Using industry data to understand patterns of play

Gambling
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Chaired by Sir Christopher Kelly KCB, the Responsible Gambling Strategy Board has published a research brief which sets out the proposed approach for the Gambling Commission and GambleAware’s latest research programme.

We need to understand how gambling behaviour and patterns of play vary across different products and environments. We also need to understand if, how and why some Sir Christopher Kellygambling environments, products and characteristics are more harmful than others.

We also need to improve our understanding of consumer vulnerability by linking industry play data with individuals’ demographic data, socio-economic background, and at-risk/problem gambling status.

This research needs to go beyond simply analysing data which is already held. It will involve an extensive analysis of real play data provided by the gambling industry and will also require effort to collect and link this socio-demographic data and at-risk/problem gambling status from players.

Industry data exists in many forms, and the level and type of data available vary from sector to sector. The most complete data records are held by online gambling companies, where data is available for individual players. Analysis of online play data will therefore constitute the first phase of this research.

For this project, the Gambling Commission will make a data request to industry to obtain the data needed for the research, as we do not currently have access to the datasets required. The research team will work with the Gambling Commission to shape the data request made to industry. Industry will also be asked to support actions to gain consent from players for additional data to be collected.The Gambling Commission is committed to supporting this process to ensure that the successful research team have access to the data and to the players they need to answer the research questions.

PRIORITY PRODUCT CATEGORIES AND KEY METRICS

Phase 1 will focus on online gambling. This is an area where we believe data should be most readily available. It is also an area where we have a number of evidence gaps in how people play. It is a large and growing market and therefore there is significant scope for players being harmed by their gambling in this environment. We have identified the following online products (listed in priority order) for which we believe that data are being systematically recorded:

– Online betting – where we currently have very little descriptive data for this very large product group. This includes sports and non-sports betting, betting exchanges and pool betting.

– Slots/Casino games where we currently have an aggregate understanding (see Forrest and McHale, 2018) which should act as the foundation for further data collection and analysis and other high frequency

– National lottery instant wins, and high frequency products
– Online bingo
– Online poker
– National Lottery draw based games – where tickets are bought online

KEY METRICS

Some of the key metrics that could be analysed will vary according to a sector’s ability to link play to an individual and across sessions. As the supporting documents outline, this is more easily achieved in the online environment. However, they may include (but are not limited to):

– Stakes (size, frequency, time between)
– Volume and value of deposits
– Speed of play
– Day of the week, time of the day
– Session and longer-term outcomes
– Availability of additional products or games
-Proportion of revenue from 10pc of customers

Where possible it will also be beneficial to consider broader contextual data, such as:

– Payment methods used (e.g. credit or debit cards)
– Patterns of withdrawal and deposit
– Use of gambling management tools (e.g. where pre-commitment spend or time limits are set)
– Contact with customers services / customer interaction
– Wider gambling behaviour (i.e. other products played).

In the online environment it is possible to link key metrics to demographic data such as age, sex and region.It is possible that other variables could be merged onto the datasets to explore how play varies for different types of people. However, to explore this further we anticipate that surveys of online players will need to be conducted to gain more socio-demographic information as well as data on players’ problem or at risk gambling status.

We are therefore looking for teams which include individuals with survey design expertise to develop a suitable methodology and identify any risks or issues that may need to be mitigated (such as achieving good response rates or being


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