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Call Center Sensemaking Systems 2

55
POSTED IN: Building Bridges from R to IBM Watson

In Pursuit of Kick Ass Customer Service

Just read a terrific article from Harvard Business Review (HBR) here - on "Kick Ass Customer service" 

https://hbr.org/2017/01/kick-ass-customer-service

In the article's conclusion:

"When we share our research with managers, they sometimes cringe at the thought of a service organization full of Controllers, let alone Controllers interacting with their most frustrated and troubled customers. Managers frequently tell us that Controllers “wouldn’t be a good cultural fit” and would lack the requisite empathy to succeed. But our interviews reveal that Controllers are, in fact, quite empathetic. They do understand customers’ needs and frustrations. But they respond in a distinctive way. They recognize that after toiling away online trying to self-serve, customers don’t want an apology—they want a solution."

 

 

Very impressive article - and my key take aways

  1. Segmentation of your Call Reps Matters very much for outcomes (and KPIs)

  2. Traits can be measured - here by survey - but also likely by automated (cognitive enabled) systems

  3. Presents an opportunity for leveraging cognitive to (a) ASSESS CURRENT STATE of Call Center and types of agents; and (b) develop a metric to MEASURE implementation of a strategy - based on some of the HBR insights

 

Call Center Archetypes

Source: HBR Article

  1. The Controller

  2. The Rock

  3. The Accomodator

  4. The Empathizer

  5. The Hard Worker

  6. The Innovator

  7. The Competitor

 

Source: HBR.ORG

 

Cognitive as a Catalyst - Watson APIs

Over the next few weeks I'll be digesting this - and exploring how the following services can be used to measure current state, and ongoing strategy for organizations seeking to implement the insights in the article.

Will leverage some of these tools and methods: https://dreamtolearn.com/ryan/r_journey_to_watson/55 and most likely candidates:

  • Speech to Text

  • Tone Analyzer

  • Alchemy Combined Call

  • Natural Language Classifier (NLC) optimized for archetypes, or archetype subtraits

  • Traditional Data Analytics Tools

 

 

 

More Information (from HBR Article)

 

For more on improving the service experience for customers by reducing their effort, see the following:

“Stop Trying to Delight Your Customers” Matthew Dixon, Karen Freeman, and Nicholas Toman

https://hbr.org/2010/07/stop-trying-to-delight-your-customers

HBR, July–August 2010 “To Keep Your Customers, Keep It Simple” Patrick Spenner and Karen Freeman

https://hbr.org/2012/05/to-keep-your-customers-keep-it-simple

HBR, May 2012 The Effortless Experience: Conquering the New Battleground for Customer Loyalty  (Book)

 

 

 

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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

English

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