Algorithmic Marketing

Some models from the book, as well as more advanced models, are available on github as a part of the TensorHouse project.

Chapter 3: Promotions and Advertisements

• Campaign/Channel Attribution using Adstock Model
• Customer Lifetime Value (LTV) Modeling using Markov Chain
• Next Best Action Model using Reinforcement Learning (Fitted Q Iteration)
• Multi-touch Multi-channel Attribution Model using Deep Learning (LSTM with Attention)

Chapter 4: Search

• Latent Semantic Analysis (LSA)

Chapter 5: Recommendations

• Nearest Neighbor User-based Collaborative Filtering
• Nearest Neighbor Item-based Collaborative Filtering
• Item2Vec Model using NLP Methods (word2vec)
• Customer2Vec Model using NLP Methods (doc2vec)

Chapter 6: Pricing and Assortment

• Markdown Price Optimization
• Dynamic Pricing using Thompson Sampling
• Dynamic Pricing with Limited Price Experimentation
• Price Optimization using Reinforcement Learning (DQN)

Extras: Supply Chain

• Multi-echelon Inventory Optimization using Reinforcement Learning (DDPG, TD3)