A Machine Learning Approach to Optimize Prices During Clearance Sales at MANGO | MANGO

Key Notes

Data, decision making

Online + Offline business

Data Driven Optimization

Good base and start creating insights

Optimal discount for sales season

Well informed decisions for optimal price

Recent purchase

Predict sales for each week

Price vs Forecast for each week

 

Price vs forecast of sales

Matrix with price for week vs sales forecast

Profit for each particular case

Stock movement vs left over stock

 A/B Testing measure impact

Difference of difference method A/B testing

Lessons Learnt

Try to use tool that gives understandable results

Get job done

Able to explain assumptions

Connect with stakeholders

Approach problem from different perspectives

Keep Thinking!!!

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!!!