Tips & Tricks for Segmentation (Targeting, Profiling, Classification)

Segmentation (Targeting, Profiling, Classification) is the process of dividing a database into distinct groups of individuals who share common characteristics.  This is readily accomplished using modern data mining and machine learning techniques. The methods are easily implemented and work well with large datasets containing nonlinearities, interactions in the data and a mix of categorical and numerical variables.

In this webinar, you will learn, via step-by-step instruction, how to use modern techniques to:

  1. Segment a large database AND
  2. Look at an already segmented/clustered database and discover the reasons for the class memberships.

We will demonstrate via an APPLIED CUSTOMER SEGMENTATION example (Banking/Risk) and two additional case studies (Insurance/Renewals and HealthClub Membership/Insights). 

Although we show three examples, the approach is widely applicable and you will be able to follow the same steps on your own datasets. Examples of datasets that would benefit include:

  1. Business: Marketing strategies, Targeted Sales , Fraud Detection
  2. Drug Discovery: Better Profiling including Adverse Events and Healthcare Outcomes
  3. Insurance Premium Optimization: finding variables, often non-intuitive,  providing clues into a prospect or customer’s purchasing behavior and risk level
  4. Environmental: Decision making for Environmental Management, Population Dynamics, Habitat Suitability
  5. Epidemiology: Risk Analysis, Population Dynamics