This document provides information about a Microsoft Showcase session on using Azure Machine Learning (ML) and Dynamics 365. The session agenda covers ML and AI concepts, how they apply to Dynamics 365, an introduction to Azure ML, and how ML can be used with Dynamics 365 today. It also discusses current ML options in Dynamics 365, such as product recommendations, knowledge article suggestions, and opportunity scoring. Resources for further learning are provided.

1. Microsoft Showcase
Jivtesh Singh
Microsoft Business Solution MVP
Azure ML and Dynamics 365

2. 2
SPEAKER INFO
Headshot And/Or
Company Logo
2
Jivtesh Singh
Rabobank Australia
CRM Business/Technical Analyst
Microsoft Business Solutions MVP
Over 8 Years with Microsoft Dynamics (GP, AX
and CRM)
2 Patents with Azure ML
Jivtesh@gmail.com
BLOG: jivtesh.com
@jivtesh

3. • How many of you are Dynamics End Users ?
• How many of you are Partner Employees /
Consultants ?
• How many of you have tried out Azure ML ?
• How many call yourself technical ?
QUICK POLL
3

4. • Machine Learning and AI Concepts
• How they Apply to Dynamics 365
• Azure ML – Introduction
• How you can use Machine Learning with Dynamics
365 Today
• Resources
AGENDA
4

5. MACHINE LEARNING AND AI
CONCEPTS

6. Machine Learning & Predictive Analytics-
$153 BILLION ESTIMATED MARKET FOR AI
SOLUTIONS BY 2020 — BANK OF AMERICA MERRILL
LYNCH

7. 7

8. 8

9. 10

10. Qualify Develop Propose Close
Predictive Lead Score
with External Data
Recommend product
Predictive
Opportunity Score
Automatically create
Lead from Post
2 - The lead will be routed to
telemarketing. Telemarketer contacts
John only 2 minutes after his post and
adds further information to the lead based
on the phoneconversation with John.
3 - Based on the updated lead
information CRM performs
predictive lead scoring with
both internal and external (bing)
data.
4 - As the predictive lead score is > 80 (hot lead) the lead is
automatically classified as sales qualified lead and routed to sales
rep. Furthermore CRM automatically creates opportunity record.
Sales rep performscustomer call and is adding further
information (budget, timeline, role of contact, other signals)
5 - Based on the updated opportunity
information CRM performspredictive
opportunity scoring.
Sales rep opensthe opportunity and
understandscurrent score, probability to close.
7 - Based on the opportunity
information CRM system provides
upselling information.Product
recommendation to sell e.g.
outsourcingservice
6 - CRM is displaying reasoning (signals
identified). Sales rep receives information
through which step probability to win can be
increased (suggest referencecall with specific
customer)
8 - Sales rep picks up the information and
is closing the now bigger deal faster.
Intelligent Customer Engagement with CRM, Azure ML & IoT
1 - John (CIO at 123 Health) is tweeting in
regard to a Contoso product(medical
measurement device). Through MSE we
automatically determine intent and create
lead from twitter post

11. 12

12. 14

13. WHATS NEW IN THE LAST FEW MONTHS
15

14. 16

15. TodayFirst thing in the morning, open the CRM
App to be welcomed with the new
Landing Space.
Relationship Assistant

16. Scrolling up we can see it’s a feed of
actionable cards.
Looks like Maria recently asked me to set
up a meeting via email.
Relationship Assistant

17. Relationship Analytics
Automatically capture rich signals
from Exchange* and CRM to
determine relationship health, risk
and best next action
Find who are the top contacts for
Accounts & Opportunities and
who in your team can best help
Determine if you are spending
your time on the right
opportunities, stakeholders and
activities
Coach and guide your sales team
based on accurate activity data

18. USING AZURE MACHINE LEARNING

19. Data Science is far too complex today
• Access to quality ML algorithms, cost is high.
• Must learn multiple tools to go end2end, from data acquisition, cleaning
and prep, machine learning, and experimentation.
• Ability to put a model into production.
This must get simpler, it simply won’t scale!
Data Science
Complexity

20. Reduce complexity to broaden participation
Microsoft Azure Machine Learning
Features and Benefits
• Accessible through a web browser,
no software to install;
• Collaborative work with anyone,
anywhere via Azure workspace
• Visual composition with end2end
support for data science workflow;
• Best in class ML algorithms;
• Extensible, support for R OSS.

21. Microsoft Azure Machine
Learning
Features and Benefits
Rapid experimentation to create a
better model
Immutable library of models, search discover
and reuse;
Rapidly try a range of features, ML algorithms
and modeling strategies;
Quickly deploy model as Azure web service to
our ML API service.

22. Steps to build a Machine Learning Solution

23. QUICK LOOK

24. DYNAMICS CRM MACHINE LEARNING OPTIONS
RIGHT NOW
29

25. • Option 1 – Azure ML Previews*
• Option 2 – Opportunity Model
• Option 3 – Build your Own ML Integration
CURRENT OPTIONS
30
*Azure Machine Learning integration with CRM Online is only
available for instances in the North America (NA) region.

26. • Preview feature: Create and manage models to make product
recommendations
PRODUCT RECOMMENDATIONS
31

27. • Preview feature: Automatically suggest knowledge articles
AUTOMATICALLY SUGGEST KNOWLEDGE ARTICLES
32

28. TOPIC ANALYSIS
33
Preview feature: Topic analysis

29. Preview Feature : Suggest similar cases for a case
SUGGEST SIMILAR CASES
34

30. OPPORTUNITY MACHINE SCORING MODEL FOR
DYNAMICS CRM
36

31. • Available at opportunity.codeplex.com
• Opportunity Scoring uses a machine learning model trained on past
sales data to predict the eventual probability that the sale will be
won.
• Template architecture and End to End Reference
Solution
WHAT IS THE OPPORTUNITY MODEL
37

32. 38

33. 39

34. 40

35. 41

36. 42

37. Resources
• Dynamics CRM: USE MACHINE LEARNING TO CALCULATE
HAPPINESS INDEX – Manny Grewal
• Predictions in Dynamics CRM with custom Azure Machine
Learning integrations - Lucas Alexander
OPTION #3 – BUILD YOUR OWN AZURE ML INTEGRATION
44

38. • Machine Learning Concepts
• Azure ML CRM Preview Features
• Azure ML with and End to End Solution
• Resources
OVERALL SUMMARY
45

39. 1. Sign up for Azure ML Trial and play
2. Download Opportunity Scoring Model, and test it
opportunity.codeplex.com
3. Review Scenarios
NEXT STEPS
46

40. MORE RESOURCES !
47
OPPORTUNITY SCORING MODEL FROM MICROSOFT
• http://opportunity.codeplex.com/
• https://opportunity.codeplex.com/wikipage?title=Machine%20Learning%20Model&version=10
• https://opportunity.codeplex.com/wikipage?title=Installer%20features&IsNewlyCreatedPage=true
CORTANA INTELLIGENCE WITH SQL SERVER
• https://gallery.cortanaintelligence.com/Tutorial/Operationalize-Azure-ML-solution-with-On-premise-SQL-Server-using-Azure-data-factory-2
GARTNER ON AI AND MACHINE LEARNING
• http://www.gartner.com/smarterwithgartner/gartner-2016-hype-cycles-reveal-4-megatrends/

41. Complete your session evaluation on MyIgnite
for your chance to WIN one of many daily prizes.
(image of prizes tbc)
Session evaluation

42. Visit Channel 9 to access a wide range of Microsoft training
and event recordings https://channel9.msdn.com/
Head to the TechNet Eval Centre to download trials of the latest
Microsoft products http://Microsoft.com/en-us/evalcenter/
Visit Microsoft Virtual Academy for free online training visit
https://www.microsoftvirtualacademy.com
Continue your Ignite learning path

43. Microsoft Ignite