Mapping Brand Drift

POSTED IN: Building Bridges from R to IBM Watson

Brand Perception in Social Media - Mapping Archetypes - Viz Foundation

thinking about what it would look like for a retail brand or luxury brand to visualize the brand over time

  • assume access to 10k+ social media sources that are expressing views on the brands on semi-regular basis
  • assume we're clustering these folks together to nearest neighbor -demographics/relationship with brand
  • measuring over many weeks months or years
  • using IBM Watson (Alchemy Language, Custom NLC trained for traits that matter) - to pop signals out of group's collective voice



Basic Viz Foundation

so the graph above is dummy data, but I needed to get a basic start on how to graph movement by cluster over time on the


This Hypothetical Example

In this case, I was thinking about three distinct groups of folks who were engaging with a slightly different face of a luxury retail brand. 

  1. Progressives - talking good stuff about the brand and MEASURED AS RESPONDING to the movement to be innovative and leading edge. Happy.
  2. Traditionalists - also happy - but other component is resonating with them - OLD SCHOOL - NOSTALGIA - Also Happy.
  3. Disaffected - Not getting the message and getting growly about the brand


Other Use Cases

    - Support Calls

    - Customer Service

    - HR / Performance Reviews

    - Education Evaluation



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    About the Author

    Ryan Anderson

    Ryan Anderson

    Hi! I like to play with data, analytics and hack around with robots and gadgets in my garage. Lately I've been learning about machine learning.

    About this blog

    This is an informal blog that explores tools, code and tricks that group members have developed to engage IBM Watson cognitive computing services - from the R Programming Language. Packages include RCURL to access Watson APIs - for services that include Natural Language Classifier and Speech to Text. THIS IS MY PERSONAL BLOG - it does not represent the views of my employer. Code is presented as 'use at your own risk' (it has lots of bugs)

    Created: September 13, 2015


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