Is 2017 The Year AI Revolutionizes Customer Service?
Is 2017 The Year
Perspectives Gained From
Decades of Experience
Eric is a tested startup leader and
team builder, with experience
launching, developing and inspiring
customer support teams. His most
recent positions were at Dropcam
(now part of Nest Labs/Google) & Zoosk.
Senior Consultant, Customer
CEO, Customer Service Lab
Mahesh Ram is the founding CEO
at Solvvy, an intelligent self-service
platform for customer service. Mahesh
is a serial entrepreneur, most recently
serving as CEO of GlobalEnglish
Corporation (acquired by Pearson
PLC in 2012).
CEO at Solvvy
Savvy Customer Service professionals
know artificial intelligence and machine
learning have the potential to make
support interactions easy and effective.
But some emerging AI products, like
chatbots, failed to live up to their promise
last year. Now the question on everyone’s
mind is “Will 2017 Be The Year AI
Revolutionizes Customer Service?”
Solvvy partnered with Customer Service
Lab to produce the content in this e-
book. Join us in exploring practical CS
applications of artificial intelligence and
Sophie is the founder of Customer
Service Lab, and she has spent her
career working with startups in
customer support with some of the
most innovative companies like Leap
Motion, Tile, Moo, Etsy, Indiegogo,
Teespring and more.
An Introduction To Terminology
Teaching machines to think the
way that humans think.
1950’s 1980’s 2010’s
Artificial Intelligence is a 60 year-old
term, but new AI technologies just started
coming to market in the last few years.
Machine Learning was popularized by
a computer chess program that learned
from its mistakes to become a more
formidable opponent over time.
Deep Learning is an iteration of machine
learning. It is machine learning algorithms
that run on multiple layers.
Also important is Natural Language Processing.
NLP is the ability for a computer program to understand human speech, regardless of slang or dialect.
Algorithms that mimic the brain’s
neural networks to learn without
The ability for machines to
learn without being
Customer Service organizations are
under pressure from triple demands of
increasing volume of support requests,
rising expectations of customers and
the need to contain costs.
Are Chatbots The Answer?
Highly engineered to work with
constraints and controls.
Fragile and likely to break around
Bots need to learn how to talk with
humans, not vice versa.
How AI/ML Impacts
Customer Service Today
Handles Basic, Repetitive Tasks
Boring questions are being
automated, making customer
service jobs more engaging and
Identifies and Predicts Patterns
ML programs identify trends in
user behavior that humans can’t
AI is already playing a major role in
Customer Service today.
Right now, chatbots are highly effective
in a narrow set of scenarios, but when you
start conversing with a chatbot, it fails.
Most bots on the market today do not learn
from prior agent interactions. They are
hard-wired to work. Chatbots are difficult
to maintain and hard to update because
they do not sufficiently leverage NLP.
The Truth About Chatbots
Today when a chatbot runs into an
unknown scenario, it is prone to creating
terrible experiences and negatively
impacting CSAT. The technology is not
there yet to support a fully adaptive
conversation, but it could be possible in
the not-so-distant future.
Automates Customer Contact
By effectively understanding
customer behavior and needs,
AI automates interactions.
Advanced technologies like artificial
intelligence and machine learning create a
more robust self-service offering for your
AI and ML offer four competitive
AI / ML is the secret
sauce for self-service.Your Customers Prefer
IT IS FAST
IT IS EASY
Self-service adoption is on the rise.
Nearly 9 out of 10 adults have used
self-service support in the past 12
IT IS CONVENIENT
Customers want to get their issue
resolved at any point. Virtual agent
use jumped from 28% in 2012 to
58% in 2015.
Your customers look for the least amount of friction when contacting your support.
Data: Forrester Trends 2016, The Future of Customer Service
Customers say valuing their time
is the most important thing
a company can do for good
customer ser vice.
How To Use AI/ML In Customer Service
Improving customer facing interactions is not
a new idea, but recent tools leveraging AI/ML
have improved its efficiency. Examples of a
front-end approach are guided or contextual
self-service, improving knowledge base
navigation functionality and surfacing the
right content to your users at the right time.
Makes agents more efficient
Quickly locate the best answer and then
deliver it through any channel. Customer
appreciate quick responses from agents
and it drives higher CSAT. Examples of
a back-end approach are learning from
past interaction data to surface the most
successful answer and intelligent routing of
Provides faster resolution
We can show efficiency in areas such
as support or risk operations, where we
don’t want to grow a really large people
organization. It behooves us to lean in
on things like machine learning, even AI
to make our business very efficient.
- Square CFO, Sarah Friar
2016 Q4 Earnings Call
Making AI/ML Work For Your Company
Technology is core to business
Dedicated ML team working on
Expected >$1M annual budget
Specialized, domain-specific need
Buying decision made by team (ex:
support team buys for self-service)
Expected <$75K annual spend
All companies can use commercial solutions to immediately gain automation benefits.
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by Advanced AI and
We help companies improve customer
satisfaction with intelligent self-service
21 3 4 5
Rapid Deployment Fast Case Resolutions Reduced Ticket Volume Improved Knowledge Base Continuous Learning
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