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Ryan Persaud
This document discusses research conducted by BCLC to better understand players' perspectives on responsible gambling. It shares insights from focus groups on BCLC's GameSense responsible gambling brand and segments players based on their responsible gambling behaviors and attitudes. The segmentation identified five player types ranging from those who are highly involved in gambling but acknowledge responsible gambling to those with low gambling exposure and involvement. The research aims to help BCLC integrate responsible gambling insights into its business strategy and further its player understanding.
Ryan Persaud
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- 2. BCLC’sEnterprise Business IntelligenceLandscape Player Data eGaming Loyalty Programs Economic / Media Monitoring Secondary Data External Research Primary Data Surveys 1:1/Ethnos Focus Groups Transactional Data Machines Point of Sale $Data Landscape
- 3. Player Insights & Understanding These insightssupport business decisions RG related research adds new dimension to player insights & understanding… PlayerInsights& Understanding
- 4. GameSense Brand PlatformRedesign • Brand Evolution: Tone, Connection & Relevancy RG Player Segmentation • Targeted Communication & Programming
- 5. Research Objectives &Methodology: Understand players’ perceptions of responsible gambling and expectations of a responsible gambling program Explore perceptions of GameSense, its messages and how it relates to BCLC Provide feedback on changes needed to the existing GameSense brand (essence, personality, positioning, voice, aesthetic, etc.) 5 x 2 hr focus groups, range of player types (facility, products, etc.) GameSenseBrand Insights Key Insights: High awareness & comfort Brand Pillars: Relevant Presence, Think Experience, Be Real Brand Spectrum: From Fun… to Responsible... to Problem… Where is GameSense perceived? For “those People”: “GameSense is important for those people who have a problem” The RG & PG Blur: Is there a difference? Authoritative/ Clinical tone: Functionally perceived, slightly condescending Reactive vs. Proactive
- 6. GameSense Brand PlatformRedesign - Brand Evolution: Tone, Connection & Relevancy RG Player Segmentation - Targeted Communication & Programming
- 7. Research Objectives &Methodology: Phase 1: Exploratory (Qual) • 6 Focus Groups: Light/Moderate players, range of activities, excluded moderate to high risk/problem gamblers on CPGI Classification • 56 Journal Surveys: Frequent players, range of activities, excluded moderate to high risk/problem gamblers on CPGI Classification • 8 1:1s with Journal participants RG PlayerSegmentation Phase 2: Segmentation (Quant) • 2,706 interviews with 19+ British Columbians, including 2,290 past year BCLC product gamblers (85% of adults). 25 min online survey. • Data weighted to be reflective of all British Columbia adults by age, gender and region. To gain a deeper understanding of players from a responsible gambling perspective (definition, awareness, knowledge, and responsible gambling behaviour)
- 8. What's unique aboutthis segmentation? RG PlayerSegmentation It goes beyond demographics or games played – it’s diverse and includes RG related dimensions Gambling motivations Reasons for spontaneous gambling Frequency of responsible gambling behaviours Gambling activity participation Past year deviations Importance of responsible gambling motivations
- 9. 27% 28% 17%17% 10% RG PlayerSegmentation Highly Driven, RG Deniers Positive Play Modelers Highly Involved, Positive Play Acknowledgers Lotto & RG Receptive Low Exposure, Low Involvement
- 10. The ‘now what?’ Challengeourselves to integrate RG insights into BCLC’s harm reduction strategy, as well as its business focuses The ‘so what?’ RG related research adds new dimension to player insights & understanding…
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Editor's Notes
- #3 Can make this interactive Insights are currently generated by a monetary need/objective
- #4 We gather these insights through a variety of disparate sources Data sources: Transactional data (from POS/Machine) Customer data (playnow/Encore loyalty) Primary Data collection (traditional MR) Secondary (context setting etc.)
- #5 We gather these insights through a variety of disparate sources Data sources: Transactional data (from POS/Machine) Customer data (playnow/Encore loyalty) Primary Data collection (traditional MR) Secondary (context setting etc.)
- #7 We gather these insights through a variety of disparate sources Data sources: Transactional data (from POS/Machine) Customer data (playnow/Encore loyalty) Primary Data collection (traditional MR) Secondary (context setting etc.)