Einstein Analytics Stories and Prediction Builder both provide predictive capabilities within Salesforce, but have some key differences: - Einstein Analytics Stories allows for more in-depth data insights and model visibility, while Prediction Builder focuses on rapid predictions within Salesforce objects. - Einstein Analytics Stories supports both numeric and classification predictions across internal and external data, while Prediction Builder is limited to Salesforce data. - Einstein Analytics Stories provides predictive stories and insights into why predictions were made, while Prediction Builder only shows high-level accuracy metrics and impacting factors. - Einstein Analytics Stories is configured by data analysts, while Prediction Builder is aimed at Salesforce administrators for simple setup.

1. Demistifying Salesforce Predictions
Insight driven decision-making
Using Salesforce’s Einstein to turn data into action
clinsky@salesforce.com
Dr Colin Linsky
Lead Solution Engineer, EMEA Einstein Analytics and AI Team

2. Forward-Looking Statements
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proves incorrect, the results of salesforce.com, inc. could differ materially from the results expressed or implied by the forward-looking statements we make. All statements other
than statements of historical fact could be deemed forward-looking, including any projections of product or service availability, subscriber growth, earnings, revenues, or other
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services or technology developments and customer contracts or use of our services.
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services, our new business model, our past operating losses, possible fluctuations in our operating results and rate of growth, interruptions or delays in our Web hosting, breach of
our security measures, the outcome of any litigation, risks associated with completed and any possible mergers and acquisitions, the immature market in which we operate, our
relatively limited operating history, our ability to expand, retain, and motivate our employees and manage our growth, new releases of our service and successful customer
deployment, our limited history reselling non-salesforce.com products, and utilization and selling to larger enterprise customers. Further information on potential factors that
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3. Topics
1. AI and Salesforce
2. Creating intelligent data-driven experiences
1. Prediction Builder
2. Strategy Crafter
3. Einstein Analytics Plus Stories
A few leading innovations around customer intelligence at Salesforce

4. 1. AI and Salesforce

5. Salesforce Einstein
Artificial Intelligence for CRM
world’s
smartest
CRM
=
customer
data
+ +AI
Salesforce
Platform

6. Salesforce Einstein
Augmented Intelligence for CRM
world’s
smartest
CRM
=
customer
data
+ +
analytics &
algorithms
Salesforce
Platform

7. Einstein Makes our Apps and Platform SMART
Store
Data
Make
Predictions
Analyze
Results
Take
Action
Rapidly
Automate
Trusted Cloud
myEinstein
Lightning
Einstein Analytics
Sales Cloud
Einstein
Service Cloud
Einstein
Marketing Cloud
Einstein
Community
Cloud Einstein
Commerce Cloud
Einstein
Einstein Prediction
Builder
Einstein
Discovery
Einstein Vision
& Language
CRM
data
Email, calendar,
social data
IoT, external
data
Einstein Apps

8. ...Sales smarter ...Service smarter ...Marketing smarter ...Operations smarter
Potential Wins
Which are the most important Leads /
Opportunities to focus on?
Improve CSAT
Which activities are hurting / improving
CSAT?
Increase Response Rates
How can response rates for campaigns be
increased?
Maximize Utilization
How can we drive the utilization of our
products?
Time to Close
How long is it likely going to take to close
an opportunity?
Time to service
When is the next service likely due?
Understanding campaign success
Why and where did our last campaign
succeed / why did it fail?
Quality Management
How can root causes for production
failures be identified?
Pipeline Health
Do we have to potential to make budget
with our existing pipeline?
Solve Cases at first Contact
How can first contact resolution (FCR)
rate be increased?
Customer Targeting
Which are the best customer segments
to focus on?
Increase Cash Flow
What actions can be taken to increase
Cash Flow, and why?
Identifying relevant customers
Which customers do have the potential
to prescribe more?
Deliver Service Faster
How can average handle time (AHT) be
decreased?
Increase Loyalty
How can customer retention & loyalty be
increased?
Minimize payment defaults
Which customer segments impose a risk
of deficit in payment?
Reduce Churn
Which customers have the highest churn
risk and why??
Cross- & Up-Sell
What are the best cross- & up-sell offers
and campaigns?
Optimize Marketing-Mix
What is the most effective marketing
mix?
Product configuration
What are the best default configurations
for our products?
Einstein makes...

9. Will this lead convert?
Will this opportunity close?
Will this customer stop using our services/contract?
Will this customer renew a contract?
Will this case escalate?
Will this case be solved at First Contact?
Can I upsell to this customer?
Will this customer open or click an email?
Will this invoice be paid in time?
Will this product pass QA?
“Yes or
No…..”

10. … is the CSAT going to be?
… revenue will we make with this customer?
… longer before this opportunity closes?
… longer before this case closes?
… longer before we need to service this installation?
… products will be sold this month?
… times will I need to contact this Doctor?
… cases are needed to resolve this issue?
… times a week will orders be placed?
“How
much…..”
“How
many…..”

11. Salesforce Keeps Getting Smarter with Einstein (Winter
’19)
Marketing Cloud Einstein
Einstein Engagement Scoring & Splits
Einstein Vision for Social
Einstein Social Insights
Einstein Journey Insights (DMP)
Einstein Segmentation (DMP pilot)
Service Cloud
Einstein
Einstein Bots
Einstein Agent (pilot)
Einstein Vision for Field Service (pilot)
Sales Cloud Einstein
Einstein Lead Scoring & Oppty Scoring
Einstein Opportunity & Account Insights
Einstein Forecasting
Einstein Activity Capture
Einstein Automated Contacts
Einstein Readiness Assessor
Einstein Data Insights (pilot)
Includes Inbox & Sales Analytics
Commerce Cloud Einstein
Einstein Product Recommendations
Einstein Search Dictionaries
Einstein Predictive Sort (beta)
Einstein Commerce Insights
Einstein Visual Search (beta)
myEinstein
Einstein Prediction Builder (pilot)
Einstein Vision
Einstein Language
Einstein Discovery
Einstein Next Best Action (pilot)
Community Cloud
EinsteinEinstein Answers (pilot)
Community Sentiment (pilot)
Einstein Recommendations
Einstein Feed Insights
Einstein Feed Search
Einstein Experts
Austin Buchan
CEO, College Forward
NEW
NEW
NEW
NEW

12. What if my needs, requirements
and more importantly, my Use
Cases, are unique?

13. Intelligent Experience for Sales
Take Action
4
See What
Happened
2
1 Get Notified
3 See What’s Likely to
Happen
Transparent explanations behind every recommendation
Einstein Delivers Insightful and Actionable Predictions

14. Demo
Not all opportunities are equal – Elevate your thinking…

15. 2. Creating intelligent data driven experiences

16. Decision Support in the Real World
Prediction Builder (Core) (Pilot)
Easy admin, self-sustained prediction engine and deployment
Stories in Einstein Analytics (Einstein Analytics Plus)
Flexible, comprehensive, open and inclusive insight generation and model building capabilities
Einstein Analytics (Einstein Analytics Growth)
Tools for surfacing insights right where they are needed in the format that has the most impact
* Prediction Builder + Discovery (Einstein Predictions)
Building and embedding your own predictions

17. Introducing: Einstein Analytics
Interactive analytics and production-ready dashboards & reports
Interactive Analytics
Unlimited exploration of relationships and differences
Deployable Insights
Embed charts, reports, metrics, guides and many other types of
insight directly into consumer and customer systems using widgets
Industrial Strength Data Handling
Features and functions designed to work seamlessly with
customer data inside and outside of Salesforce
Native CRM Insight into Action Framework
See something and then take action on it right from within the
insight

18. Introducing: Einstein Analytics Stories
Advanced analytics at the heart of Salesforce
Automated Analytics
Analyze millions of data combinations in minutes
Unbiased Insights
Understand what happened, why it happened, what will happen,
and what to do about it
Narrative Explanations
Natural language-based insights and stories exported to Salesforce
or Microsoft Office
Recommended Actions
Take action, stay on top of changes, and gain advantage right
where the decisions happen
speed to insight
+10x
*Source: Salesforce Customer Relationship Survey conducted 2014-2016 among 10,500+
customers randomly selected. Response sizes per question vary.

19. Introducing: Prediction Builder
Automated Predictive Model building and deployment
Automated Analytics
Wizard driven setup and ease of administration
Adaptive Sensitivity
Intelligent model building and control of inputs
Predictions Embedded into Records
Automated Model Management
Model competition and automatic sensitivity to changes in data
patterns
Writeback process automated
https://engineering.salesforce.com/open-sourcing-transmogrifai-4e5d0e098da2

20. An example of Prediction Builder…
Retaining customers in a fickle services market

21. An example of Einstein Analytics Stories…
Retaining and growing accounts

22. Mmmmm…. so what does all of that mean?

23. Analytics needn’t just be about reporting and the management of past behaviours
Analytics should also:
• Understand customer data and customer engagement
• Deliver intelligent support to decision-making
• Be used at the point of interaction with customers or end users
• Look forwards not just backwards
Resulting in:
• A more responsive and intelligent view of all data and actions leading to more informed decisions
and better experiences
Extracting value from data and fueling better decisions
Key Takeaways: Einstein Analytics and Prediction Builder

25. Backup Slides

26. Prediction Builder
Quick Start: Prediction Builder
https://trailhead.salesforce.com/en/content/learn/projects/prediction_builder
Einstein Analytics Plus (Stories/Discovery)
Gain Insight with Einstein Discovery
https://trailhead.salesforce.com/en/content/learn/trails/wave_analytics_einstein_discovery
Einstein Discovery Stories
https://trailhead.salesforce.com/en/content/learn/modules/understand_einstein_discovery_stories
Einstein Discvoery Story Insights
https://trailhead.salesforce.com/en/content/learn/modules/dm_einstein_discovery_advanced_stories
Einstein Discovery Basics
https://trailhead.salesforce.com/en/content/learn/modules/wave_exploration_smart_data_discovery_basics
Einstein Analytics Insight Specialist Superbadge
https://trailhead.salesforce.com/en/content/learn/superbadges/superbadge_analytics_insights_specialist
Trailhead resources

27. Crikey, I think I got that, but remind me what are
the difference between Einstein Analytics Stories
and Prediction Builder?

28. General
o Business-analyst centric tool for gaining full
understanding of data insights: Discover!
o Create predictions with the obtained model
o Input / training data: EA datasets
o Fine-grain visibility on model accuracy
o Rapid point & click approach to generating
predictions on force.com objects: Predict fast!
o See a few high level impacting factors on what
drives the predictions
o Input / training data: force.com data
o Limited visibility on model accuracy
Einstein Discovery Einstein Prediction Builder

29. Predictive capabilities
o Numeric predictions (regression)
o Predicted LTV, Predicted CSAT etc.
o Likelihood of yes/no questions (binary
classification)
o Churn, conversion propensity, escalation etc.
o Piece Generalize Linear Regression with dummy
variables using Ridge Kernels
o No auto-feature engineering, field selection
controlled by users
o Numeric predictions (regression)
o Predicted LTV, Predicted CSAT etc.
o Likelihood of yes/no questions (binary
classification)
o Churn, conversion propensity, escalation etc.
o Regression models, Random forests, Naive
bayes..,
o Auto-feature engineering
o https://engineering.salesforce.com/open-
sourcing-transmogrifai-4e5d0e098da2
Einstein Discovery Einstein Prediction Builder

30. Data insights & Model insights
o Story with data insights to improve model and
increase business understanding
o What happened / Why did it happen
o What could happen / How to improve it
o What is the difference / Story over time
o Precise model accuracy metrics & insights
o R-squared, MAE, residual based metrics
(regression) etc.
o Specificity / Sensitivity based metrics
(classification)
o No Story, model built with auto-machine
learning for simple point & click setup
o High-level model metrics & insights
o “Predictive model Score Cord” with accuracy
percentage , top impacting factors
o R2 for numeric, AUC for binary classification
Einstein Discovery Einstein Prediction Builder

31. Data Sources & Deployment
o Internal & External Data, Multiple Objects
o Manual data set creation / preparation
o Get Record based predictions in force.com
o With APEX / managed package
o Possibility of (near) real time
o Manual data refresh / retrain
o Setup by “Data analyst” & Admin
o SF Internal data only, Single object
o Directly from org, pick fields & apply filters
o Get Record based predictions in force.com
o Automatically
o A-synchronous (every hour)
o Automatic data refresh/ retrain
o Setup by Salesforce Admin
Einstein Discovery Einstein Prediction Builder

32. Security, Data residency & Encrypted fields
o Running on Core data centers
o Encryption in transit and at rest
o Manual salesforce connection, include
encrypted fields if wanted
o Customer decides on input fields and can
decide to leave out data such as PII data
o No FLS applied to impacting factors
o Running on AWS in the US
o Connection between Salesforce Core and AWS
managed by Salesforce
o Encryption in transit and at rest
o No access to encrypted fields, encrypted fields
are not part of the model
o All data (excl. encrypted) moved to Einstein
data lake, even if not in the model
o FLS applied to impacting factors
Einstein Discovery Einstein Prediction Builder