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Einstein Analytics Prediction Builder
1. Einstein Prediction Builder
All you need to know
Colin Linsky
Lead Solution Engineer, EMEA Einstein Analytics Team
clinsky@salesforce.com
2. Topics for today
1. AI at Salesforce
2. Creating intelligent, data driven experiences
3. Seeing is believing – a worked example of Prediction Builder
5. Don’t just take my word for it!
https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-
“Whether consumers or businesses, customers are
increasingly at the epicenter of the economy, which
is all about improving how customers are served.”
6. Adopting AI
“Many organizations
have successfully
launched cognitive
pilots, but they haven’t
had as much success
rolling them out
organization-wide. ”
8. The Enterprise Can Benefit From AI Too
Iulia Creanga
Sales Process Driver
Schneider Electric
Katrice Riley
Client Success Specialist
ZeroCater
Suvi Silvanto
Marketing Director,
Service Business
Kone
Annika Milton
Sr. Project Manager
adidas
Zac Otero
Salesforce Lead
BKD CPAs
& Advisors
Sales:
increase
conversion
rates
Service:
increase
customer
satisfaction
Marketing:
increase
customer
engagement
Commerce:
increase
revenue
IT:
be innovative,
always
84%
of enterprise execs
believe AI will help
them create a
competitive
advantage
Source: “Reshaping Business with Artificial Intelligence” MIT Sloan Management Review & Boston Consulting Group
9. 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
10. “Nice, you’ve built templated
applications to support standard
decisions but my business is unique
– can I use the same technology
free-form to support my own Use
Cases?”
12. 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…..”
13. … 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…..”
14. … 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…..”
Step One: Define The Problem
Positive
Examples
Records that have
churned
Negative
Examples
Records that have not
churned
Records that we will give a
score for
Records to
Predict
Example: “I want to predict churn, but what does it
mean?”
Break your records down into three or four
buckets
Records to
Ignore
Irrelevant records
(Account has not purchased
in the last six months)
(Account have purchased
in last three months)
(Account that have not purchased
in the last three months)
(Accounts that don’t fit
into the other buckets)
15. … 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…..”
Step Two: Capture Churn in a Formula
Field
“TRUE”
Records that have
churned
“FALSE”
Records that have not
churned
Records that we will give a
score for
NULL
Example: “I want to predict churn, but what does it
mean?”
Represent the buckets as responses from a formula
field
“IGNORE”
Irrelevant records
(Account has not purchased
in the last six months)
(Account have purchased
in last three months)
(Account that have not purchased
in the last three months)
(Accounts that don’t fit
into the other buckets)
17. Decision Support in the Real World
Prediction Builder (Core)
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, and ready to take action from
* Prediction Builder + Discovery (Einstein Predictions)
Building and embedding your own predictions
18. 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
19. 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.
20. 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
25. Einstein Prediction Builder Accesses data from a single object at a time
• All data fields must be found on the Salesforce object
• Prepare data (including rollups/lookups/calculations/formulae) ahead of Model building phase
• Multiple Prediction Builder models can be built on a single object using
• Segmenting
• Filtering
• Model building record set vs scoring record set – bake that mindset into data prep!
Modelling Data
Tips: Operations - Basics
26. Einstein Prediction Builder works with all custom objects, and supports the following standard
objects.
Supported Objects
Tips: Operations – Salesforce Objects
Account
Asset
Campaign
Case
Contact
ContractLineItem
Entitlement
Lead
LiveChatTranscript
Opportunity
Order
OrderItem
Product2
Quote
QuoteLineItem
ServiceContract
27. Einstein Prediction Builder can make predictions for the following types of fields.
• Checkbox
• Specially constructed formula fields
• Numeric
Supported Data Types for Target
Tips: Operations
29. o Models built by Admins and Scores consumed by end users, customers or colleagues
o Admins and Business Users who know their domain and data work as a team (it’s not a “build
me a model request”!)
o Quick/Simple to set up and easy to maintain
o Designed for a single Salesforce object per model
o Targetable to segments of the records for model building and model scoring
o Automatically score records daily
o Automatically the models refresh monthly
o Can be deployed and used in conjunction with Einstein Analytics Plus Stories models
Einstein Prediction Builder Models are:
Key Takeaways
30. Thank You
First Name Last Name
Title of Presenter
email@salesforce.com
@twitterhandle
Remember to tell us what you think in the event survey
www.LondonsCalling.net/survey/
32. The Details tab on the prediction scorecard shows a list of predictors for your model. It shows
information about each predictor, such as impact, correlation, and importance or weight. If your
predictive model contains more than 100 predictors, you might not see them all. The scorecard
shows you the top 100 predictors ranked by impact, and the top 100 ranked by correlation. If your
model has at least 100 predictors, the number displayed in the scorecard is likely to be between 100
and 200.
Limits
Operations
