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Too free (or not too free)? A brief look at casino ‘comping’

I’m a great believer in the cliché that there is no such thing as a free lunch. Except of course of you are in Las Vegas and take advantage of the vast array of bonuses and complimentary offers (more commonly known as ‘comps’) that are on offer. It doesn’t take a psychologist to tell you that the psychology behind ‘comping’ is to get the gambler to spend more money. Comping is a legitimate psychological marketing strategy used as an incentive to either get punters to gamble in the first place, or an incentive used to prolong gambling. Here in the UK, we are obviously not on the same level as Atlantic City or Las Vegas, but most gambling establishments offer an array of temptations to get you to gamble. These include cash prize draws, gift raffles, tokens or credit boosts (for instance, winning additional credit on selected slot machines instead of cash), and scratchcards (which can be redeemed inside the arcade or casino). These types of marketing ploy have two main effects. Firstly, they get people exposed to the gambling environment. Secondly, they get people exposed to gambling itself.

As I noted in a previous blog, the frequency of bonuses varies depending the gambling establishment but can occur hourly, daily, weekly, or seasonally. These are often used to entice the consumer in several retail environments, but what makes them especially psychologically appealing in a gambling environment are the obvious similarities to the characteristics of gambling events in general (such as risk, uncertainty, intermittent reinforcement, and non-monetary psychological rewards). Furthermore, the appeal is strengthened since gamblers feel they are getting something for nothing.

“Comps” can come in many guises. These include travel amenities such as free room, food, drink, shows, golf, limos, with which the casinos reward their “good players” – those that spend (i.e., lose!) a lot of money – and entice other potential gamblers onto their premises. The easiest comps to get are free parking and fun books (which often contain coupons for free drinks, snacks, and souvenirs). For these comps, you don’t even have to gamble. Punters simply have to walk into the casino to get them. The lowest level comp for gamblers is the ubiquitous free drink. It doesn’t matter if you’re putting a quarter in a slot machine or laying down a couple of grand at the poker table, casinos will serve complimentary drinks. However, just remember that drinking alcohol over prolonged periods will impair judgement and rationality. The outcome is usually more money spent by the gambler, which is what the casino wanted in the first place!

It should be no surprise that the value of comps increases with the value of bets. The standard equation used by casinos to determine comps is: size of average bet times number of hours played times the house advantage times the comp equivalency. In other words, say you play blackjack, making £10 bets for two hours. The casino multiplies 120 hands (60 an hour) by £10 and comes up with £1,200 worth of action. It then multiplies £1,200 by the 2% house advantage and comes up with £24. This is what the casino believes it will win from you on average in two hours of $10 blackjack. It then multiplies £24 by 40% (i.e., what it is willing to return in comps). This means the gambler is entitled to £9.60 in freebie amenities.

Comps returned to the big gamblers include high-roller suites, lavish gourmet dinners, unlimited room service, en-suite Jacuzzi, private lap pool, ringside seats at live shows or sporting events, private parties, limos, and Lear jets. Does this sound good to you? It’s yours. All you need to do is bet $25,000 a hand in Las Vegas eight hours a day over a long weekend, or have a $5 million credit line. More within your reach are the comps for $25-a-bet gamblers. This might include half-price hotel room, limited food and beverage, and line passes to the show. The $100-a-bet gamblers will usually get full room, food, and beverage, meaning their whole stay is free. Simple psychological economics – but it works.

To enter the comp game, you must “get rated” by the casino. The casino then records your time in, time out, average bet size, and other details. The data are entered into the computer and casino marketing determines what comps you’re entitled to. If you are a slots player, the casino will use smart cards to monitor and assess your gambling. By playing table games the gambler can exploit the system. In short, it’s possible to trick the casino into thinking that you’re a bigger gambler than you really are by utilizing what is known as “comp wizardry.” Casinos are especially vulnerable to comp system exploitation, because a player’s gambling must be observed by pit bosses. Simple tricks by the gambler include looking like a loser, slowing down the speed of play (such as playing one hand every minute and a half instead of every minute), and betting more when the pit bosses are watching and less when they aren’t. It’s the simplest psychology that can minimize your risk and maximize your reward in the comp game.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Further reading

Griffiths, M.D. (2005). The psychology of gambling: Complimentary nuts. Inside Edge: The Gambling Magazine, November (Issue 20), p. 66.

Griffiths, M.D. (2007). Brand psychology: Social acceptability and familiarity that breeds trust and loyalty. Casino and Gaming International, 3(3), 69-72.

Griffiths, M.D. (2010). Online ads and the promotion of responsible gambling. World Online Gambling Law Report, 9(6), 14.

Griffiths, M.D. (2012). Internet gambling, player protection and social responsibility. In R. Williams, R. Wood & J. Parke (Ed.), Routledge Handbook of Internet Gambling (pp.227-249). London: Routledge.

Griffiths, M.D. & Parke, J. (2003). The environmental psychology of gambling. In G. Reith (Ed.), Gambling: Who wins? Who Loses? (pp. 277-292). New York: Prometheus Books.

Griffiths, M.D. & Wood, R.T.A. (2008). Responsible gaming and best practice: How can academics help? Casino and Gaming International, 4(1), 107-112.

Griffiths, M.D. & Wood, R.T.A. (2009). Centralised gaming models and social responsibility. Casino and Gaming International., 5(2), 65-69.

Wood, R.T.A., Shorter, G.W. & Griffiths, M.D. (2014). Rating the suitability of responsible gambling features for specific game types: A resource for optimizing responsible gambling strategy. International Journal of Mental Health and Addiction, 12, 94–112.

Loss leaders: What is the best way to measure ‘gambling intensity’?

The issue of how to measure ‘gambling intensity’ is an important one in the gambling studies field. Gambling intensity is one of those concepts that means different things to different researchers but basically refers to how absorbed gamblers are based on the time and money they spend gambling. Over the last few years, this issue has become much more to the fore as researchers in various jurisdictions have been given access to behavioural tracking data (i.e., actual data showing what online gamblers actually do online such as the games they are playing, the time they spend online, the amount of money that they spend, etc.). This has initiated a whole new line of gambling research that is already providing insights about gambling that we never had before.

Many of these studies have used proxy measures for gambling intensity including variables such ‘bet size’ and ‘number of games played’. Another major problem with these studies is that they have tended to present data by single game type (e.g., only data from online poker players or sports bettors are presented). However, as researchers such as myself have noted, online gamblers typically gamble on a variety of games.

There are various ways to conceptualize gambling intensity. Such ways could include parameters involving the time spent gambling, the number of gambles made, and/or the amount of money won or lost while gambling. In almost all of the studies carried out to date, monetary involvement has tended to be the main proxy used measure for gambling intensity. However, I and my colleague Michael Auer have proposed a different proxy measure for the money risked while gambling. We define gambling intensity as the amount of money that players are putting at risk when playing. This might be considered easy to do (e.g., by using ‘bet size’), but the element of chance is rarely accounted for, especially when a random win occurs. For instance, two gamblers putting the same amount of money at risk might end up with very different wins or losses at the end of similar length gambling sessions because of the chance factor. For this reason, we are now using a measure that is completely independent of random events and takes into account the true amount of money that players are prepared to risk. The interesting aspect of this is that most of the time, gamblers themselves are probably not aware of the amount of money they risked at the end of a playing session.

Our first published paper in this area was a simulation study published last year in the journal Gaming Law Review and Economics. In that paper, we demonstrated that the most robust and stable measure for ‘gambling intensity’ is what we call the ‘theoretical loss’. Our fiest paper on this topic showed that all previous studies using proxy measures for ‘gambling intensity’ had failed to take into account the house advantage. Outcomes in games of chance over the long-term will always be dependent upon the house advantage of each different type of game. Dr. S. Li showed in a 2003 paper published in the Journal of Risk Research that ‘at risk’ decision-making in the short-term is totally different from decision-making over longer periods of time. Decision making over the long-term can be explained by the expected value whereas short-term decision-making does not seem to be based on any expectation rule. However, studies investigating decision-making in situations where people have to make choices assume that players have a real choice in which they can truly influence the outcome and (thus) the expected return. However, this is not the case in pure chance games. Whatever the player chooses to do in pure chance situations, the house advantage will determine the expected return in the long-term.

As we pointed out in our 2012 paper, games with a high house advantage lead to higher player losses and games with a low house advantage lead to lower player losses. Theoretical loss is the same measure that the gaming industry describes as Gross Gaming Revenue (GGR), and is the difference between ‘Total Bet’ and ‘Total Win’. The ‘theoretical loss’ of any given game is represented by the product of the bet size and the house advantage. Over very long periods of time, the theoretical loss corresponds to the GGR with increasing accuracy. The more diverse the gambling behaviour, the more that bet size deviates from the theoretical loss.

By incorporating the theoretical loss, the amount risked can be measured at a very detailed level. For instance, French roulette has a house advantage of 2.7% and keno has a house advantage of 10%. This means that a player who repeatedly bets $100 on roulette will end up with a loss of $2.7, and a player who repeatedly bets $100 on keno will end up with a loss of $10. Therefore, the product of bet size and theoretical loss represents the amount of money that player will lose in the long run. Previous studies that have used bet size (as a proxy measure for gambling intensity) would assign the same gambling of $10 intensity to the two players in the aforementioned example (and which obviously is not the case). The bet size is the one risk parameter that players are most likely to be aware of during gambling. However, it is deceptive as it does not take into account the expected return/loss that is controlled by the gaming operator via their house advantage.

Our simulation study of 300,000 online gamblers showed that bet size explained only 56% of the variance of the theoretical loss, and the number of games played explained 32% of the variance of theoretical loss. This means that when using bet size alone, 44% of the gambling behaviour remains unexplained. When using the number of games played alone, 68% of the variance is left unexplained. As this study was a simulation, we recently replicated our first study using real online gambler behavioural tracking data. There are many advantages and disadvantages with using data collected via behavioural tracking. However, the main advantages are that behavioural tracking data (a) provide a totally objective record of an individual’s gambling behaviour on a particular online gambling website, (b) provide a record of events and can be revisited after the event itself has finished, and (c) usually comprise very large sample sizes.

Our latest study on theoretical loss in the Journal of Gambling Studies comprised 100,000 online gamblers who played casino, lottery or poker games during a one-month period on the Austrian win2day gambling website. All games played by these gamblers were recorded and subsequently analysed. The game types were categorized into eight distinct groups: (i) Lottery – Draw/Instant, (ii) Casino – Card, (iii) Casino – Slot, (iv) Casino – Videopoker, (v) Casino – Table, (vi) Casino Other, (vii) Bingo and (viii) Poker. For each of the game types and each player, the ‘bet size’ and the ‘theoretical loss’ were computed for the recorded time period. In terms of house advantage these game types are very different. In general, lottery games have a relatively high house advantages (typically 50%) whereas slot machines have house advantages in the range of 1 to 5% depending on the gaming platform and the specific game. Poker on the other hand does not have a house advantage as such. In poker, the gaming involvement can be measured via the rake. The rake is a fixed percentage of the stake (bet size) that goes to the casino. The overall theoretical loss is thus comprised of the theoretical loss across all game types plus the poker rake.

Although we found a high correlation between the ‘bet size’ and the overall ‘theoretical loss’ across the eight game types for the 100,000 players, we also found the bet size alone explained only 72% of the variance of the theoretical loss (not as large as we found in our simulation study but that was most likely because we had more games in the simulation study and the games in the simulation study were approximated house advantages whereas the follow-up study used actual house advantages.

This study broadly confirmed the findings from our previous simulation study. The results of our most recent study suggest that future research and particularly those that utilize behavioural tracking approaches should measure their participants’ gambling intensity by incorporating the game-specific theoretical loss instead of using proxy measures such the bet size and/or the amount of money staked. Another implication is that previously published research could be re-analysed using the more robust measure of gambling intensity presented here (i.e., theoretical loss) rather than the proxy measures that were used in the original published studies. This study demonstrates that bet size does not reliably indicate the amount of money that players are willing to risk as it does not take into account the house advantage of each individual game that gamblers engage in. The house advantage represents the percentage held back by the gaming operator and is essential for the amount lost in the long-term and will eventually be equal to the total losses that a player accumulates. In order to further generalize our results, further empirical research utilizing data from other online gaming platforms as well as land-based casino premises needs to be carried out.

Dr Mark Griffiths, Professor of Gambling Studies, International Gaming Research Unit, Nottingham Trent University, Nottingham, UK

Additional input: Michael Auer

Further reading

Auer, M. & Griffiths, M.D. (2013). An empirical investigation of theoretical loss and gambling intensity. Journal of Gambling Studies, in press.

Auer, M., Schneeberger, A., & Griffiths, M.D. (2012). Theoretical loss and gambling intensity: A simulation study. Gaming Law Review and Economics, 16, 269-273.

Broda, A., LaPlante, D. A., Nelson, S. E., LaBrie, R. A., Bosworth, L. B. & Shaffer, H. J. (2008). Virtual harm reduction efforts for Internet gambling: effects of deposit limits on actual Internet sports gambling behaviour. Harm Reduction Journal, 5, 27.

Colbert, G., Murray, D., Nieschwietz, R. (2009). The use of expected value in pricing judgements. Journal of Risk Research, 12, 199-208.

Griffiths, M.D. & Auer, M. (2011). Online versus offline gambling: Methodological considerations in empirical gambling research. Casino and Gaming International, 7(3), 45-48.

Griffiths, M.D. & Whitty, M.W. (2010). Online behavioural tracking in Internet gambling research: Ethical and methodological issues. International Journal of Internet Research Ethics, 3, 104-117.

LaBrie, R.A., Kaplan, S., LaPlante, D.A., Nelson, S.E., & Shaffer, H.J. (2008). Inside the virtual casino: A prospective longitudinal study of Internet casino gambling. European Journal of Public Health, 18, 410-416

LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Population trends in Internet sports gambling. Computers in Human Behavior, 24, 2399–2414.

Li, S. (2003). The role of Expected Value illustrated in decision-making under risk: Single-play vs multiple-play. Journal of Risk Research, 6, 113-124.

Wardle, H., Moody, A., Griffiths, M.D., Orford, J. & and Volberg, R. (2011). Defining the online gambler and patterns of behaviour integration: Evidence from the British Gambling Prevalence Survey 2010. International Gambling Studies, 11, 339-356.