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Marketing Analytics: Markov Decision Process

What are the problems with traditional analytics? What is Markov Decision Process? How is it better?
by

Smriti Sakhamuri

on 25 November 2010

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Transcript of Marketing Analytics: Markov Decision Process

Traditional Analytics 1. Segmentation &
Segment Strategy Allocates customers to various segments based on demographics etc. Traditional analytics are product centric, and lack accuracy and efficiency 3-Step Methodology:

1. Segmentation and Strategy

2. Customer Dynamics via Predictive Models

3. Marketing Campaign & Execution 2. Customer Dynamics
via Predictive Modeling 3. Marketing Campaigns
& Execution Models customer behavior with respect
to propensity for product offerings Optimize Portfolio valuation by maximizing
revenue and allocating the budget efficiently Products may be profitable but not necessarily efficient Example:Offering a 20% discount on memberships when 10% is enough Problem: Problem: Problem: Markov Decision Process Mathematical technique used to model customer behavior and calculate lifetime value in marketing analytics Components of MDP:
1. Set of states (S1, S2 etc.)
2. Actions (special offer, club membership etc.)
3. Transitional probabilities
4. Expected value Solutions & Benefits 1. Segmentation &
Segment Strategy 2. Customer Dynamics
via Predictive Models 3. Marketing Campaigns
& Execution Adding variables such as loyalty, value
metrics allows for more detailed
and accurate segmentation Customer behavior can be
modeled with better finesse More of the customer base is reached Customer Centric:
Creates a sequence of actions
based on the customer Optimizes marketing budget allocation using portfolio diversification techniques This policy obtained will give
the fraction of customers to be
targeted with a specific action
to achieve portfolio optimization A MDP model is derived with a list of policies based on the various states a customer can be in and the resulting probabilities
Full transcript