Driving Responsible Gamblingwith Data AnalyticsNew Horizons ConferenceVancouver, January 2013
Overview1. RG Data Analytics Today• Database Management & Business Intelligence• Player Education2. Game Change• Embed RG ...
1.0 RG Data Analytics – TodayDatabase Management & Business Intelligence“Red-Flag” Interaction Report: Informs policy, emp...
1.1 RG Data Analytics – Today      Database Management & Business Intelligence“Self Exclusion” Database Report: Inform pol...
1.2 RG Data Analytics – Player EducationPlayer Survey & Market ResearchSurveys and market research: Inform policy, educati...
1.3 RG Data Analytics – Player Education Loyalty Card Data: Can help to inform effectiveness of RG initiatives on player b...
2.0 Game Change - Embed RG into player experience Operators must integrate safe play habits and build data analytics into ...
2.1 Game Change - How RG Data can impact behaviour    Informed Choice:  • Based on individual’s    actual behaviour  • Tel...
3. Context – Why & how OLG will manage player dataRG core to business strategy• Sustainable player base• As strategic driv...
4. Collaboration drives Innovation• OLG to share anonymous data sets with researchers and  clinicians• Expand industry-wid...
Paul Pellizzarippellizzari@olg.caSheona Hurdshurd@olg.ca
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Paul Pellizzari, Sheona Hurd - Data-Driven Responsible Gambling

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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.

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Paul Pellizzari, Sheona Hurd - Data-Driven Responsible Gambling

  1. 1. Driving Responsible Gamblingwith Data AnalyticsNew Horizons ConferenceVancouver, January 2013
  2. 2. Overview1. RG Data Analytics Today• Database Management & Business Intelligence• Player Education2. Game Change• Embed RG into player experience• How RG Data can impact behaviour3. Why & how OLG will manage player data4. Collaboration drives Innovation
  3. 3. 1.0 RG Data Analytics – TodayDatabase Management & Business Intelligence“Red-Flag” Interaction Report: Informs policy, employee training andreinforcement 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%
  4. 4. 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 Unknown120 1% 106 100100 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+
  5. 5. 1.2 RG Data Analytics – Player EducationPlayer Survey & Market ResearchSurveys and market research: Inform policy, educational content andchannels•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
  6. 6. 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%
  7. 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
  8. 8. 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
  9. 9. 3. Context – Why & how OLG will manage player dataRG core to business strategy• Sustainable player base• As strategic driver, RG needs an analytical frameworkConduct 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 RGRisks:• Failure to implement properly
  10. 10. 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
  11. 11. Paul Pellizzarippellizzari@olg.caSheona Hurdshurd@olg.ca

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