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Paul Pellizzari, Sheona Hurd - Data-Driven Responsible Gambling

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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.

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 Gambling with Data Analytics New Horizons Conference Vancouver, January 2013
  2. 2. 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
  3. 3. 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%
  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 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+
  5. 5. 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
  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 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
  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 Pellizzari ppellizzari@olg.ca Sheona Hurd shurd@olg.ca

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