The document provides a comprehensive guide on using the Einstein Prediction Builder for machine learning in Salesforce, specifically targeted at Salesforce admins and data experts. It outlines the steps for defining a use case, creating predictions for numerical and binary outcomes, and evaluating the accuracy of predictions, utilizing a practical example of predicting medical appointment no-shows. Additionally, it details how to act on predictions by implementing strategies for the next best action and encourages further learning about Einstein resources.

1. Drum into understanding of prediction builder with NBA
Hands-on Workshop: Step-by-Step Guide
Rajat Jain, Program Specialist @ MTX Group Inc.
rajat.jain@mtxb2b.com
@rajat_307

2. What is Machine Learning?
Machine Learning is using past data to predict the future
Computer algorithms find patterns in the historical data to
apply to new data and make predictions
Historical Data
Your historical records
For example, opportunities won/lost
in the last 12 months
New Data
Records to Predict
For example, new opportunities

3. What is it?
A wizard where you can define a machine
learning problem for the Einstein Platform to
solve
Who is it for?
Salesforce admins, in conjunction with with
their data experts
What is the value?
Easy to create predictions, iterate on
them and use predictions in production
Einstein Prediction Builder
Point, Click, Predict

4. Subtitle placeholder
What Einstein Builder Predicts For
Numeric Predictions
Leverage historical information to
predict a number
Binary Predictions
Leverage historical information to
answer a Yes/No question
Scores Returned:
● $400,000
● 30 days
● $1,200,000 (welcome to the Bay Area..)
Scores Returned:
● The likelihood a record churns
● The likelihood a lead converts
● The likelihood a student graduates
Example Questions:
● Is this customer going to churn?
● Is this lead going to convert?
● Will this student graduate on time?
Example Questions:
● How much is this deal likely worth?
● How long will this deal take to close?
● How much will this house sell for?

5. 1. Define
your use
case
2. Plan
your
prediction
3. Fill out
the wizard
4. Review
your
scorecard
5. Enable
your
prediction
6. Use
model in
production
Einstein Prediction Builder Lifecycle

6. Q: What is the problem your company is facing?
Example: Only 60% of Medical Appointment shows up on time. How do I increase rate to get
patient to show on time for appointment?
Q: What type of prediction will help?
Example: Predict which Medical Appointment are likely to be No Show.
Q: Can this prediction be phrased as a yes/no or numeric question?
Example: Will this prediction be No Show?
How to Define Your Use Case

7. Q: How will I use this prediction?
Example: Call/SMS Patient in advance if they are going to be No Show.
Q: How do you measure if the prediction is successful?
Example: Percentage decrease in Patient No Show.
How to Define Your Use Case (cont.)

8. Plan Your Prediction

9. Building a Yes/No Prediction
-
+
-+ - ?
Example set (Training set)
Minimum 400 Records Required
Records to predict (Scoring set)
?

10. Will an Medical Appointment be a No Show?
Dataset
Segment
Positive
examples
Negative
examples
Prediction
Set
Prediction Set:
Records that have scheduled
Appointment but which is in future
Object:
Medical Appointment
Segment:
N/A
Positive examples:
Patient Records which did not shows up on time
Negative Examples:
Patient Records which shows up on time

11. Predicting Appointment No Shows
● Set up → Prediction Builder
● Click “New Prediction”
Create a Prediction Choose an Object and Type of
Prediction
Records to Learn from and
Records to Predict
Prediction Inputs and Output
● Give your prediction a name
● Click “Next”

12. Predicting Appointment No Shows
● Select “Medical Appointment__c”
● Click “Data Checker”
○Ensure you have enough records
in this object to build a prediction
Create a Prediction Choose an Object and Type of
Prediction
Records to Learn from and
Records to Predict
Prediction Inputs and Output
● The answer to “Will this appointment
be a No Show?“ is Yes/No

13. Predicting Appointment No Shows
● Field to Predict = “No Show”
● Appointments to learn from (e.g.
training examples) ~ 7800
○ Set condition to Appointments on or before
May 31, 2016
● Appointments to predict ~ 2200
○ Appointments after May 31, 2016
Create a Prediction Choose an Object and Type of
Prediction
Records to Learn from and
Records to Predict
Prediction Inputs and Output
Best Practice Tip:
● Always use Data Checker to confirm:
○ You have enough records to learn from
(training set).
○ You have enough positive and negative
examples in your training set
○ You know how many records will have a
prediction (records to predict)

14. Predicting Appointment No Shows
● Select fields in the appointment record
that should be used as input to predict
○Use as many as possible
● Click “Next”
Create a Prediction Choose an Object and Type of
Prediction
Records to Learn from and
Records to Predict
Prediction Inputs and Output
● Name the custom field that store your
prediction.
○ Note that the prediction will be a
number between 0-100 representing
the likelihood of “No Show”
● Click “Next”
● Click “Build Prediction”

15. Evaluating Prediction Accuracy
How good was that predictive model?
Use EPB Model Accuracy Package

16. Evaluating Prediction Accuracy
● Click on the “App Launcher”
● Click on “Analytics Studio”
Launch EPB Model
Accuracy
Complete the Wizard View Prediction Accuracy
● Click on “Create App”

17. Evaluating Prediction Accuracy
● Click on “Create App from Template'”
Launch EPB Model
Accuracy
Complete the Wizard View Prediction Accuracy
● Select “EPB Model Accuracy”
● Click “Continue”
● Click “Continue” again

18. Evaluating Prediction Accuracy
Fill in the following details about your
prediction
Fill in the following details about your
prediction
● Object = Medical Appointments 2
● Field containing actuals = No Show
Launch EPB Model
Accuracy
Complete the Wizard View Prediction Accuracy
Choose two business dimensions to
analyze your prediction accuracy. For
example you could:
● Choose “Age Bracket” and “Gender”
● Click “Looks good, next”

19. Evaluating Prediction Accuracy
Provide suitable labels:
● A high score (100) means ...No Show
● A low score (0) means...Show
● Click Next
● Click Next
Launch EPB Model
Accuracy
Complete the Wizard View Prediction Accuracy
Provide a Name for your App
● Click Create

20. Evaluating Prediction Accuracy
Wait for 2-3 minutes until you see
“Application Complete”
● Refresh your browser page
● Open the Binary Classification
Performance dashboard“
Launch EPB Model
Accuracy
Complete the Wizard View Prediction Accuracy
Switch to the confusion matrix view
● Set different thresholds and observe
the impact on Accuracy, False
Positives, False Negatives
● Discuss - Would you lower the
threshold to reduce overall accuracy
while minimizing false negatives?

21. Acting on your Prediction
Next Best Action

22. Use Prediction to Recommend the Best Course of Action
● Go to Setup
● Type “Next best ”
● Click on “Next Best Action”
● Click on “New Strategy”
Launch Next Best Action Craft a Strategy Display your Recommendation
● Name = “No Show Strategy”
● Object where Recommendations
Display = “Medical Appointment 2”
● Click “Create”

23. Use Prediction to Recommend the Best Course of Action
● Drag 2 Load Nodes on the strategy
builder
○ Node 1: Name “Call Patient” &
Description contains “Call”
○ Node 2: Name “SMS Patient” &
Description contains “SMS”
Launch Next Best Action Craft a Strategy Display your Recommendation
● Drag a “Branch Selector”
○ Label: “Predicted No Show?”
● Re-arrange nodes as shown

24. Use Prediction to Recommend the Best Course of Action
● Branch 1
○ No Show Score >= 18
Launch Next Best Action Craft a Strategy Display your Recommendation
● Branch 2
○ No Show Score < 18

25. Use Prediction to Recommend the Best Course of Action
○ Open a Medical Appointment 2
record
○ Click “Edit Page”
Launch Next Best Action Craft a Strategy Display your Recommendation
● Drag the Einstein Next Best Action to
the right pane
● Confirm your strategy is selected
● Now
○ Click “Save”
○ Click “Activate”
○ Click “Assign as Org Default”
○ Select “Desktop”.
○ Click “Next” & “Save”
● Click “Back”

26. Use Prediction to Recommend the Best Course of Action
Your Recommendation is displayed
Launch Next Best Action Craft a Strategy Display your Recommendation

27. Continue Learning About Einstein!
Check out content on the
Einstein Hub
Learn about the Einstein
Champions Program
Join the Einstein Trailblazer
Community Group
einstein-hub.com sfdc.co/einsteinchampions sfdc.co/einsteingroup

28. Questions?

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