paul pellizzari, sheona hurd - data-driven responsible gambling
DESCRIPTION
Paul Pellizzari & Sheona Hurd's presentation on "Data-Driven Responsible Gambling". Presented at the New Horizons in Responsible Gambling conference. January 28-30, 2013 in Vancouver, BC.TRANSCRIPT
Driving Responsible Gambling with Data Analytics New Horizons Conference Vancouver, January 2013
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
Overview
Database Management & Business Intelligence
1.0 RG Data Analytics – Today
“Red-Flag” Interaction Report: Informs policy, employee training and reinforcement content; available to researchers and clinicians.
2%
19%
29%
6%2%
16%
25%
1%
Breakdown of Red Flag Behaviours - Q2 F13
Assistance Requested for Family Member/Friend Comments about Overspending/Losses
Crying, Aggressive, Angry Problem Gaming Disclosure
Threat to Property/Staff/Customers Extended Play/Observable Exhaustion
Comments about Myths Other
Sleeping
29%
5%
11%
6%8%
20%
7%
4%
5%5%
Breakdown of Action Taken - Q2 F13
Suggest Break Followed Fatigue Impairment Policy
Direct to RGRC Security Involvement
Direct to KnowYourLimit.ca Verbal Explanation of How Games Work
Provide RG/PG Information Brochure Provide Problem Gambling Helpline
Escalate to Sr. Manager No Action Taken
Database Management & Business Intelligence
1.1 RG Data Analytics – Today
“Self Exclusion” Database Report: Inform policy, programming, operational functions; available to researchers and clinicians.
672
446
0
100
200
300
400
500
600
700
800
SE: Registration SE: Reinstatement
Nu
mb
er
of
Pat
ron
s
Q3 F13 Self Exclusion Registrations and Reinstatements
89
76
106
63
41
1717
28
61
100
50
22
0 0 0 1 1 00
20
40
60
80
100
120
19-30 31-40 41-50 51-60 61-70 70+
Self Exclusion Registrations by Age Group and Gender - Q3 F13
Male
Female
Unknown
Male59%
Female40%
Unknown1%
Self Exclusion Reinstatements -Q3 F13
Male58%
Female42%
Unknown0%
Self Exclusion Registrations -Q3 F13
Player Survey & Market Research
1.2 RG Data Analytics – Player Education
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
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%
RG Kiosk Participants
Operators must integrate safe play habits and build data analytics into the core of player experience.
2.0 Game Change - Embed RG into player experience
•Account-based play •Risk assessment
algorithms •Limit setting tools •Self Assessments
• Inform polices: e.g. Marketing, RG Interactions with Players
•Enable personalized, direct communication to players
Informed Choice:
• Based on individual’s actual behaviour
• Tells story, builds a profile over time
• Can better affect player behaviour and minimize harm
2.1 Game Change - How RG Data can impact behaviour
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
3. Context – Why & how OLG will manage player data
• OLG to share anonymous data sets with researchers and clinicians
• Expand industry-wide knowledge
• Evolve and complement “self reporting” with actual player behaviour
4. Collaboration drives Innovation
