GIS in the Gaming Industry – Modernizing Analytics at
the OLG
Brad McCallum
Sept, 23, 2016
Organization Overview
•
Ontario Lottery and Gaming Corporation (OLG) is a Crown
Corporation of the Government of Ontario. It is responsible for the
province's lotteries, charity and Aboriginal casinos, commercial casinos,
and slot machines at horse-racing tracks.
•
OLG employs over 8000 individuals throughout Ontario.
•
Gaming HQ in Sault Ste. Marie & North York
•
Esri user since…2010
The Analytics Centre of Excellence (ACE)
•
Provide technical and operational expertise to improve the delivery of
actionable information for all facets of Gaming and across the
organization.
Operations
Finance
Marketing
I.T.
Analytics Centre of Excellence
ACE Clients
OPERATIONS
MARKETING
CUSTOMER
MANAGEMENT
Primary Functions
Dashboard & Scorecard Reporting
Business Planning & Ad Hoc Analysis
Customer Analytics
Predictive Modelling & Forecasting
Spatial Analytics
RESPONSIBLE
GAMING
MODERNIZATION
Examples
•
Example 1 – Providing aggregated customer reports
•
Example 2 – Introduce Location Analytics to Marketing
Example 1 - Customer Reporting
•
OLG modernization process
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Provide aggregated customer data
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Reports grouped by Geography
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Grouping based on 3 different layers
•
Geocoding, Corporate Data and Model Builder
•
No Budget, Tight deadlines, Re-constructing previous work
•
~1 week to build, test, improve, rebuild, simplify, rebuild, etc…
First Report required GIS
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Report by Administrative Census
boundary
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Next 5 reports used the same data
Complete Running model
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First iteration used 5 separate models and 19 different data files
•
Current Version uses 1 table and 1 model.
Data makes data…
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One set of reports needed extra calculations
•
Separate model modifies original output tables
Clean up…
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Created a model to clean up all the
unnecessary files
Benefits
•
•
How did GIS help you improve your services/achieve your
goals?
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Used Geocoding to ‘standardize’ data
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Used spatial joins and selection by location
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Model builder to do the grunt work
What were able to achieve?
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Reduce processing from ~1 day per site to creating 6 reports for all 19
sites in under 2 hours
-
Consistent repeatable output
-
Flexible, Documented
Lesson Learned
•
What would you do differently?
-
•
Would Learn more Python
Positive recommendations for others?
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Learn and use model builder
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Iterate, iterate, iterate
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Don’t change RAW data or structure, if possible
Future Plans
•
•
What are the next steps for your project?
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Reuse for other data sources
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Optimize, tweak, improve
How do you think your organization will leverage this
achievement?
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Reusable, repeatable, documented
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Provide high quality data and reporting to end users
Example 2 - Trade area creation and reporting
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Trade area delineation using customers
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Provide Marketing with demographic reports for individual
markets
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Built Customer Defined Trade Areas, convert to Data Driven,
non-overlapping rings.
•
Teamed with Marketing leads to educate, refine and customize
demographic reports
•
Business Analyst, Model builder & some hacking
1) BA’s Geocoder
2) BA’s Customer Derived Trade Area
3) BA’s Data driven ring tool.
4) Identify areas where CDTAs overlap
5) BA’s Remove trade area overlap
6) Create reports using
non-overlapping trade
areas
Benefits
•
How did GIS help you improve your services/achieve your
goals?
-
Marketing has better understanding of their markets - both
spatially and from the Census data
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Visualize market based on customer locations
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Expose Marketing to Spatial Analytics and Census data
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Cross functional Team building
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Larger GIS knowledge footprint.
Lesson Learned
•
•
What would you do differently?
-
More upfront education
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Get feedback on parameters, assumptions
Positive recommendations for others?
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Spatial data or analytics not well understood
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Don’t forget about Projections! & Geodesic Distances
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Overlap gets counted in reports
Future Plans
•
•
What are the next steps for your project?
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Standardize Trade areas for other spatial analytics (Huff model)
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Online gaming use
How do you think your organization will leverage this
achievement?
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Bring knowledge of our customer base to other teams
-
Continue to leverage trade areas for marketing purposes
Conclusions
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OLG is committed to spatial analytics and Esri
•
Spatial analytics is improving our knowledge of our customers
•
Education, knowledge transfer, basic understanding of spatial
data and analytics still challenging
•
Part of our analytics stack and embedded in our analytics
culture
•
Increases efficiencies
•
Using it, growing it…just scratching the surface
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Thank-you
[email protected]