The document discusses how to use the Einstein Intent API to build predictive models for upselling and cross-selling products by analyzing historical customer purchase data to identify relationships between core and add-on products, and then training a natural language processing model on this data to generate recommendations for new customers based on their initial purchases. It provides an overview of data preparation, model training and evaluation, and demonstrates implementing predictions from a Salesforce CPQ configuration using code.

1. Blaze a Trail to Predictive
Selling with Einstein Intent

2. Daniel Peter
Salesforce MVP
Lead Applications Engineer,
Kenandy
22x Salesforce Certified
twitter: @danieljpeter
instagram: @realdanpeter
email: dan@danpeter.com

3. Einstein Intent API
A Natural Language Processing classification service
Marketed primarily for lead routing and case
routing.
But could it be used to predict other things?
Sure!

7. Use Einstein Intent on real customer data!

8. Tent products

9. Add ons

10. You bought a Mastertent, you should consider buying footplates.
Recommendations

11. How do you know what people might want before they do?
historical data + algorithm = predict the future!
model
Tent, Sidewall => Lighting
Inflatable Tent => Flag
Pavilion, Truss => Wind Dancer
Lighting
Training Data
(independent => dependent)
independent
to be predicted
predicted
dependent

12. Einstein Intent API
A Natural Language Processing classification service
Examples Labels
10x15 MASTERTENT Transit Bag 66lb Footplates Wheel Set MASTERTENT Stake Kit 4 Stakes
10x10 MASTERTENT Transit Bag 6 PowerStretch Table Cover MASTERTENT Stake Kit 4 Stakes
10x15 MASTERTENT Transit Bag Sandbag MASTERTENT Stake Kit 4 Stakes
Feather Flag Large Double Sided Same Art Large Flag Pole Hardware w Carry Bag Square
Water Bag
Cross Base
Feather Flag Small Double Sided Same Art GYBE 4000 13 Wall Double Sided Same Art
Groundspike Gybe Counter w Digital Print Scoprega Rechargeable Gybe Pump Small Flag Pole
Hardware w Carry Bag Square Water Bag
Cross Base
6 Table Cover Feather Flag Medium Double Sided Same Art Groundspike Medium Flag Pole
Hardware w Carry Bag
Cross Base
10x10 MASTERTENT Transit Bag MASTERTENT LED Lighting Kit MASTERTENT Stake Kit 4
Stakes Wind Dancer LT Flag Double Sided Wind Dancer LT Hardware
66lb Footplates
10x10 MASTERTENT Transit Bag MASTERTENT Stake Kit 4 Stakes Peak Flag Double Sided
Same Art Peak Flag Hardware 10 Series Raingutter 10 x 10 Tent Connecting Clamps Wheel
Set
66lb Footplates
13x20 MASTERTENT Transit Bag MASTERTENT Stake Kit 4 Stakes Wheel Set 66lb Footplates

13. How to train the model with real data
Separate items into core and add-on.
Find items ordered together. These products create a bag of words
which we use as our independent variable.
Clean data: Special case customers, products which aren’t products,
product with insufficient examples, duplicate examples.
Data Prep

14. Predicting from within Salesforce CPQ

15. Predicting from within Salesforce CPQ

16. Predicting from within Salesforce CPQ

17. Architecture
TRAINING
PREDICTING

18. Fuzzier than using an item id primary key. This means new products which aren’t in the model
can still work.
Can use any words that might be good predictors — Such as customer demographics, season.
Data Prep is your job, and very important!
Using Natural Language Processing
for product recommendations

19. Code Demo
Generating JSON file from Salesforce data and
submitting it to Einstein Intent to train the model.
Sending in the bag of words from a CPQ quote to
get a prediction for an upsell.

20. Interpreting model accuracy metrics
Overall Model Percentage Accuracy Train / Test (0-100%)
Test is the important one.
F1 score: accuracy of the label (0-1)
Confusion Matrix: granular strengths and weakness of the model
NEW! Learning Curve

21. Confusion Matrix
(source:
wikipedia)

23. Resources
Einstein Intent API Basics on Trailhead
https://trailhead.salesforce.com/en/modules/einstein_intent_basics
Main Einstein Site (signup, docs, more)
https://einstein.ai/
Github repo with the code from this demo
https://github.com/danieljpeter/DF17IntentDemo