Opinion A Tasting Menu Of Customer Analytics Techniques

Published
  • April 18 2016, 6:30am EDT

The primary objective of customer analytics is to transform data into valuable insights that impact organizational goals.  With an abundance of organizational goals, petabytes of data at their disposal, and a whole slew of potential techniques for analyzing that data, customer insights professionals often (quite ironically) find themselves in a state of analysis paralysis.

Forrester’s updated TechRadar™: Customer Analytics Methods, Q2 2016, originally published in 2014, aims to help CI pros by highlighting 15 customer analytics techniques their peers are using to extract insights from their data.

In this edition, we have emphasized the need for customer entity resolution, a foundational precursor to many of these techniques.  We have also broadened sentiment analysis to text analytics to reflect the move toward more actionable categorization of unstructured data.  And we have updated examples of relevant technology and services vendors, the estimated cost of implementation, and our assessment of where each technique sits on the analytics adoption curve.

The techniques run from descriptive to predictive, and employ structured, unstructured, and geospatial data.  Potential use cases run the customer lifecycle gamut from acquisition to personalization to loyalty and retention.  Since customer analyses don’t exist in a vacuum, the report describes the interrelationships and dependencies between different techniques. 

CI pros who face an ever-expanding list of stakeholder requests should use read this report to help plan and prioritize customer analytics projects.

Register or login for access to this item and much more

All Information Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access