BCLC’sEnterprise Business Intelligence Landscape
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
GameSense Brand Platform Redesign
• Brand Evolution: Tone, Connection & Relevancy
RG Player Segmentation
• Targeted Communication & Programming
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
GameSense Brand Platform Redesign
- Brand Evolution: Tone, Connection & Relevancy
RG Player Segmentation
- Targeted Communication & Programming
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)
What's unique about this 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
The ‘now what?’
Challenge ourselves 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…
Can make this interactive
Insights are currently generated by a monetary need/objective
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.)
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.)
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.)