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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

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Paul Pellizzari, Sheona Hurd - Data-Driven Responsible Gambling

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