Driving Responsible Gambling
with Data Analytics
New Horizons Conference
Vancouver, January 2013

Overview
1. RG Data Analytics Today
• Database Management & Business Intelligence
• Player Education
2. Game Change
• Embed RG into player experience
• How RG Data can impact behaviour
3. Why & how OLG will manage player data
4. Collaboration drives Innovation

1.0 RG Data Analytics – Today
Database Management & Business Intelligence
“Red-Flag” Interaction Report: Informs policy, employee training and
reinforcement content; available to researchers and clinicians.
Breakdown of Red Flag Behaviours - Q2 F13 Breakdown of Action Taken - Q2 F13
Assistance Requested for Family Member/Friend Comments about Overspending/Losses Suggest Break Followed Fatigue Impairment Policy
Crying, Aggressive, Angry Problem Gaming Disclosure Direct to RGRC Security Involvement
Threat to Property/Staff/Customers Extended Play/Observable Exhaustion Direct to KnowYourLimit.ca Verbal Explanation of How Games Work
Comments about Myths Other Provide RG/PG Information Brochure Provide Problem Gambling Helpline
Sleeping Escalate to Sr. Manager No Action Taken
1%
2% 5%
5%
19% 4%
25% 29%
7%
20% 5%
16%
29%
11%
2% 8% 6%
6%

1.1 RG Data Analytics – Today
Database Management & Business Intelligence
“Self Exclusion” Database Report: Inform policy, programming,
operational functions; available to researchers and clinicians.
Q3 F13 Self Exclusion Registrations and Reinstatements
Self Exclusion Registrations -
800
Q3 F13
700 Unknown
0%
672
600
Number of Patrons
Female
500
42%
400 446 Male
58%
300
200
100
0
SE: Registration SE: Reinstatement
Self Exclusion Reinstatements -
Q3 F13
Self Exclusion Registrations by Age Group and Gender - Q3 F13 Unknown
120 1%
106
100
100 89
Female
76 40%
80
61 63
Male Male
60 50
41 Female 59%
40 Unknown
28
22
17 17
20
0 0 0 1 1 0
0
19-30 31-40 41-50 51-60 61-70 70+

1.2 RG Data Analytics – Player Education
Player Survey & Market Research
Surveys and market research: Inform policy, educational content and
channels
•32% of frequent players
think your chances of
winning slots are better at
certain times of day
•17% of frequent players
DO NOT think game
outcome is random
•45% of infrequent players
think a slot machine that
hasn’t had a jackpot in
awhile is due for a win

1.3 RG Data Analytics – Player Education
Loyalty Card Data: Can help to inform effectiveness
of RG initiatives on player behaviour
RG Kiosk Participants Average of all Particpants with
Pre and Post Promo Play
Days Played -5.0%
Avg Visit Duration -1.7%
Avg Session Count -0.8%
Avg Coin In $ 0.2%
Avg House Net Win $ 6.1%
Avg Handle Pulls -0.7%

2.0 Game Change - Embed RG into player experience
Operators must integrate safe play habits and build data
analytics into the core of player experience.
•Inform polices: e.g.
•Account-based play Marketing, RG
•Risk assessment Interactions with
algorithms Players
•Limit setting tools •Enable personalized,
•Self Assessments direct communication
to players

2.1 Game Change - How RG Data can impact behaviour
Informed Choice:
• Based on individual’s
actual behaviour
• Tells story, builds a
profile over time
• Can better affect player
behaviour and minimize
harm

3. Context – Why & how OLG will manage player data
RG core to business strategy
• Sustainable player base
• As strategic driver, RG needs an analytical framework
Conduct and Manage
• Criminal Code of Canada (section 207) requirement
• OLG approach is to manage customer data
• One view of the customer across lines of business
• Analysis for strategic decision-making, including RG
Risks:
• Failure to implement properly

4. Collaboration drives Innovation
• OLG to share anonymous data sets with researchers and
clinicians
• Expand industry-wide knowledge
• Evolve and complement “self reporting” with actual
player behaviour

Paul Pellizzari
ppellizzari@olg.ca
Sheona Hurd
shurd@olg.ca