1.
New Delhi Salesforce Developer Group
#ImpactSalesforceSaturday
Einstein – Use cases and Products
By: Jayant Joshi
LEARN . SHARE . CELEBRATE . SALESFORCE

2.
About New Delhi Salesforce DG
• First Revival Meetup in February 2016
• Twitter: https://twitter.com/newdelhisfdcdug
• New Delhi Salesforce DG Trailblazer Community Group:
http://bit.ly/NewDelhiCommunity
• Facebook: https://www.facebook.com/newdelhisfdcdug
#ImpactSalesforceSaturday

3.
What is #SalesforceSaturday
• Started by Stephanie Herrera in Austin, Texas
• Meetup every Saturday in a Coffee Shop or anywhere to share and
learn about Salesforce
• It’s now a global phenomena with more than 25 SalesforceSaturday
Group over 5 Continents
• For India, it comprises of Online Knowledge Sharing sessions and
Trailhead Challenges
#ImpactSalesforceSaturday

4.
TIME Topic
‘15 What is AI and Machine Learning?
AGENDA
’15 Demo
‘25 Salesforce Einstein – Use Case and Products
’05 Q & A and Wrap-Up

5.
About Me: Jayant Joshi #ImpactSalesforceSaturday
Professional:
- SFDC Technology Architecture Managing Delivery Architect
- Around 14 Years of overall experience and 9+ years in SFDC
- Working in CG but have worked in Accenture from the last 6 years.
Have worked with Deloitte Consulting and IBM earlier.
- Have worked in India, US, Canada, and Germany.
- Enterprise Architecture/TOGAF
- Passionate about SFDC
- Among Top SFDC Certified People in World
- Regularly contribute to Salesforce related articles on Social
Media
- Mentoring around SFDC Topics
- Upcoming Public Sessions on SFDC Topics in India and
Germany
- Additional Skills: SFDC Commerce Cloud, Machine Learning, IOT,
TOGAF
Hobbies include Travelling (Have visited 23+ countries so far),
Sky Diving and Astronomy.
1 Min

6.
What is AI and Machine
Learning?
29.06.2019
15 Min

7.
WHAT IS AI?
As per Wiki, Artificial intelligence (AI), sometimes called machine intelligence, "is intelligence
demonstrated by machines, in contrast to the natural intelligence displayed by humans and other
animals".
1 Min

8.
AI – Current Use Cases
Let us look at some examples of AI already used currently (as of 2019):
 Google Assistant for engaging in a two-way conversations
 A powerful and a very successful recommendation engine is used by Amazon for
Products Search and Recommendations. They use machine learning behind the scenes
 Image recognition by companies like Google (Google Photos), Facebook (Facebook
App) etc. They use the Image recognition algorithms for this purpose
 Navigation Apps like Apple Maps, Google Maps, Waze etc.
 Cab sharing Apps like Uber, Ola, Lyft are using advanced Machine learning techniques
to provide best user experience to their customers
 Harvard scientists used Deep Learning to teach a computer to perform viscoelastic
computations, these are the computations used in predictions of earthquakes
1 Min

9.
AI – Facts and Figures
• By 2025, the global AI market is expected to be almost $60 billion; in 2016 it was
$1.4 billion
• Global GDP will grow by $15.7 trillion by 2030 thanks to AI
• AI can increase business productivity by 40%
• AI startups grew 14 times over the last two decades
• Investment in AI startups grew 6 times since 2000
• Already 77% of the devices we use feature one form of AI or another
• Cyborg technology will help us overcame physical and cognitive impairments
• Google analysts believe that next year, 2020, robots will be smart enough to mimic
complex human behavior like jokes and flirting
30
Seconds
Source: https://techjury.net/stats-about/ai/

10.
How do we describe 'Intelligent Behavior'?
 Learn from experience
 Determine what is important
(Prioritize)
 Solve Problems
 Handle complex situations
 Apply knowledge acquired from
experience (Very Important trait
for being human)
 …
2 Min

11.
How can machine acquire Intelligence?
2 Min
 Learn from experience (As human do)
 Determine what is important (Prioritize)
 Solve Problems (as human do)
 Handle complex situations (As human
do)
 Apply knowledge acquired from
experience

12.
And what is Machine Learning (ML)?
“Machine learning (ML) is the ability for computers to learn
and act without being explicitly programmed”. The key
idea behind ML is that it is possible to create algorithms
that learn from and make predictions on data.
Some of the examples of ML from real life are:
 Amazon and Netflix Recommendations for products you
buy and movie recommendations respectively.
 Spam Filters of your favorite email account (e.g. Gmail)
keep on learning as the new data (spam messages)
comes on.
2 Min

13.
Machine Learning - Process
2-3 Min

14.
Types of Machine Learning
• Supervised learning is when you have
knowledge of the input (X) and the
output (Y), then you “supervise” the
program in predicting the right outcome
via trial and error. This means mapping
input data to known labels, which
humans have provided.
• Unsupervised learning is when you
have zero knowledge of the output and
you want to try to find patterns or
groupings within the data.
2-3 Min

15.
Good to Learn…
• Deep Learning
• Machine Learning Algorithms (at least a few)
• Machine Learning Frameworks and Tools
• Application of AI across various industries
• …
1 Min

16.
Now, talking about Salesforce Einstein…
 Einstein products are based on Machine Learning models.
 Every machine learning model needs a lot of data to make
better predictions. Of course, This is the same with Einstein.
 Einstein Products (well, most of them including Einstein
Discovery) identify patterns with the data and make
predictions. e.g. Identify potential late payments)
 Most of the time, as an admin or even a developer you will
not create models in Salesforce but use the Salesforce
Einstein features (which are based on models). e.g. When you
create a Story in Einstein Discovery, the analytical model
building takes place automatically.
 With Einstein Products, many decisions can be
made automatically (of course in many cases, human
intervention is required).
1 Min

17.
Salesforce Einstein – Use
Case and Products
29.06.2019
Total Time: 25
Min

18.
What is Salesforce Einstein?
 Salesforce has embedded a lot of Intelligence into
the existing Clouds. e.g. there are many Einstein features in
Sales Cloud itself (e.g. Intelligent Lead Scoring, Opportunity
insights, Automated Contacts, etc.).
 All (well, almost), Salesforce Clouds have Einstein
features (e.g. Service Cloud, Marketing Cloud,
Commerce Cloud, etc.).
 Additionally, Salesforce provides a lot of other
Einstein Products.
2-3 Min
Einstein Out-of-the-Box Applications and Einstein Platform are the two categories of Einstein.

19.
What are typical AI Use Cases for CRM?
 How can Leads be Priortized for the Sales Reps?
 How can a Sales Rep identify which deals are likely to be won?
 How to handle Customer Service request in a best possible way? How can a company
reduce service request handle time?
 Why company’s Margins are Shrinking?
 How to upsell/cross sell opportunities?
 How to take notes while taking with Voice Assistent?
 How to predict customer sentiment?
 How to add recommendations in the CRM data?
 How to predict customer attritions?
 How to predict customers with Late Payments?
 ...
5 Min

20.
Use Cases and Einstein Products
2-3 Min
CRM AI Use Case
• How can Leads be prioritized
for the Sales Reps?
• How can a Sales Rep identify
which deals are likely to be
won?
• How to handle Customer
Service request in a best
possible way? E.g. How can a
company reduce service
request handle time?
• How can service requests be
deflected? E.g. Reduce the
number of cases
Einstein Product / Feature
Sales Cloud Einstein:
• Automated Lead Scoring
• Opportunity Insights
• Opportunity Scoring
Service Cloud Einstein:
• Case Classification
• Einstein Bots
1

21.
Use Cases and Einstein Products
2-3 Min
CRM AI Use Case
• How can you automate
image classification to
resolve issues faster?
• How to understand
customer buying
behaviour and take
actions?
Einstein Product / Feature
Field Service Lightning
Einstein:
• Einstein Vision for Field
Service
Pardot Einstein:
• Einstein Behavior
Scoring
2

22.
Use Cases and Einstein Products
2-3 Min
CRM AI Use Case
• How to help shoppers find
exactly the products
they’re looking for?
• How to have highly
relevant product
recommendations tailored
to every shopper?
Einstein Product / Feature
B2C Commerce:
• Einstein Predictive Sort
B2C Commerce:
• Einstein Product
Recommendations
3

23.
Einstein Bots
 Bots can handle routine requests and free
your agents to handle more complex issues.
 They can even prevent them from being
opened in the first place!
 Bots can also gather pre-chat information to
save your agents time.
 Bots can be trained to understand human
language—and respond intelligently—
through Natural-Language Processing
(NLP).
2 Min
#Einstein Tools
“Chatbots will power 85% of all customer service
interactions by 2020.” - Gartner

24.
DEMO (Einstein Chatbots)
10-15 Min
1. Obtain a Service Cloud license and a Chat or Messaging license. Each org is
provided 25 Einstein Bots conversations per month for each user with an active
subscription. To make full use of the Einstein Bots Performance page, obtain the
Service Analytics App.
2. Enable Lightning Experience.
3. Run the Chat guided setup flow.
4. Enable Salesforce Knowledge if your bot serves Knowledge articles to
customers.
5. Publish a Salesforce community (preferable) or a Salesforce Site.
6. Provide an Embedded Chat button for your customers on your community or
site.

25.
Einstein Prediction Builder
 Point & Click Wizard
 Make custom predictions on your non-
encrypted Salesforce data
 Enrich the prediction by creating other
special fields
 Use rules-based and predictive models to
provide anyone in your business with
intelligent, contextual recommendations and
offer
2 Min
#Einstein Tools

26.
Einstein Analytics (EA)
 Explore your organization’s data and get
interesting insights.
 Get great visualizations using charts and
dashboards
 Easily import the data to Einstein Analytics
 Export data from EA
 Use Apex to pull data in real time to your
Einstein Analytics dashboard.
 Query data with Salesforce Analytics Query
Language (SAQL)
2-3 Min
Note: Topic for Second Einstein Session
(20-July)

27.
Einstein Analytics – How it work?
2-3 Min
Note: Topic for Second Einstein Session
(20-July)

28.
Einstein Discovery
Einstein Discovery provides answers to key
business questions:
• What happened? What was significant or
unusual?
• Why did it happen? What are the factors that
possibly contributed to the observed outcome?
• How do some factors compare with other
factors?
• What might happen in the future, based on a
statistical analysis of the data? Is there a trend,
or does this data represent an isolated
incident?
• What are some possible actions that could
improve the outcome?
2 Min
Note: Topic for Second Einstein Session (20-July)

29.
DEMO 29.06.2019

30.
DEMO
 Einstein Products Setup
 Sales Cloud Einstein Features
 Lead Scoring
 Einstein Chatbots
 Einstein Prediction Builder
 (If Time Permits)
10-15 Min

31.
Next Sessions 29.06.2019

32.
Einstein Sessions
 Einstein – Use Cases and Products,
29.June
 Einstein Analytics and Discovery,
20.July
 Einstein Platform, TBD (Sometime in
August)
1 Min

33.
How to Learn Einstein? 29.06.2019

34.
Einstein Resources
 Salesforce Trailhead
 Einstein Trailmixes
 Salesforce Einstein Help
1 Min

35.
Q & A 29.06.2019
Total Time: 05
Min

36.
Follow & Join New Delhi Salesforce DG
• Join to know about future events and to RSVP:
https://trailblazercommunitygroups.com/delhi-in-developers-group/
• Let’s start conversations on Success Community:
http://bit.ly/NewDelhiCommunity
• Follow us on Twitter: https://twitter.com/newdelhisfdcdug
• Follow us on Facebook: https://www.facebook.com/newdelhisfdcdug
• For all the content: https://newdelhisfdcdug.com
#ImpactSalesforceSaturday

37.
#ImpactSalesforceSaturday