Adding RFM and CLTV templates

README.md

@@ -56,8 +56,8 @@ All prototypes and template are implemented in Python using a limited set of sta
 
 ## List of Prototypes and Templates
 The artifacts listed in this section can help to rapidly evaluate different solution approaches and build prototypes using your datasets. Artifacts are marked with the following qualifiers:
-  * 🧪 - artifacts that are particularly suitable for exploratory data analysis, evaluating the strength of causal effects in your data, and determining whether these data is feasible for solving a certain use case or not.
-  * 🚀 - conceptual prototypes that use advanced methods and not necessarily suitable for productization.
+  * 🧪 - artifacts that are particularly suitable for exploratory data analysis, evaluating the strength of causal effects in your data, and determining whether these data is feasible for solving a certain use case or not
+  * 🚀 - conceptual prototypes that use advanced methods and not necessarily suitable for productization
   * 📚 - notebooks that demonstrate basic algorithms and intended mainly for educational purposes
 
 #### Promotions, Offers, and Advertisements
@@ -66,18 +66,20 @@ These notebooks can be used to analyze the behavior of *individual* customers, c
 * Customer Scoring and Lifetime Value
    * Customer Propensity Scoring Using Deep Learning (LSTM with Attention) ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/propensity-scoring-lstm.ipynb))
    * Customer-level Uplift Modeling Based On Observational Data Using Causal Inference ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/uplift-modeling-observational.ipynb)) (🧪)
-   * Customer Lifetime Value (LTV) Modeling Using Markov Chains ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/markov-ltv.ipynb))
+   * Customer Lifetime Value (LTV) Estimation Using Markov Chains ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/ltv-markov.ipynb))
+   * Customer Lifetime Value (LTV) Estimation Using Bayesian Buy-Till-You-Die (BTYD) Model ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/ltv-btyd-bayesian.ipynb))
 * Decision Automation
    * Dynamic Content Personalization Using Contextual Bandits (LinUCB) ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/dynamic-content-personalization-rl.ipynb))
    * Next Best Action Model Using Reinforcement Learning (Fitted Q Iteration) ([notebook](https://github.com/ikatsov/tensor-house/blob/master/promotions/next-best-action-rl.ipynb))
 
 #### Marketing, Customer, and Content Analytics
-The notebooks can be used to analyze customer *population* or *segments*, get insights from user-generated content, and optimize marketing budgets.
+The notebooks can be used to perform *aggregated* analysis of the customer population or segments, get insights from user-generated content, and optimize marketing budgets.
 
 * Content Analytics
    * Sentiment Analysis Using Basic Transformers ([notebook](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/sentiment-analysis.ipynb)) 
    * Virtual Focus Groups Using LLMs ([notebook](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/virtual-focus-groups.ipynb)) 
 * Customer Behavior Analytics and Embeddings
+   * Recency, Frequency, and Monetary Value (RFM) Analysis of Customer Orders/Transactions ([notebook](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/rfm-analysis.ipynb)) (🧪)
    * Analysis of Customer Behavior Patterns Using LSTM/Transformers ([notebook](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/behavior-patterns-analytics-lstm.ipynb))
    * Item2Vec Using Word2vec ([notebook](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/item2vec.ipynb))
    * Customer2Vec Using Doc2vec (notebooks: [simulator](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/customer2vec-prototype.ipynb), [prototype](https://github.com/ikatsov/tensor-house/blob/master/marketing-analytics/customer2vec.ipynb))

_resources/datasets.md

@@ -7,6 +7,7 @@
 The dataset is anonymized and contains a sample of over 3 million grocery orders from more than 200,000 Instacart users. 
 * Data: https://www.instacart.com/datasets/grocery-shopping-2017 or https://www.kaggle.com/c/instacart-market-basket-analysis/data
 * Description: https://gist.github.com/jeremystan/c3b39d947d9b88b3ccff3147dbcf6c6b
+* Backup (data and description): https://drive.google.com/drive/folders/1JC-D3vtYI6iOSGaZ9DoSXDQ4GrvMzqLL
 
 #### Dunnhumby - Retail Transactions (The Complete Journey dataset)
 Household level transactions over two years from a group of 2,500 households who are frequent shoppers at a retailer
@@ -31,6 +32,13 @@ KKBOX offers subscription based music streaming service. The dataset includes us
 This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil.
 * Data: https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce
 * Description: https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce
+* Backup copy (data and description): https://drive.google.com/drive/folders/1vyoG0z7QpAcI4w47HD605-HA3lL45c92
+
+#### UCI Online Retail
+This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
+* Data: https://archive.ics.uci.edu/static/public/352/online+retail.zip
+* Description: https://archive.ics.uci.edu/dataset/352/online+retail
+* Backup copy (data and description): https://drive.google.com/drive/folders/1_u0CjaTPQAh5esUW6ZCyNmvSSs_esgVp
 
 
 ## 2. Product Recommendations