Social games where players play together with others in multiplayer mode are currently emerging and attracting attention in the media after the success of e.g. Pokemon GO. Nevertheless, little is known about the profitability of social players. Previous gaming research that has profiled different types of players has focused on games played alone, without social mechanics (cf. Vahlo et al., 2017). From a marketing perspective, it is also interesting to study the effects of merely watching game play or browsing new game ads as a mode of entertainment, and what kind of effect that has on future behaviour. Passive participation as a type of aesthetic entertainment has been acknowledged in eSports context (Seo, 2013). This paper analyses big data of a gaming company on an individual player level, including different types of single play and multiplayer gaming sessions and in-game purchase behaviour data. We compare the effects of social and non-social ingame mechanics on how individuals spend money in-game over time, frequency of play, sessions as well as the length of played sessions in minutes during the gamers’ whole lifecycle. The anonymized repository data includes gaming behaviour, in-game purchase behaviour related to the use of one specific digital game in the USA. The game can be played both alone, and with others as a team. We used structural equation modeling to analyse the behavior of 23 049 randomly selected players, who have played the game for at least one week. The data included the individual players’ total session history during their play lifecycle. Interestingly, social play with other people is a strong predictor of money spent in the long term. Social play also prompts long-term interest in the game, as friends invite and encourage each other to play with or against other virtual teams. Nevertheless, social play is not for everyone! The results help to optimize player journeys and to make strategic decisions that support long term profitability of gaming companies.