This document discusses recommendations and discovery in Microsoft Dynamics 365 Commerce. It explains that recommendations are an important way for retailers to increase engagement and revenue using machine learning. It provides examples of different types of recommendations like "People Also Buy" and "Frequently Bought Together" that are powered by AI. It also shows how recommendations can be integrated and personalized in different parts of the commerce experience, including on product pages, carts, and in point of sale. Data is important for powering the intelligent recommendation systems.

1. Global AI
Bootcamp
14 Diciembre del 2019

2. AI
Recommendations
and Discoveryin
Dynamics365
Commerce
Juan Rafael
@jlc_rve

3. Microsoft
Dynamics 365
Common application platform: PowerApps, Microsoft Flow, Common Data Model
Microsoft AppSource
Dynamics 365

4. Agenda
Why Recommendations?
What Recommendations?
How Recommendations
(in Dynamics 365 Commerce)?
Demos
Q & A

6. Across the Industry
https://www.mytotalretail.com/article/4-ways-ai-is-transforming-e-commerce-businesses/​
https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers
Retailersare leaning stronger than ever towardsRecommendationsand Machine Learning intelligentsystems to drive
more consumer engagement and higher revenue.

8. Microsoft’s Approach
Present shoppers with great choices for a satisfying experience
that increases revenue, increases repeat visits,
and grows average order value​s for Retailers

9. Recommendations in action in Microsoft
Landing Page Product Details Page Cart / Checkout
User behavior is used to drive relevant discovery. Includingcross-selland upsell, in any UX context.

10. World Class AI-ML Research Team

12. Omni-Channel Recommendations Lists
• “People also buy” AI-ML list based on transactions
• “Frequently bought together” AI-ML list based on orders
• “Picks for you” AI-ML list based on transactions
• “New” chart based on assorted date field
• “Best Selling” chart based on transactions
• “Trending” chart based on transactions and assorted date field

13. People Also Buy
Machine Learned list using
consumers transactions to
show recommendations in the
context of a single product.
Driving comparison shopping
and increase in consumer
engagement.

14. Frequently Bought Together
Machine Learned list using
consumers multi-item orders to
provide recommendations in
the context of the current
consumer’s cart.
Driving increase in average
order value.

15. Picks For You
Machine Learned list based on
consumers transactions provides
personalized recommendations
for the signed in consumer.
GDPR compliant, including
support for consumer opt out.
Model allows for personalization
of any Chart and ML list.

16. New
Chart shows all newly
available products in an
environment, ordered from
most to least recent.
Driving novel discovery.

17. Bestselling
Chart shows the all-time
best-selling products in an
environment, ordered from
most to least performing.
Driving discovery via popular
content.

18. Trending
Chart shows the best-selling
products in a recent period,
ordered from most to least
performing.
Driving discovery with
in-vogue content.

19. Additional Capabilities
• Respects Assortments, Channel, Category,
and Catalog out of the box
• Customers can adjust
algorithms’ configuration
• Support for Algo / ML List
manual modifiers

20. Seamlessly integrated in Dynamics 365 HQ
• Enable / Disable
Product Recommendations
• Enable / Disable
Personalized Recommendations
• Create and manage
Editorial lists

21. Expressive User Experience in E-commerce
• In E-commerce:
• Discovery Pages
• Product Display Page
• Cart Pages

22. Expressive User Experience in E-commerce
• In E-commerce:
• Discovery Pages
• Product Display Page
• Cart Pages

23. Expressive User Experience in E-commerce
• In E-commerce:
• Discovery Pages
• Product Display Page
• Cart Pages

24. Bonus Examples
• Newest Hats → New Chart with applied Category
• Bestselling Menswear for You → Personalized Bestselling Chart with
applied Category
• Like 2014 Col Solare Cabernet Sauvignon wine → People Also Buy with
specified seed product

25. Compelling Recommendations in Point of Sale
• In Point of Sale:
• Product Details Screen
• Transactions Screen
• Customer Details Screen

26. Compelling Recommendations in Point of Sale
• In Point of Sale:
• Product Details Screen
• Transactions Screen
• Customer Details Screen

27. Compelling Recommendations in Point of Sale
• In Point of Sale:
• Product Details Screen
• Transactions Screen
• Customer Details
Screen

28. Importance of Data
“People also buy” AI-ML list based on transactions
“Frequently bought together” AI-ML list based on orders
“Picks for you” AI-ML list based on transactions
“New” chart based on assorted date field
“Best Selling” chart based on transactions
“Trending” chart based on transactions and assorted date field

29. Dynamics 365 CommercePreview

30. Everything to buildand run digital commerce

31. Build loyalty and exceed customer expectations

32. Scalable, reliable, secure, and compliant

52. Links
https://dynamics.microsoft.com/en-us/commerce/overview/
https://docs.microsoft.com/en-us/dynamics365/retail/index#dynamics-365-
commerce-preview
https://cloudblogs.microsoft.com/dynamics365/bdm/2019/10/07/announcing
-dynamics-365-commerce-preview/
https://www.meetup.com/es-ES/SQL-PASS-LIMA/