all available attributes, not a subset as in most segmentation models.

Price Optimization: Provides customer-specific, market-aware, and win-rate based pricing.

ADVANTAGES

  • Peer group is determined dynamically for every transaction
  • Improved prediction accuracy of the model
  • Ability to use a large quantity and variety of features
  • Generate optimal price recommendations using loss information
  • Automatic Seasonality and Trend
  • Additional co-variates (market indices, competitor data)
  • Sparse data handling — many features, categorical features with many values
  • Profit/Revenue Optimization to determine target price
  • Uses explainable AI model to provide transparency
  • Flexibility to utilize loss information or indirectly model win rate
  • Can be easily extended to Non-Negotiation Guidance

GEN. 3

2018

Dynamic Pricing

Major move to user-driven workflows so that the science moves out of the backroom and into the application itself.

HOW IT WORKS

Segmentation: Enhanced to SKU-centric symmetric tree that utilizes dynamic attribute roll-up.

Price Optimization: Customer-level analysis with benchmarking and guidance against peers with price change aggressiveness levers changes controlled by the user.

ADVANTAGES

  • Product centric
  • Embedded gradual correction of underperformers
  • Easy results validation by the user within the UI
  • Simulation capabilities that include a benefit estimate
  • Enable users to create and manage all aspects of segmentation and pricing guidance

DISADVANTAGES

  • History can be misleading during volatile market conditions
  • Indirect use of win/loss data
  • Segmentation model is limited to pre-determined attributes

GEN. 2

2014

Branching Tree

AI advances continue as we move to the use of data-science driven segmentation to reduce the Cartesian data sparsity problem.

HOW IT WORKS

Segmentation: Supervised machine learning asymetric binary tree model (based on CART) with advanced model fit statistic Bayesian Information Criteria.

Price Optimization: No change.

ADVANTAGES

  • Dynamic attribute use
  • Improved predictability
  • Improved data sparsity handling

DISADVANTAGES

  • Segmentation was difficult or infeasible to visualize, which hurt adoption
  • Business rules to ensure target pricing achievable
  • Attribute selection and segmentation were determined in offline manner

GEN. 1

2009

Cartesian Model

AI begins in its basic form. Customer-specific willingness-to-pay price guidance to provide the right price to the customer.

HOW IT WORKS

Segmentation: Symmetric cartesian cross-product algorithm.

Price Optimization: Percentile-based algorithm and broad peer groups.

ADVANTAGES

  • Consistent peer groups
  • Placeholder segments
  • Easy to visualize and explain

DISADVANTAGES

  • Static attribute groupings
  • Rigid data structure leading to sparse data conditions
  • Business rules to ensure target pricing is achievable
  • Attribute selection and segmentation determined in an offline manner

GEN. 0

2005

Digitization

Foundational price management technology. Replace Excel and integrate into back-office systems.

ADVANTAGES

  • More efficient and automated
  • One central platform for all pricing and cost information
  • Price effectiveness transparency and visibility
  • Granularity — able to drive into more detail
  • Exception-based review relying on alerts and thresholds

DISADVANTAGES

  • Business-rules driven
  • Human decision framework
  • Relies on broad customer classifications/segments
  • Matrix-based discounting structures
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