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Course Details

CRM Implementation: Predictive Analytics

Build, profile, evaluate and deploy predictive models to your CRM data in order to improve marketing efficiency and predict the likelihood of future outcomes

$99 non-members $149 Take This Course

Course Description

Predictive modeling is an analytic technique that is a critical component of a marketer’s toolkit. In this module, you will learn the methods to build, profile, evaluate and deploy predictive models to your CRM data in order to improve marketing efficiency and predict the likelihood of future outcomes.

This course consists of three sections that will help you understand the core principles of Predictive Modeling usage, development, and applications: first, we will provide an overview of Predictive Modeling, common marketing goals and data requirements for developing effective Predictive Models, and important considerations in setting analysis specifications. Second, we will review the steps needed to build, profile, evaluate, and deploy a Predictive Model. And lastly, we will discuss real-life examples and applications of Predictive Modeling as well as ways in which you could use this technique to improve marketing efficiency.

This is the final module in a six-part series entitled: “CRM Implementation: Merging Data & Insights”. This module can be taken either as a stand-alone course or as part of the series.

Topics

  • Common marketing goals that require predictive analytics
  • Data requirements for predictive analytics
  • How to build, evaluate and deploy a predictive model
  • Applying predictive modeling to improve your customer acquisition and retention, offers, and messaging

Instructors

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Devyani Sadh
CEO Data Square Read Biography »

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Section 1: Overview, Goals & Data

  • Definitions
  • Why predictive modeling?
  • Predictive modeling components
    • Business objectives
    • Analysis sample
      • Analysis specification
        • Analysis level
        • Analysis specification
          • Dependent variables vs independent variables
          • Continuous vs. categorical
          • B2B vs B2C
        • Analysis universe
        • Scoring frequency
      • Data acquisition
      • Data integration
      • Data sampling
  • Section exercise

Section 2: Process

  • Model build
    • Decision trees
    • Regression / multiple regression
    • Neural network
  • Model evaluation
    • Statistical measures
    • Predictor contribution
    • Gains chart
    • ROC Curve
  • Model scoring
  • Model profile / distribution profile
    • No comparison
    • With comparison
  • Model deployment
  • Section exercise

Model scoring

Section 3: Applications

  • Predictive analytics tiers
    • Tier 1 – List selection
      • Lifecycle
      • Acquisition phase
      • Suspects
      • Prospects
      • Leads
      • Retention / reactivation phase
      • New customers
      • Repeaters
      • Core customers
      • Active customers
      • Inactives
    • Tier 2 – Offer / Pricing / Messaging
    • Tier 3 – Triggers / Timing
    • Tier 4 – Contact / Optimization
  • Predictive analytics applications
  • Section exercise

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