Evidence theme 4 - The impact of operator practices
Evidence theme 4 - The impact of operator practices
This theme is about:
- understanding how common operator practices influence consumer behaviour
- assessing the effectiveness of interventions designed to detect and reduce gambling harms.
Gambling operators – along with gamblers and the gambling product – are part of the trio which make up gambling experiences. They have a significant ability to influence gambling activity through the environment that they provide and their behaviours.
Although operators are reliant upon their customers for revenue, they are also obligated to protect their customers from harm through regulatory requirements such as customer interaction requirements - and the largest gambling industry trade bodies have also made harm reduction commitments. Given the inherently risky nature of gambling, and the unusual adversarial relationship between gambler and operator, our research into exploring the information needs of consumers suggests that gamblers feel a tension around trust which risks undermining safer gambling messages.
Operator practices that impact upon consumers can include communications that encourage gambling such as advertising that is visible to everyone and targeted marketing to selected groups. It also includes communications that encourage gambling mitigation through the provision of tools and safer gambling messaging, whether targeted or otherwise and conducted through a range of different channels.
Other practices that could be considered include the presentation of information about products or offers, the way that a product or game functions and the location of gambling opportunities in-person or through online choice architecture. Remote operators often rely upon harm detection algorithms to identify consumers at increased risk of experiencing harms, and the effectiveness of these algorithms is also a topic of interest.
Amongst operator practices that are visible universally, research has examined the relationship between operator advertising practices, consumer awareness and consumer activity.14 There is the potential to understand more about directed advertising practices, how they fit into a complex online advertising ecosystem and their potential impact on selected groups. A particular group of interest are people living in disadvantaged communities who are more likely to be in the vicinity of land-based gambling premises15 and whether there is a link with other demographic characteristics identified in the previous theme as being associated with an increased risk of experiencing harms. Better understanding of the impact of advertising on children and young people is also important.
Regarding the more direct relationship, rules relating to VIP schemes were strengthened in 2020 and a significant reduction of consumers considered to be VIPs was reported by one industry body following the publication of their code of conduct.16 For telephone contact, there is indicative evidence of telephone calls to gamblers providing a positive impact on subsequent gambling activity17, including high-spending gamblers18. However, there are still a lot of unknowns regarding the impact on subsequent experience of harms and of safer gambling messaging on different groups of people. Methods of increasing uptake of safer gambling tools have been identified19 but the subsequent impact of doing so remains unclear and it is uncertain whether behaviours observed in jurisdictions with few regulated operators would be replicated in Great Britain.
To feed into harm-detection algorithms, research has been conducted that seeks to identify potential indicators of higher-risk gambling20 and it remains an area of great interest and debate, with differences between products, platforms and the amount of gambling activities likely. An improved understanding of the customer journeys that once seemed on a trajectory towards more harmful activity but did not progress to that level could be insightful in identifying protective operator practices. An additional unknown relates to the varying implementation approaches for algorithms, machine learning, artificial intelligence or other technological solutions applied by operators.
To explore this research theme, blending multiple sources of data is likely to be required, including operator-held account-level data suitable for detailed analysis, financial data, qualitative data, and potentially longitudinal data.
Example research questions within this theme
These are the type of questions that could be considered in relation to this theme:
- How can marketing and safer gambling practices be incorporated effectively together as part of a seamless player experience?
- How well do consumers understand information (for example, about offers or products) provided to them by operators?
- How effective are harm detection algorithms used by online operators?
- What are the factors that drive and influence consumer's perception of whether gambling is fair and can be trusted?
Evidence theme 4 - Progress made in year one
The Gambling Commission published new research exploring how gambling consumers engage with bonus offers and incentives, and the role that they play in the consumer journey, addressing evidence gaps outlined in our advice informing the Gambling Act review.
We also published research in support of our consultation on financial vulnerability and financial risk checks, which used a deliberative approach to enable participants to reflect upon the proposed policies in an environment free from media influence where information could be explained and explored.
Our Data Innovation Hub conducted pilot sprint projects exploring how to make best use of open-source data from Meta and Twitter to further understand marketing and advertising practices.
The Behavioural Insights Team have conducted several experiments testing different designs of game features, advertising and spending limits to isolate effective interventions for safer gambling.
Forward look
Upcoming research being conducted as part of our Consumer Voice programme will explore consumer trust in the gambling industry and the effect that operator practices have on this.
We are designing a pilot of industry data collection of a more detailed daily dataset (Regular Feed of Operator Core Data – ROCD) from a small number of operators, which if successful would enable us to conduct more in depth analyses of consumer play data to explore the impact of a range of factors on gambling behaviour.
Last year we published the outcome of a consultation on the frequency at which regulatory returns should be submitted by operators. As a result, from July 2024 all operators have moved to quarterly regulatory return submissions, with harmonised reporting periods. This will provide a deeper, more timeley, more accurate understanding of the gambling sector.
We will continue the work started in the pilot data sprints, using the Meta ad library to track trends in gambling advertisements and expand the analysis to other social media platforms.
Notes
14The effect of gambling marketing and advertising on children, young people and vulnerable adults (opens in new tab) (PDF), Ipsos MORI, 2020.
15Geography of gambling premises (opens in new tab) (PDF), J Evans and K Cross, University of Bristol, 2021.
16BGC welcomes new rules on VIP schemes (opens in new tab), Betting and Gaming Council, 2020.
17Patterns of Play Technical Report 2: Account Data Stage (opens in new tab) (PDF), D Forrest and I McHale, NatCen, 2022.
18Reaching out to big losers: Exploring intervention effects using individualized follow-up (opens in new tab), J Jonsson, I Munck, D Hodgins and P Carlbring, Psychology of Addictive Behaviours, 2023.
19Can behavioural insights be used to reduce risky play in online environments? (opens in new tab) (PDF), The Behavioural Insights Team, 2018.
Safer Gambling Messaging Project Phase Two An impact evaluation from the Behavioural Insights Team (opens in new tab) (PDF), report commissioned by GambleAware and completed by the Behavioural Insights Team.
20Examples include: Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting (opens in new tab), M Auer and M D. Griffiths, Journal of Gambling Studies, 2022.
Predicting online gambling self-exclusion: An analysis of the performance of supervised machine learning models (opens in new tab), C Percy, M França, S Dragičević and A d’Avila Garcez, International Gambling Studies, Volume 16, 2016, pages 193 to 210.
Evidence theme 3 - Gambling-related harms and vulnerability Next page
Evidence theme 5 - Product characteristics and risk
Last updated: 11 July 2024
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