Machine Learning for Customer Success · SaaS Systems of Record Machine Intelligence People ‣...
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Machine Learning for Customer Success Joshua Bloom CTO (Wise.io), Professor (UC Berkeley) @pro%sb Creative Intelligence, North America Technology Forum 2016
Transcript of Machine Learning for Customer Success · SaaS Systems of Record Machine Intelligence People ‣...
Machine Learning for Customer SuccessJoshua'Bloom'
CTO'(Wise.io),'Professor'(UC'Berkeley)
@pro%sb
Creative Intelligence, North America Technology Forum 2016
COPYRIGHT 2016, WISE.IO INC.
COPYRIGHT 2015, WISE.IO INC.
WISE SUPPORT
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T I E R 1
AU TO M AT E D
R E S P O N S E
C U S TO M E R
F RO M C O M P L E X I T Y TO C L A R I T Y
INTELLIGENT ROUTING
RECOMMENDED RESPONSE
AUTOMATED REPLY
Wise Support>30%
faster avg. response
time
more consistent answers
faster scaling of support teams
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A Machine Learning System Most academic CS/stat work here…
Copyright 2014, wise.io inc.
The most time consuming part…
This is hard…
Hard to understand for non-experts...
Prescriptive actions/actionable insight
What really matters…
What are you optimizing for?Component What
Algorithm/Model Learning rate, convexity, error bounds, scaling, …
+ Software/HardwareAccuracy, Memory usage, Disk
usage, CPU needs, time to learn, time to predict
+ Project Stafftime to implement, people/resource costs, reliability,
maintainability, experimentability
+ Consumers direct value, useability, explainability, actionability
+ Society indirect value
- multi-axis optimizations in a given component
- highly coupled optimization considerations between components
- myopic view can be costly further up the stack
AugmentManual Automate
P(confidence)
Confidence
t=0t=1
t=3 Risk/Cost
Confidence
Manual Augment
Augment Automate
high
low high
Human-Machine Interface
Fault Tolerant ML/UXaugmentation vs. automation
Random forest prediction of body segment in Xbox
Kinect
gmail
Prediction API
in-houseas a service
experimental/sandbox
production/scale ready
watsonAPI
Prediction API
in-houseas a service
experimental/sandbox
production/scale ready
watsonAPI
time & cost to
implement cost to maintain
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Machine Intelligence For Customer Success
Support & Service as a bridge to value creation
Customer Support Product: ○Intelligent Routing/Triage ○Response Recommendation ○Auto-Response ○Knowledge-base Deflection ○Federated Search ○Spam Filtering ○Sentiment Prediction ○Proactive Support (IOT)
Enhancing Decisions in (SaaS) Workflows
COPYRIGHT 2016, WISE.IO INC.
Hardware
Networking/Infrastructure
Software-defined Platform
SaaS Systems of Record
Machine IntelligencePeople ‣ Systems of Intelligence, with machines and
people is the "last mile" of the 21st century software stack ‣ Machine learning differentiates core (data) products, but soon will be apart of common lines of business tasks ‣ Wise.io customer support example of "fault-tolerant" human-machine interface
Summary
@pro%sb
Backup Slides
http://research.google.com/pubs/pub43146.html
• Complex models erode abstraction boundaries
• Data dependencies cost more than code dependencies: weak contracts
• System-level Spaghetti• Changing External World
“It may be surprising to the academic community to know that only a fraction of the code … is actually doing ‘machine learning’. A mature system might end up being (at most) 5% machine learning code and (at least) 95% glue code.”
see also, Bottou (Facebook) ICML
https://www.reddit.com/r/funny/comments/3e7gy4/yes_netflix_because_my_6_year_old_will_enjoy_the/
“Yes Netflix, because my 6 year old will enjoy the animated fun of
Sons of Anarchy”
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Selected Customers
