Comparing purchase patterns in online and offline gambling Chris Hand, Kingston University.

Comparing purchase patterns in online and offline gambling Chris Hand, Kingston University. One oft-cited advantage of online shopping is convenience, especially in the grocery market which has been the focus of much research attention. The ability to buy online overcomes situational factors such as ill health, or the presence of small children in the household (e.g. Morganosky and Cude, 2000). For many product categories, such ease of access is a clear advantage; however, for some it may be a disadvantage if over-use or overconsumption may be harmful to either the consumer, or to the consumers family and friends. Gambling is one such category. This paper investigates the incidence of and the degree of cross-purchasing between offline and online forms of gambling using data from a UK government sponsored survey. Studies of gambling have, with a few exceptions discussed below, tended to focus on the traits and motivations associated with betting. For example, Lam (2007) found that different motivations were associated with different forms of betting; social interaction was significant predictor of casino betting whereas excitement was a predictor of betting on lotteries, track betting and casinos. Similarly, Fang and Mowen (2009) found that different forms of betting were associated with different behavioural antecedents. Studies of participation in individual forms of gambling have tended to focus on demographic characteristics; lotteries in particular have received a lot of attention (e.g. Browne and Brown, 1994, Grun and McKeigue, 2000). Increasingly, research attention is focussing on online gambling behaviours. A number of studies have suggested that online gambling (and other forms of electronically mediated gambling) may be more dangerous to players than gambling in casinos or bookmakers (e.g. Smith and Campbell, 2007; King, Delfabbro and Griffiths, 2010), whilst Siemens and Kopp (2011) found that online gambling environments made it more difficult for gamblers to keep track of the balance in their accounts. Cotte and Latour (2009) investigated gamblers perceptions of online and casino gambling, using a series of indepth interviews undertaken in Las Vegas. Their findings suggested that casino gambling was associated with more social contact and a heightened sense of excitement, whereas online gambling lacked social contact. However, among casino gamblers social contact was seen as an advantage, which added to the experience. Online gamblers in contrast preferred the lack of social contact either because it removed distractions from the game or because the experience in the casino was seen as too overwhelming. These findings echo those presented by Lam (2007), but show that in addition to different games meeting different needs, different channels also met different needs. A second stream of literature has examined patterns in gambling behaviour. Gambling participation has been found to follow similar patterns (in terms of penetration rates, frequency and cross-purchasing) as other forms of purchase behaviour. For example, Lam and Mizerski (2009) found that the penetration and frequency of participation in gambling could be described by a general model of brand performance known as the Dirichlet model. Furthermore, the model described behaviour of both regular and problem gamblers. Lam (2006) found evidence that gambling conformed to a pattern of cross purchasing known as duplication of purchase (DoP). The duplication of purchase law says that the proportion of the buyers of brand A who also buy brand B reflects brand B s overall penetration rate or in other words the proportion who buy Brand B (see e.g. Uncles, Ehrenberg and Hammond, 1995). The duplication of purchase law has its roots in audience measurement in particular magazine readership (e.g. Agostini, 1961) and television viewing (e.g. Ehrenberg and Goodhardt, 1969) and was subsequently found to hold more generally across brand purchases (e.g.ehrenberg, 1988). It has also been found to hold for radio audiences (Lees and Wright, 2013) and for leisure pursuits (Hand, 2011). One implication of

DoP is that the market is unsegmented each brand is cross-purchased with every other brand. Hand and Singh (2014) examined a larger number of gambling pursuits than Lam (2006) in the UK and found a number of deviations from the duplication of purchase pattern (known as partitions). In order to formulate expectations of online and offline gambling behaviour, we look to the broader online buying behaviour literature. Prior studies of online and offline grocerybuying have identified a number of differences in online buying compared to offline. For example, Andrews and Currim (2004) found that, compared to offline customers, online customers bought from a smaller repertoire of brands, were less price sensitive and preferred larger pack sizes. A heightened level of brand loyalty amongst online customers was also found by Danaher, Wilson and Davis (2003) when measured against benchmarks provided by the Dirichlet model. Such differences in behaviour could be explained by differences in the characteristics of online and offline shoppers, rather than the characteristics of the channels themselves. However, findings reported by Hand, Dall Olmo Riley, Harris, Singh and Rettie (2009) suggested that online shopping was complementary to shopping in store, rather than a substitute for it, and hence online customers would also be offline customers. The study by Chu, Arce-Uriza, Cebollada-Calvo and Chintagunta (2010) also found higher loyalty online, based on data that captured online and offline purchases by the same customers. A more recent study, by Dawes and Nencyz-Thiel (2014), investigated cross-purchase patterns between online and offline supermarket brands and between online supermarkets. They proposed that the majority of online shoppers of a retailer were also offline shoppers of that retailer and that higher levels of cross-purchasing would be seen between online retailer than between offline retailers. Based on consumer panel data for the UK, they find evidence to support both of these propositions. Given that similar patterns of behaviour have been found in general buying behaviour and in gambling (as discussed above), it is reasonable to assume that what has been found in the online buying literature may also be found when online and offline gambling behaviours are compared. Propositions Based on the discussions above, three propositions are developed. Adopters of online shopping typically do not abandon in-store shopping altogether (e.g. Hand et al. 2009). Rather the different modes of shopping are used in combination with some goods bought in person and others online; store visits may also be used to fill the gaps between deliveries of larger online orders. Such an argument is less applicable to online gambling online and offline bets are direct substitutes. However, different modes of gambling could also be used to meet different needs so it need not follow that they are mutually exclusive. Hence the following proposition is put forward: P1: The majority of online gamblers will also gamble offline Online buyers might be predominantly also offline buyers if trust in the retail brand were transferred from offline to online. Online retailers use the same brand and branding as in their offline stores. In this study, we examine gambling at the game level (e.g. betting on horse racing or on casino games), so the transference of brand loyalty is less likely to be the case. Rather, at the category level, the closest substitute for an offline bet is likely to be an online version of the same form of gambling. However, Cotte and Latour s (2009) findings that the online and offline forms of the same form of gambling may be associated with different motivations could also imply that online gamblers may not necessarily be in-person gamblers. Nonetheless, it is proposed that: P2: Within each form of gambling, the majority of people who gamble online will also gamble in-person.

The third proposition put forward concerns the degree of cross-purchasing between different forms of gambling. Less switching between online retailers might be expected than between offline retailers. Keeping the same online retailer provides the convenience of saved shopping lists or favourites. However, a counter-argument would be that switching could be easier online than offline, as additional travel times to and from the store are eliminated (although there is also a cost in terms of setting up a new account with the online retailer). Dawes and Nenycz-Thiel (2014) found a higher level of cross-purchasing online than offline, implying that switching between retailers online is easier than switching between stores (which may be constrained by the strength of habit or the location of the stores). The benefit of saved lists does not really apply to online gambling, so this is unlikely to decrease the degree of cross-purchasing, however the comparative ease of switching online than offline does apply. Hence it is proposed that: P3: Higher levels of cross-purchasing will be found online than offline. Data Studies of buyer behaviour tend to use data captured at point of sale or from consumer panels (such as the Kantar world panel). Unfortunately, no such panel data exists for online and offline gambling purchases. The data used here were collected as part of the British Gambling Prevalence Survey, commissioned by the Gambling Commission and undertaken by the National Centre for Social Research (National Centre for Social Research, 2011). This survey employed a sample of 7,756 respondents drawn from across the UK. The survey was based on a random sample drawn from the Postcode Address File and employed a computeraided self interviewing data collection method. The survey was undertaken in 2010 as a follow on to two previous surveys (undertaken in 1999 and 2007). The Gambling Commission has since decided against commissioning a fourth survey, but did include question on the Health Survey for England undertaken in 2012-13. However, the survey collected information on online gambling via a single question on participation. Behaviour over three time frames is captured by the survey: over the last 12 months, regular/monthly participation and over the past 7 days. However, questions about both online and offline gambling are only asked for all forms of gambling in the past 12 months questions. Questions on frequency are also asked, but do not always separate out online and offline participation. Collecting recalled information on participation over such a long period has disadvantages. However, in this particular context focussing on participation (rather than frequency of participation) it also has an advantage it is likely to reduce the effect of social desirability bias. The survey covers a wide range of gambling pursuits, but for the crosspurchase analysis in this study we focus on five forms of gambling: betting on horse races, on slot / fruit machines, bingo, betting on (other) sports and casino games. These were selected on the basis of two criteria: data on both online and offline participation are available and a penetration rates of above one per cent were recorded (online bets on dog races were captured by the survey, but very few people gambled in this way, 0.3% of the sample). Results Looking across the complete range of gambling pursuits covered by the survey and the subset of types selected for the cross-purchase analysis, it is clear that gambling in person remains the dominant mode. Table 1. Proportions of respondents gambling online, offline and both Full survey Subset Online only 1.6% 1.9% Online or both 13.9% 6.8%

Offline only 59.5% 25.8% Did not gamble 26.3% 67.4% The higher penetration for gambling in the full survey data set is largely due to the inclusion of the National Lottery (59.8% had gambled on it), along with scratchcards and charity lotteries (each recording penetration rates of around 25%). Perhaps contrary to fears about the dangers of online gambling, participation at the time of the survey is rather low less than 2% of the survey respondents only gambled online, whilst most gambled in person. Hence, online gambling seems to follow the same pattern of usage as online (grocery) buying as a complement rather than a substitute, hence proposition 1 is supported. The second and third propositions relate to cross-purchasing. Patterns of crosspurchase between forms of gambling (also known as duplication) are shown in table 2 below. Table 2. Cross-purchase / Duplication of purchase Offline Online Offline % Horses Slots Bingo Sports Casino Slots Sports Horses Casino Bingo played Horses 14.7 -- 26.4 13.0 26.4 9.4 4.6 5.4 4.5 4.3 2.5 Slots 12.3 31.4 -- 17.2 21.7 14.5 13.9 7.1 5.1 8.9 7.3 Bingo 7.8 24.5 27.5 -- 8.6 7.1 6.0 2.2 2.0 2.6 7.0 Sports 6.3 61.5 42.4 10.7 -- 20.1 10.3 12.1 6.4 10.1 3.9 Casino 3.4 40.2 52.3 16.2 37 -- 17.7 17.7 8.6 16.2 4.1 Average 39.4 37.2 14.3 23.4 12.8 10.5 8.9 5.3 8.4 5.0 Online Slots 2.6 25.9 66.2 17.9 24.9 23.4 -- 18.9 13.4 24.4 23.4 Sports 2.1 37.0 41.2 7.9 35.8 28.5 23.0 -- 46.1 29.7 10.9 Horses 1.9 34.5 33.1 7.4 20.9 15.5 18.2 51.4 -- 21.6 8.1 Casino 1.8 34.3 59.4 11.2 34.3 30.1 34.3 34.3 22.4 -- 16.8 Bingo 1.7 20.7 51.9 31.1 14.1 8.1 34.8 13.3 8.1 17.8 -- Average 30.5 50.4 15.1 26.0 21.1 27.6 29.4 22.5 23.8 14.8 Duplication Coefficients Offline Offline = 3.2 Online Offline = 3.7 Offline Online = 3.8 Online Online = 11.6 The types of gambling in the columns and rows are arranged in descending order of penetration (i.e. the proportion of the sample who reported gambling in this way in the last 12 months). If duplication of purchase holds, the figures in each row should decline when read from left to right or equivalently, the average duplication for each form of gambling (the overall column average) and the penetration rate should be correlated (r = 0.85, p<0.01). The figures shown in bold are the duplications between the online and offline forms of each type of gambling. Reading across the first row of the table, 14.7% of the sample reported gambling on horse races offline; of those people, 26% played slot machines, 13% played bingo and so on. It also shows that 4.5 % also gambled on horse races online. Proposition 2 stated that those who gambled online on a particular event would be more likely to gamble on the same event offline. This would appear in table 2 as overduplication of offline gambling amongst online gamblers (as shown in the lower left quadrant of table 2). Proposition 2 holds for slot machines (66.2% of online players also played slot machines offline). However, for the other forms of gambling, the results appear to be less clear cut. Around a third of those who gambles on horse races online also gambled on horses

offline, and only slightly fewer had gambled on slot machines. For the other forms of gambling between 30% and 35% of online players also play offline, a greater proportion have gambled in other ways offline. For example, 31.1% of online Bingo players also played Bingo in person, but 51.9% had played slot machines in person. The patterns in cross-purchase tables can be made clearer if deviations from the column averages are examined. Table 3 below shows the deviations from the average for each column of each quadrant. Table 3. Cross-purchase patterns deviation from average cross-purchase rates. Offline Online Offline % Horses Slots Bingo Sports Casino Slots Sports Horses Casino Bingo played Horses 14.7 -- -10.8-1.3 3.0-3.4-5.9-3.5-0.8-4.1-2.5 Slots 12.3-8.0 -- 2.9-1.7 1.7 3.4-1.8-0.2 0.5 2.3 Bingo 7.8-14.9-9.7 -- -14.8-5.7-4.5-6.7-3.3-5.8 2.0 Sports 6.3 22.1 5.3-3.6 -- 7.3-0.2 3.2 1.1 1.7-1.1 Casino 3.4 0.8 15.2 1.9 13.6 -- 7.2 8.8 3.3 7.8-0.9 Average duplication -- 39.4 37.2 14.3 23.4 12.8 10.5 8.9 5.3 8.4 5.0 Online Slots 2.6-4.6 15.8 2.8-1.1 2.3 -- -10.6-9.1 1.0 8.6 Sports 2.1 6.5-9.2-7.2 9.8 7.4-4.6 -- 23.6 6.3-3.9 Horses 1.9 4.0-17.3-7.7-5.1-5.6-9.4 21.9 -- -1.8-6.7 Casino 1.8 3.8 9.0-3.9 8.3 9.0 6.7 4.8-0.1 -- 2.0 Bingo 1.7-9.8 1.5 16.0-11.9-13.0 7.2-16.2-14.4-5.6 -- Average duplication -- 30.5 50.4 15.1 26.0 21.1 27.6 29.4 22.5 23.8 14.8 The measurement criterion for over (and under) duplication is largely, as Scriven and Danenberg (2010) note, a matter of managerial usefulness, although a deviation of 5 points or more is often used. Looking at the lower left quadrant of the table, it shows that among online gamblers, there are higher than expected duplications for the online equivalents (again, shown in bold) except betting on horse races, which falls just below the 5 points criterion. However, for horse racing the other duplications rates are lower than would be expected (the values in table 3 are negative). There are other over-duplications too. More online sports gamblers also bet on horse races than would be expected and more online casino players also play slot machines. When we look at offline gamblers also gambling online (the top right quadrant of table 3), the proposition does not appear to hold so strongly. However, as the averages for that quadrant are low (all bar one are less than 10), a deviation of 5 points would require the cross-purchase rate to be 50% higher (or lower than the average). More of those who play Casino games offline also play online than would be predicted by the duplication of purchase law, whilst slightly more of those who play bingo, slot machines and bet on sports offline also do so online whilst for betting on horse races, the duplication level is close to the average. Hence, tables 2 and 3 show support for proposition 2, although that support is more clear cut for offline gamblers also gambling online. Looking across tables 2 and 3 as a whole, some general patterns of cross-purchasing emerge. Playing Casino games over-duplicates with playing slot machines which could arise

from the availability of slot machines in Casinos. However, playing casino games online also over duplicates with playing slot machines online. This could reflect that some table games in Casinos, such as roulette and dice are games of chance, as are slot machines (as opposed to games with a skill element like some card games). Playing Bingo in person seems to under duplicate there are fewer bingo players amongst players of other games than would be expected, given the penetration rate for Bingo. Online Bingo shows a similar pattern of under duplication with the exception of slot machines (both online and offline). Proposition 3 stated that there would be greater cross-purchasing between online forms of gambling, than between online and offline. The overall levels of duplication can be characterised by the duplication coefficient, D. The D coefficient describes the amount cross-purchasing between brands (or in this case between forms of gambling) and controls for the effect of market penetration. A duplication coefficient of 2.0 would show that crosspurchases rates were on average twice the penetration rate. The D coefficients are shown in table 2. The coefficients for cross-purchasing between forms of offline gambling is 3.2, showing that the cross-purchase rates are around 3 times the penetration rates. The D coefficient for cross-purchases between online forms of gambling is indeed higher (about three times higher) than the others at 11.6. In other words, whilst the absolute proportions of cross purchase are lower in the lower left quadrant of table 2 than in the upper right quadrant, the cross-purchase levels in the lower left quadrant (online to online) are much higher than expected, given the penetration rates. Thus, proposition 3 is supported. Conclusions and Caveats The results suggest that online gambling does seem to conform to the patterns of seen in online buying behaviour. Online gambling is, in the main, a complement to offline gambling. This holds at the aggregate level (proposition1) and for individual forms of gambling (proposition 2). Very few online only gamblers were captured by the survey. The survey sample was randomly selected and sufficiently large to suggest that this finding should be generalizable (although, as with any recall-based measure, there is a risk of deliberate or accidental under-reporting). Individual forms of offline and online gambling tend to display higher levels of cross-purchasing with each other than with other types of gambling. That said, amongst online forms of gambling there are higher levels of crosspurchasing than amongst offline forms of gambling; the duplication coefficient for online gambling was almost four times higher than offline gambling (11.6 and 3.2 respectively). However, such a high coefficient could be due in part to the comparatively low penetration rates and to the high levels of cross-purchasing undertaken by offline casino game players. Overall, it seems that gambling behaviour, both online and offline displays very similar patterns to online and offline brand buying behaviour. However, there are clear partitions in the gambling market, with some forms of gambling displaying higher crosspurchasing than expected (e.g. sports betting and betting on horses) and others displaying much lower cross-purchasing (such as bingo). It would be interesting, from both a consumer behaviour perspective and a public policy perspective to see how far these findings hold following the relaxation of regulations regarding advertising gambling services and the rise of mobile commerce (including the development of smartphone gambling apps by both high street and online bookmakers) since the original survey was undertaken. As the data is based on recall, attention has been deliberately focussed on participation, rather than on expenditure or time spent gambling. Whilst such measures are undoubtedly important, their collection in a survey would be particularly subject to both recall and social desirability bias. Expenditure data is collected in the survey, but not for all types of gambling and not for online and offline gambling individually. It would, of course, be useful to investigate both frequency and expenditure levels, if the data were available.

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