Data Science for Customer Service

Sameer  Maskey  

About •  Founder & CEO, Fusemachines •  Adj. Asst. Professor, Columbia University •  PhD Computer Science •  Teach Data Science, Machine Learning/Statistical

Methods for Natural Language Processing (NLP) in Columbia University

•  We sell Customer Service Automation Platform

We're  sorry  all  a/endants  are  

currently  busy.  Please  con8nue  to  hold  and  

your  call  will  be  answered  by  the  next  available  agent.  Thank  you  for  your  pa8ence  

42+ Billion Customer Service Interactions a Year

Not  close  to  reality  Close  to  reality  

Technology Hasn’t Caught up for Call Center Automation

Easy for humans to learn language; Machines are terrible in learning language

True Conversational Machines Not a Reality Yet…

Can  We  S8ll  Automate  Customer  Service?  

Customer Interactions Social  Web  In  Person   Mobile   Live  chat  Email   Telephone  

Contact  Center  

Browse community forum

Browse company website

Browse FB page

Tweet

Email a service agent

Visit an in store sales agent

Receive info via sms

Navigate an IVR via a smartphone

Online chat forum

Call customer support

Data Generated in Customer Interactions

Social  Web  In  Person   Mobile   Live  chat  Email   Telephone  

Data

Data

Data

Data

Data

Data

Data

Data

Data

Data

Contact  Center  

Data

Data

Data

Data

Data

Data

Data

Data

Data Data

Can We Use Generated Data to Enhance Customer Service?

Social  Web  In  Person   Mobile   Live  chat  Email   Telephone  

Data

?  ?

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Contact  Center  

Data ?

Automation From Data Social  Web  In  Person   Mobile   Live  chat  Email   Telephone  

Automatically rank new piece

of knowledge from

community forum Data

Data

Data

Data

Data

Data

Data

Data Data

Data

?  ?

?

??

??

?

??Based on

context pre-empt and display different help options

Predict the best next step/response based on tweet

Predict the product purchase

Rank the channel preference

Provide best automated response

Predict which agent to transfer

Data ?

Automation From Data Social  Web  In  Person   Mobile   Live  chat  Email   Telephone  

Contact  Center  

Historical Purchase/ Order Data Context

Data

Historical Usage Data

Tweets and post

Web forum text

Email Text Speech

Automatically rank new piece

of knowledge from

community forum

Based on context pre-empt and display different options

Predict the best next step/response based on tweet

Predict the product purchase

Rank the channel preference

Provide best automated response

Predict which agent to transfer

Automation From Data Historical Purchase/ Order Data

Context Data

Historical Usage Data Tweets and

post

Web forum text

Email Text Speech

Automatically rank new piece of knowledge

from community

forum

Based on context pre-

empt and display different

options Group the

customers for next best action

Predict the product

purchase

Rank the channel

preference Provide best

automated response

Predict which agent to transfer

Unstructured Data Structured Data

Historical Purchase/ Order Data

Context Data

Historical Usage Data Tweets and

post

Web forum text

Email Text Speech

Automatically rank new piece of knowledge

from community

forum

Based on context pre-

empt and display different

options

Predict the product

purchase

Rank the channel

preference Provide best

automated response

Predict which agent to transfer

Ranking   Classifica8on   Clustering  

Group the customers for

next best action

3 Fundamental Problems

•  Data to Scores - Ranking

•  Data to Classes/Labels - Classification

•  Data to Clusters - Clustering

Data Science for Customer Service

•  Data to Scores –  Rank answers to provide end users –  Rank channel preference –  Rank new piece of knowledge in community forum

•  Data to Classes/Labels –  Predict answer type machine should respond with –  Predict next best action for customer service representative –  Predict which agent to transfer to

•  Data to Clusters –  Cluster customers based on various features –  Cluster topics from text data

•  Data to Scores –  Rank related questions based on context –  Rank answers based on the question input

•  Data to Classes/Labels –  Predict answer type (number vs list, etc) –  Predict dialog act (statement vs rhetorical question)

•  Data to Clusters –  Cluster topics from text data

Data Science in Fusemachines Customer Service Automation

Platform

Data to Scores Ranking of related questions based on context

Rankings  

x  

z  

y  

Data to Classes/Labels

Generative Classifier

Given a new data point find out posterior probability from each class and take a log ratio

If higher posterior probability for C1, it means new x better explained by the Gaussian distribution of C1

p(y|x) = p(x|y)p(y)p(x)

p(y = 1|x) ∝ p(x|µ1,

1)p(y = 1)

•  Learn from a few samples of data on how to tag topic labels of answers

Classes  

Data to Clusters •  Automatically cluster text at various granularity –

sentences, passages and documents •  Use the cluster label as kayak like filters for

customer service reps to find answers quickly

Clusters  

Data Science for Customer Service

•  Data generated in customer interactions can be used to improve customer service automation using machine learning algorithms

•  Data Science can be applied across all data sets that is generated in customer-company interaction points

•  Full Automation (Conversational Robots) not a reality but we hope that these small steps will take us closer to that possibility

Thank you

@sameermaskey @fusemachines

Facebook.com/fusemachines

linkedin.com/company/fusemachines

smaskey@fusemachines.com