Our GEN.4 AI will blow your neural network
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