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Use cases of Financial Data Science Techniques in retail

Submitted by Sudipto Pal (@sudipto-pal) on Monday, 15 April 2019


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Section: Crisp talk Technical level: Intermediate Session type: Lecture

Abstract

Financial domains like Insurance and Banking have uncertainty itself as an inherent product feature, and hence makes extensive use of Statistical models to develop, valuate and price their products. This presentation will showcase some of the techniques like Survival models and cashflow prediction models, popularly used in financial products, how can they be used in Retail data science, by showcasing analogies and similarities.

Survival models were traditionally used for modeling mortality, then got extended to be used for modeling queues, waiting time and attrition. We showcase, 1) How the waiting time aspect can be used to model repeat purchase behaviors of customers, and utilize the same for product recommendation on particular time intervals. 2) How the same survival or waiting time problem can be solved using discrete time binary response survival models (as opposed to traditional proportional hazard and AFT models for survival). 3) Quick coverage of other use cases like attrition, CLTV (customer lifetime value) and inventory management.

We show a use case where survival models can be used to predict the timing of events (e.g. attrition/renewal, purchase, purchase order for procurement), and use that to predict the timing of cashflows associated with events (e.g. subscription fee received from renewals, procurement cost etc.), which are typically used for capital allocation.

We also show how the backdated predicted cashflows can be used as baseline to make causal inference about strategic intervention (e.g. campaign launch for containing attritions) by comparing with actual cashflows post-intervention. This can be used to retrospectively evaluate the impact of strategic interventions.

Outline

  • Importance of Survival Regression techniques in modeling events and their timings in finance (3 mins)

  • Discuss difference between event models and waiting time models (2 mins)

  • Showcase traditional survival models and discrete time survival models solved through ensemble learning and ANN. Compare and show advantages, disadvantages (3 mins)

  • Retail Use cases:

  1. Product Recommendation using waiting time models: repeat purchase behavior as a function of waiting times between purchases and other factors (detailed showcase) (5 mins)

  2. Customer Attrition models (quick overview) (1 min)

  3. Inventory management using waiting time models and queuing theory (quick overview) (1 min)

  • Cashflow models for subscription based retail businesses, and how it can be solved using survival models (3 mins)

  • Causality analysis of Promotions/Campaigns using cashflow models

Speaker bio

Sudipto has worked as predictive modeling expert in Actuarial Science domain in Insurance Industry (with Swiss Re, AIG) for 8+ years, and in Retail and Marketing Analytics domain (with Walmart Labs) for 3 years. He has built and productized various models hands on for Insurance business which have been used for Pricing, Reserving, Risk Management, Campaigning etc. Similarly he has built ML products for retail which has been used for Marketing, Customer Analytics, Cashflow Modeling, and Budget Allocation. Sudipto did his B Stat and M Stat degree from Indian Statistical Institute, Kolkata. Sudipto has used his expertise in Statistics, Economics, Finance and ML to ensure successful adoption of Statistics and ML in Insurance and Retail products. Sudipto has led a predictive modeling team as a Manager and Director for 3 years in AIG. He is a currently a Staff Data Scientist with Walmart Labs.

Links

Slides

https://www.slideshare.net/secret/wjNXhuko0SdFSC

Preview video

https://www.youtube.com/embed/RTrba-bZ7Hg

Comments

  • Zainab Bawa (@zainabbawa) Reviewer 2 months ago

    Thanks for sharing this Sudipto. Two questions that came up in the review:

    1. Who should attend this talk? What background knowledge should your audience have in order to attend this talk?
    2. What is it that you want participants to takeaway from this session?
    • Sudipto Pal (@sudipto-pal) Proposer 2 months ago

      Hi Zainab

      Please find my response below.

      1. Anyone who is interested in understanding how data science techniques of one domain (here Finance) is used in other domains (mostly Retail), should attend this talk. They need some very basic prior knowledge of machine learning (knowing regression and ensemble learning is decent enough).
      2. At the end of the talk an attendee will be able to understand commonalities between survival models catering to various purpose (attrition, mortality, inventory management, product recommendation) and can apply them on their respective work areas. Similarly they will learn some examples of cashflow model popular in finance, but application in retail. A heavy emphasize will be on commonality and cross-applicability so that the attendee is able to use same techniques in unusual areas, where these techniques are not traditionally applied.

      Please feel free to revert back for further queries.

      Thanks & Regards
      Sudipto

  • Sudipto Pal (@sudipto-pal) Proposer 2 months ago

    I have added the slides.

    Thanks
    Sudipto.

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