1. 9/14/ :46 AM
BRK3293
How the Portland Trail Blazers Use Personalization and Acxiom Data to Target Customers
Chris Hoder
Program Manager, AI + Research
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2. Challenges with Personalization:
9/14/ :46 AM
Challenges with Personalization:
Data collection and integration
Modeling
Deep insights about customers
© 2014 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

3. Portland Trail Blazers
Microsoft 2016
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Portland Trail Blazers
Use Case 1:
25% call conversion rate for new season ticket sales
Use Case 2:
2x improvement in identifying returning single game ticket buyers
“Reaching and engaging fans is a priority for the Trail Blazers, and our work with Azure Machine Learning has delivered real results”
– Mike Schumacher,
Director of Business Analytics
© 2016 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

4. Trail Blazers Use Cases
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Trail Blazers Use Cases
New fans
Fans stop buying tickets
Single game ticket buyers
Dedicated Fans
Use Case 1
Season Tickets
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

5. Trail Blazers Use Cases
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Trail Blazers Use Cases
New fans
Fans stop buying tickets
Use Case 2
Single game ticket buyers
Dedicated Fans
Season Tickets
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6. Use Case 1: Improving Season Ticket Sales
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Use Case 1: Improving Season Ticket Sales
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

7. 14 4% A TYPICAL SALES CAMPAIGN LEADS TO ONLY CONVERSION RATE CURENT
CAMPAIGNS
ARE BASED ON
HISTORICAL
INSIGHTS
INSTEAD OF
PREDICTIVE
TARGETING...
OPPORTUNITIES & CHALLENGES
A TYPICAL
SALES
CAMPAIGN
LEADS TO ONLY
CONVERSION RATE 4%
AVERAGE TENURE OF PACKAGE HOLDERS IS OVER YEARS 14

8. 24.8% A PREDICTIVE MODEL WAS BUILT TO IDENTIFY CUSTOMERS MOST
SOLUTIONS & IMPACT
A PREDICTIVE
MODEL WAS
BUILT TO IDENTIFY
CUSTOMERS MOST
LIKELY CHANGE SEASON TICKET PURCHASE PLAN
USING THE MODEL IT TAKES JUST 16% OF THE NUMBER OF LEADS TO REACH THE SAME NUMBER OF CONVERSIONS
CONVERSION RATE
OF LEADS IDENTIFIED
WAS
24.8%

9. Demo – Azure Machine Learning
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Demo – Azure Machine Learning
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10. Use Case 2: Single Game Purchase Propensity
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11. Opportunities & Challenges
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Opportunities & Challenges
16% fans return from the prior year
Want to drive increased fan loyalty
Limited Info about customer preferences
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. Our Solution Integrated with Acxiom data 50% boost in model recall
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Our Solution
Integrated with Acxiom data
50% boost in model recall
In testing, 30% of leads purchased tickets
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

13. Data Modelling Deep-Dive
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

14. Binary Classification
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Approach
Profile Customer
From Trail Blazers
Behavioral Patterns
Demographic information
CRM & Sales information
Team preferences
From Acxiom
Financial, spending & product purchasing information
Interests & propensity
Lifestyle
Binary Classification
Is Retaining?
Target a high recall
Cost on FPs is low
Try Various Features
Feature selection
Derived features
Data transformation
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

15. Work Flow Azure SQL Database Azure Machine Learning Azure Blob Storage
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Work Flow
Demographic
Sales Records
Attendance
Extract data fields
Generate new data
Labeling data
Features
Label
Acxiom Data
Feature selection
Prediction Model
Model Training
Azure SQL Database
Azure Blob Storage
Azure Machine Learning
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

16. Importance of Acxiom Data
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Importance of Acxiom Data
Trail Blazers Data Only
Trail Blazers & Acxiom Data
Top 15% customers
Top 15% customers
0.276
0.520
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

17. Only Use Trail Blazers Data Integrate Trail Blazers & Acxiom Data
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Model Performance
Only Use Trail Blazers Data
Integrate Trail Blazers & Acxiom Data
Improvements with Acxiom Data
Better overall Recall value
(49.4% improvement) 2
Increased F1 Score
(19.0% improvement) 3 1
Captured more TPs
(49.6% improvement)
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

18. Machine Learning, Analytics & Data Science Conference
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Insights & Learnings
Learnings
Azure ML provided tooling for data
manipulation and model building
User’s consumption data, interests,
and lifestyle played a significant role
Integrating Acxiom data provided a
significant boost to model accuracy
Use Data + ML to know your
customers better and drive revenue
Next Steps
Run a live marketing campaign using leads generated
Automate the pipeline for regular lead generation
Expand analytics to new use cases throughout the Blazers
© 2014 Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

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Tech Ready 15
9/14/2018
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© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

20. 9/14/ :46 AM
© Microsoft Corporation. All rights reserved. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.