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.
HOW IT WORKS
Price Prediction: Neural network eliminated and replaced segmentation approach that use all available attributes, not a subset as in segmentation models.
Price Optimization: Provides customer-specific, market-aware, and win-rate based pricing.
ADVANTAGES
- Peers are predicted dynamically based on transaction attributes (which includes customer and product features)
- Improved prediction accuracy of the model
- Ability to use categorical features with huge number of values
- Ability to generate optimal price recommendations using loss information
- Automatic Seasonality and Trend
- Ability to use unconventional 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
- Extensible to Non-Negotiation Guidance
GEN*3
2018
Dynamic Segmentation-Based 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 that is done online.
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 (for ROI estimation)
- Enable users to create and manage all aspects of segmentation and pricing guidance
- Further improvement on sparsity handling with rolled up ability to re-segment if needed
- More meaningful floor and expert price to aid negotiation
DISADVANTAGES
- Limited number of features and feature values
- Limited ability to use market indices that informs potential changes in historical data. History can be misleading during volatile market conditions
- Inability to use loss data (indirect use of win/loss data)
- Segmentation model is limited to pre-selected fixed set of attributes
COMPETITION TODAY
GEN*2
2014
Decision 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 uses Bayesian Information Criteria to prune the decision tree.
Price Optimization: No change.
ADVANTAGES
- Wights recent transactions more heavily
- Dynamic attribute use
- Flexibility of the tree generation process improves predictability
- Improved data sparsity handling (not enough transactions and not enough customer diversity)
DISADVANTAGES
- Segmentation was difficult or infeasible to visualize, which can 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. (Bucket feature values and then create cross-product from the bucket) uses Bayesian Information Criteria.
Price Optimization: Percentile of historical price paid within the segment.
ADVANTAGES
- Consistent peer groups
- Placeholder segments
- Easy to visualize and explain
- Customer specific pricing takes into account low and high performers
DISADVANTAGES
- Static attribute groupings
- Rigid data structure leading to sparse data conditions
- Require business rules to ensure target pricing is achievable (otherwise could result in large price changes)
- Attribute selection and segmentation determined in an offline manner
- No explanation for price recommendations in UI
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 transparency and visibility
- Granularity — able to dive into more detail (price at lower-level components and rolled up to the whole producct)
- Exception-based review relying on alerts and thresholds
- Streamlined workflow
- Ability to see the impact of price change (added 2019)
- Ability to use business rules based on deader-follower relationships on product portfolio
- Flexibility to build complex business rules
DISADVANTAGES
- Business-rules becomes too numerous and hard to maintain and kept updated
- Rely on human decision
- Relies on broad (business/marketing) customer classifications/segments
- Matrix-based discounting structures