From Customer Success Summit 2017 - Dan Davidge, VP Marketing and Customer Success at AnswerDash, discusses "Helping Your Customers Help Themselves". Learn more about Customer Success Summit: http://customersuccesssummit.com Totango is the leading enterprise- grade customer success platform that helps recurring revenue businesses proactively impact business outcomes with customer success. With solutions to empower Customer Success Teams or entire companies, Totango enables everyone to participate in customer success. Learn more at www.totango.com.

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Help Your Customers Help Themselves
A Look at Predictive Self-Service
& MOO Case Study

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About Me
• Don Davidge
• VP Marketing and Customer Success
• Predictive Self-Service Software

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The Customer Experience Problem
Consumers are frustrated.
To get “HELP” they must:
• Move from their point of action
• Formulate queries
• Pore through random content
Companies pay with:
• Dissatisfaction
• Abandonment
• Churn
• High support costs

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Let’s Delve Into the Problem
61% 57% 53% 0.5%
Static FAQ / KB
attempts fail
Users abandon a site
if they can’t find an answer
Users abandon purchases
if they can’t find an answer
Customers access
static FAQs or KB

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Your Customers Don’t Want to Talk to You
• 72% of customers don’t want
assisted service
• They prefer to use self-service
support rather than phone, email
or chat support

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Why Help Centers Don’t Work
Help Islands
• FAQs and KB take users away from point-
of-action
• Content is random
• Utilized by <1% of users
Key Insight
• Not a content problem
• An experience problem
Why?
• “Help distance”

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Why Smart Self-Service Can Work
Predictive
self-service
80% 20%
20% 80%
The Q&A 80/20 rule
A few questions are asked
most frequently.
frequency
questions
live chat, email, phone

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Questions
Answers
Existing
Q&A
Suggested
Q&A
Channels
Desktop
Web
Mobile
Apps
Chat
Bots
Predictive
Self-Service
Knowledge
BaseSources
Native
Curation
Integrations
Crowdsourcing
• Predictive Q&A is AI powered
• Contextual to the page
• Personalized to the user
• Popularized by the crowd
• Feedback enables system to learn
Predictive Q&A Enables Self-Service
Q&A
▼
Popular Questions Search all questions
▶ How does AnswerDash work?
▶ How does AnswerDash predict accurately?
▶ What are AnswerDash’s measured benefits?
Powered by
▼ How does AnswerDash work?
We leverage users’ context to predict
likely questions and deliver:
• Relevant answers
• At the user’s point of action
• With just a few clicks

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Goals and Requirements
• Improve online experience
• Make it easy for customers to do business with MOO
• Give customers channel choice
• Expose content in context
• Reduce repetitive support tickets
• Out of the box integration
Live Chat
& Ticket
Reduction
+2%
-­‐18%
Order Value
Increase
Results
AnswerDashRIGHT INFO | RIGHT PLACE | RIGHT TIME
AnswerDashRIGHT INFO | RIGHT PLACE | RIGHT TIME
AnswerDash
color study (last round)
Gray (Cool Gray 10 C)
Light (2230C)
Dark (7715 C)
Case Study
“AnswerDash is a game changer to our customer service approach. It’s saving my support representatives from constantly dealing
with repetitive inquiries, allowing them to spend more time with customers needing more personalized assistance.”
– Dan Moross, Director of Customer Experience, MOO

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4 Key Ingredients
Award winning
customer
experience
Delightful
customer
services
Great
agent
experience
Valuable
customer
insights
Transformation
and continuous
improvement
Awesome Self-Service Results

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Improved Customer Experience
• Feedback from frequent users has been super positive
• We’ve reduced customer effort
• Users spending more time on site and rated our content
helpful
• Uptake of help content has increased by more than 4x
MOO CASE STUDY 2016

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Great Agent Experience
• Contacts per order are 20% lower in 2016 vs. 2015
• We’ve reduced agent effort - self-service journey, is helping
them to diagnose issues more quickly
• Interactions with customers more interesting and engaging
MOO CASE STUDY 2016

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MOO CASE STUDY 2016
Valuable Customer Insights
• Insight from the reporting dashboard has helped us to
identify gaps and inaccuracies in our content
• Product Managers and UX Designers able to identify
weaknesses in our product offering, content and UX

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Top Line and Bottom Line Results
• Incremental revenue – increased £1.5M incremental in
2016 due to increase conversion rates
• Savings in customer services - 20% decrease in
contacts saved us £300K in 2016
MOO CASE STUDY 2016

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THANK YOU

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Q&A
don@answerdash.com

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Rapid Trend Toward Self-Service
“By
2020,
85%
of
customer
engagements
with
enterprises
won’t
involve
a
human”
2014
Predictive
Q&A
AnswerDashRIGHT INFO | RIGHT PLACE | RIGHT TIME
AnswerDashRIGHT INFO | RIGHT PLACE | RIGHT TIME
AnswerD
color study (last round)
Gray (Cool Gray 10 C)
Light (2230C)
2008
Mobile
Apps
Virtual
Agents
2000
1996
Airport
Kiosks
Web
FAQs
1995
1988
Knowledge
Bases (KBs)
IVR
1980s
1970s
ATMs
2016
Chatbots

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Flip Your FAQ
Static
FAQ
/
KB
• Help island
• Random content
• Static Q&A
• Keyword search
• Usage: 0.5%
• Increased support tickets
• Answers at point of action
• Contextual content
• Dynamic Q&A
• Point and click
• 10x-36x engagement
• 30%-50% ticket deflection
Predictive
Self-­‐Service