Price Optimisation: From Exploration to Productionising

Key Notes

• Optimize for revenue / profit / market share
• Lose market share but retain profits
• Lose price but increase market share

• Standardize - Learn from business, Apply Algorithm
• Apply / Learn, Avoid fluctuations, inconsistencies
• Optimize - Know both win and lost scenarios
• For loss, how price can be pushed
• Explore - No more data to learn from
• AI solution to explore / Reinforcement learning problem
• Learn by itself (Rare occurrence)

• Working model - API
• Hosted model
• Web front end

• Building products
• Learned from historical data

• Business knowledge
• Existing Systems
• Sales Data
• Market Research

• Features - Pricing
• Perishability
• Premiumness
• Cost
• Frequency of Sale
• Seasonality
• Market features

• Feature Implementation
• Clustering, Encoding, Algorithms
• Algo choices
• Segmentation
• Segment dataset
• Data Driven Unsupervised method

Break things earlier if it saves us

Segmentation - Unsupervised vs Business Driven

• Business driven explainable
• Ownership

Category driven models - encoding

• One hot - Yes / No
• Factors - Assign numerical value, Ordering issues
• Binary - Introduce hierarchy, Reduce objects to describe objects

Base Algorithms

Depending on the time-series aspect

Code Optimization

POC mentality

Changes in the market - Time series and frequent retraining

Make model more time-dependent, time-series

Short term corrections

• LSTM to output median difference
• Different between regression model and error calculation
• Rolling average based correction

Production implementation

• Azure, Flask, Kubernetes

• Kubernetes deployed models
• Load balancers - Containers

Data feeds - Landing zone - Data Engineering - Consistency Check - Databricks layer 

• API End point - Model in Flask - Data Pipeline
• Model confidence

Optimization methods

• Optimizing without data
• Price elasticity

• Use bands
• Historical data
• Quantiles
• Lower and upper bands

Elasticity

Price elasticity vs Sales forecast

Historical data for every value with respect to price

Pseudo cells, pdf distribution

DYNAMIC PRICING IN E-COMMERCE

Keep Thinking!!!