The demo shows how to identify potential campaign memebers for a targeted marketing campaign. SFDC stores general information about customers/leads, and information about how they responded to previous campaigns. Those dataset are fed to AWS Machine Learning for prediction

1. Salesforce Campaign Members Prediction
with AWS Machine Learning

2. Overview : How to work with AWS Machine Learning
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DM Campaign to
Top Customers
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3
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Trigger AWS ML
1. Add members from Contact
/ Lead records
2. Once members are added to the campaign, Apex
trigger passes each member’s attributes to AWS ML
to get predictions, who are likely to respond the
campaign and who are not
3. The predictions via AWS ML are shown here

3. Architecture
Trigger
DM Campaign to
Top Customers
Completed campaign records
store each campaign
member’s status such as
sent or responded.
csv file
Report
Create the ML model based on csv file stored in
SFDC. The ML model is a collection of patterns
that Amazon ML finds in your data during training
Campaign members are added
and then the trigger is called to
execute callout request to AWS
API Gateway to get the
predictions Feed those data to
Machine Learning for
prediction

4. Prepare Training Data for AWS Machine Learning
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Campaign
Each campaign members
(Contact) has specific
attributes for ML such as
education, job category, and
so on
Once the campaign is completed, the
trigger converts campaign member’s
status (sent or responded) along with all
attributes on his contact/lead record into
AWS Training Datasources record. This
example generates three AWS Training
Datasource records because there are
three campaign members for this
campaign.
Same data
Target 1 : responded
Target 2 : sent
Contact
AWS Training Datasource

5. Upload Training Data source to S3
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1. In order to feed training
datasource to AWS
Machine Learning, save all
records as csv file via
report function
csv file
2. Upload csv file to S3
backet

6. Set up AWS Machine Learning and API
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1. Create ML Model from csv
file on S3 bucket
2. Review the ML Model’s
Predictive Performance
3. Create Lambda function to
call the ML model
4. Create Rest API to receive
parameters and pass them
to Lambda function in order
to get prediction based on
the ML Model
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7. Call the API from Salesforce
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Once campaign member is added
to a campaign, Apex calls the
prediction API with the member’s
attributes based on his contact
record.

8. Result
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The API returns Yes or No
which the member is likely
to respond the invitation and
purchase products.