An empirical attempt to identify binge gambling utilizing account-based player tracking data

Abstract

Binge gambling is a relatively under-explored area and the few published studies have all used self-report data (i.e. surveys and interviews). The use of account-based tracking data has increasingly been used to identify indicators of problem gambling. However, no previous study has ever used tracking data to operationalize and explore binge gambling. Therefore, the present study investigated whether it is possible to identify behavioral patterns that could be related to binge gambling among a real-world sample of online gamblers. The authors were given access to an anonymized secondary dataset from a British online casino operator comprising 150,895 online gamblers who gambled between January and March 2023. Using 14 parameters of gambling (e.g. total number of gambling days, total number of gambling sessions, average amount of money spent per game), six distinct clusters of gamblers were identified. Two clusters – Cluster 2 (n = 22,364) and Cluster 5 (n = 12,523) – gambled on a relatively low number of days during three months, but displayed a high gambling intensity on those days compared to the other four clusters. These two profiles could potentially match the habits of binge gamblers. The majority of players retained their behavior in the following three months between April and June 2023 and were consequently assigned to the same cluster in the latter time period. A total of 17% of gamblers in Cluster 3 and 29% of gamblers in Cluster 5 stopped gambling entirely between April and June 2023. The findings suggest that binge gambling may be able to be identified by online gambling operators using account-based tracking data and that targeted interventions could be implemented with binge gamblers.