Michel Feaster, CEO and co-founder of Usermind, gave a presentation on enhancing customer journeys with machine learning. He discussed how companies can benefit most from machine learning by having rich historical customer data from mature processes and high-volume customer segments. However, operationalizing machine learning models can be challenging due to fragmented customer data across systems and teams. Usermind aims to help companies at each stage of implementing machine learning-driven customer journeys, from unifying customer data to operationalizing models within journeys. Feaster demonstrated Usermind's platform, which uses a customer data platform and journey orchestration engine to integrate systems and personalize journeys with machine learning models.

1. © 2017 Usermind, Inc. 1FORRESTER CXSF 2017
Forrester CXSF 2017
Michel Feaster | CEO & Co-Founder
Enhancing Customer
Journeys with Machine
Learning
@michelfeaster

2. © 2017 Usermind, Inc. 2FORRESTER CXSF 2017
Usermind Overview
Apptio 2009-2012
• VP of Products
• From employee 17 to
$80MM ARR
HPSW 2007-2009
• Led the $1.6B acquisition
and integration of
Opsware
Mercury 1998-2007
• Product management and
presales
Michel Feaster
CEO & Co-Founder
@michelfeaster

3. © 2017 Usermind, Inc. 3
Customer Data is the New Oil
CX Leaders
We’re all here because we believe that CX is the
competitive differentiator in the life and death of
our businesses.
CX Laggards
Compound average revenue growth, 2010 to 2015
17%
3% Source: June 21, 2016,
“Customer Experience
Drives Revenue Growth,
2016” Forrester report
forrester.com/cxindex
CX leaders
grow revenue
faster than
CX laggards.
@michelfeasterFORRESTER CXSF 2017

4. © 2017 Usermind, Inc. 4FORRESTER CXSF 2017
What Customer Problems Can be Solved with Machine Learning?
• Mature business processes with
rich historical data
• A new process with data at very
high volumes
• A process that has high enough
business impact to justify
training the model
• Launching a new business process
• Working with a newly discovered
customer segment or low data volume
• Business processes that don’t justify
spending time training the model to
high degree of accuracy
Ripe for ML: ML is not right for:
@michelfeaster
Companies with rich historical data, mature processes, and high-volume customer segments can
benefit most from machine learning.

5. © 2017 Usermind, Inc. 5
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FORRESTER CXSF 2017
Why is Data-Driven CX Hard?
Customer data is fragmented and siloed by systems & teams
If your customer data is in a single place, it’s often not actionable
— models are difficult to operationalize
If you’ve built machine learning models at scale, you will end up
with model collisions
@michelfeaster

6. © 2017 Usermind, Inc. 6
There’s No Clear Winner in ML Technology Yet
FORRESTER CXSF 2017
Machine
Intelligence 3.0
—IMAGE VIA O’REILY
@michelfeaster

7. © 2017 Usermind, Inc. 7FORRESTER CXSF 2017
Models without State are Bad for CX
@michelfeaster

8. © 2017 Usermind, Inc. 8FORRESTER CXSF 2017
So How Do You Get There?
@michelfeaster
4
Machine learning-
driven customer
journeys
3
Customer journey
orchestration
2
Unified customer data &
integrated systems
1
Disconnected customer
data & systems

9. © 2017 Usermind, Inc. 9FORRESTER CXSF 2017
Usermind Helps at Every Stage of the Curve
@michelfeaster
4
Machine learning-
driven customer
journeys
3
Customer journey
orchestration
2
Unified customer data &
integrated systems
1
Disconnected customer
data & systems
Replace fragile point-to-
point integrations with an
integration hub
Map and connect customer
data into a universal ID
connected to a machine
learning environment
Baseline and collect data
on key customer
journeys across systems,
channels,
and teams
Operationalize
machine learning
models within
journeys
Continuous
experimentation
and iteration on
journeys

10. © 2017 Usermind, Inc. 10
How Does ML Work with Your Current Stack?
@michelfeasterFORRESTER CXSF 2017
Signals &
Events
ActionsMarketingAd Tech Finance & AccountingCustomer SuccessDemand GenerationRetail & Ecommerce
Journey Orchestration Engine
Customer Data Platform
Deploy Your Own Models in Usermind
Build Models on Usermind CDP in
JupyterHub
Deploy 3rd-Party PMML Models in
Usermind
Usermind Integration Layer

11. © 2017 Usermind, Inc. 11
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Operationalizing Models in Customer Journeys
Measure the baseline performance of the customer journey
Build, train and optimize machine learning models (JupyterHub)
using the data in the Usermind CDP
Publish models to Usermind platform
Use models to generate next best actions at individual touch
points
Use models to personalize entire customer journey
Close the loop: measure the performance of models against
business outcomes
FORRESTER CXSF 2017 @michelfeaster

12. © 2017 Usermind, Inc. 12
Demo
FORRESTER CXSF 2017 @michelfeaster

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19. 19© 2017 Usermind, Inc.FORRESTER CXSF 2017 @michelfeaster

20. 20© 2017 Usermind, Inc.FORRESTER CXSF 2017 @michelfeaster

21. 21© 2017 Usermind, Inc.FORRESTER CXSF 2017 @michelfeaster

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2
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Operationalizing Models in Customer Journeys
Measure the baseline performance of the customer journey
Build, train and optimize machine learning models (JupyterHub)
using the data in the Usermind CDP
Publish models to Usermind platform
Use models to generate next best actions at individual touch
points
Use models to personalize entire customer journey
Close the loop: measure the performance of models against
business outcomes
FORRESTER CXSF 2017 @michelfeaster

25. © 2017 Usermind, Inc. 25FORRESTER CXSF 2017
Usermind Can Help at Every Stage of the Curve
@michelfeaster
4
Machine learning-
driven customer
journeys
3
Customer journey
orchestration
2
Unified customer data &
integrated systems
1
Disconnected customer
data & systems
Replace fragile point-to-
point integrations with an
integration hub
Map and connect customer
data into a universal ID
connected to a machine
learning environment
Baseline and collect data
on key customer
journeys across systems,
channels,
and teams
Operationalize
machine learning
models within
journeys
Continuous
experimentation
and iteration on
journeys

26. © 2017 Usermind, Inc. 26FORRESTER CXSF 2017
Thank You. Questions?
@michelfeaster