An Investigation into the Characteristics and Gambling Habits of Bingo Players in the UK
Bingo is a popular pastime in the UK, and has grown as an industry over recent years, moving away from local community centres to dedicated bingo halls and numerous online sites. As with all industries, to ensure that there is a continual renewal of customers, those in the bingo industry must be able to target their marketing strategies effectively. This means that they need to be able to identify who their target audience is for them to be able to deliver an effective marketing campaign which is aimed predominantly at those people (Aaker et al., 2000).
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Studies in the past have suggested that bingo is considered to be a rather low-level leisure activity. As a result, it has also been previously associated heavily with being a pastime of predominantly working class women (Dixey, 1987). In addition, it is generally associated with pensioners, largely due to the sedentary but social nature of the game (Cousins & Witcher, 2007). Although this may have been the case a number of years ago, there is little recent empirical evidence to determine whether this is still the main market for bingo. Given the changing nature of the game and its delivery, it is plausible that changes in the main demographic of players may also be in a process of change. This study therefore uses pre-collected data to evaluate whether this may be the case.
Rationale of Study
Gathering information on the demographics of people who play bingo, or are interested in playing bingo, is likely to be useful to bingo halls for targeting their marketing campaigns. In addition, with the advent of online gambling, understanding whether current online gamblers would be likely to participate in bingo games if they were available may be important in design of online gaming sites. Marketing opportunities online may also be improved if evidence is available of the demographics of the target audience. Consideration will also be given to the expenditure of different demographic groups on bingo, as this information may be useful in the industry for determining customer value.
Based on the previous literature, it is hypothesised that the main demographic of bingo players will be predominantly women, but that there will be a substantial number of younger players. It is also expected that there will be an association between those gaming online and those playing bingo. Finally, it is expected that there will be no difference in the expenditure on bingo between age groups.
Origins of the Data
The data which is used in this study was taken from the British Gambling Prevalence Study 2007, which was the largest study of its kind to be conducted in the UK to the present date. The study was commissioned by the National Centre for Social Research and aimed to collect information on gambling habits and demographics in order to assess the prevalence of problem gambling within the UK.
The study was designed to be representative of all adults in the UK aged 16 years and older who were living within private households.
All residences in the UK were separated into Primary Sampling Units (PSUs) and 317 of these were randomly selected for the study. The probability of selecting each was apportioned according to the number of addresses within them, but no other demographics were taken into account. For each PSU which was selected, 32 addresses were selected randomly, and this gave a total of 10,144 addresses. After non-completion, the total sample available in the data-set was 9003.
The study was conducted using a multiple-choice questionnaire. This clearly explained at the beginning the goal of the study, and explained clearly how the survey form should be completed. A coding document was then compiled to code the answers into statistical software packages, and this was included alongside the data-set. A copy of the questionnaire which was used is available from //www.data-archive.ac.uk/findingData/snDescription.asp?sn=5836. The data which was included in the data-set was already weighted to account for non-response and bias, and this was completed by the researchers who completed the data collection and original analysis.
Proportion of Bingo Players in the Sample
The results of the analysis indicate that only a small minority of the population sampled had played bingo in the last twelve months, with a total of 7.5% of the sample. This is shown graphically in Figure 1. Table 2 then details the frequency with which that small group had played bingo. It may be seen that 45% of those who had played bingo in the last twelve months had done so less than once a month. A total of 36.4% of those who played bingo in the last twelve months had done so once a week or more, and a further 15.6% reported playing at least once a month. This information is then presented graphically in Figure 2.
Table 1. The frequency with which respondents reported playing bingo in the last twelve months.
|Frequency of bingo playing in the last year||Number of participants||Percentage of those who had played bingo in the last year|
|Twice or more a week||92||13.7|
|Once a week||152||22.7|
|Once a month||105||15.6|
|At least once a year||302||45.0|
For those who had played bingo in the last twelve months, the mean age was 47.78 with a standard deviation of 18.08, while it was 47.75 for those who had not played bingo in the last twelve months, with a standard deviation of 18.33. Performing an independent two-sample t-test on the data produced a p-value of 0.963, which indicates that the null hypothesis may not be rejected. This means that there is no significant difference between the mean age of the two groups at the 95% confidence level.
Age of Bingo Players
Table 2 displays the mean age of each group when those playing bingo within the last twelve months were grouped according to frequency of playing. Alongside the mean, the standard deviation is also given. This information is presented in the box-plot in Figure 3. This shows that the mean age of the players appears to increase as the frequency of playing increases. In addition, it would also appear that the variation in age is smallest in the group who play at least twice a week. Performing a one-way ANOVA analysis indicates that there is an association between age and the frequency of playing bingo and that the null hypothesis of no association may be rejected at the 5% level (p < .000).
Table 2. Mean and standard deviation of the age of players grouped according to the frequency with which they play bingo.
|Frequency of bingo playing in the last year||Mean age||Standard deviation|
|Twice or more a week||59.64||15.21|
|Once a week||55.31||17.83|
|Once a month||40.82||17.62|
|At least once a year||42.33||15.82|
|Did not play bingo in last twelve months||47.75||18.33|
Gender of Bingo Players
Of those who had played bingo in the last twelve months, 71.4% were female, and this percentage is show graphically in Figure 4. A chi-squared analysis indicates that there is evidence that the null hypothesis may be rejected at the 5% level (p < .000). This means that there is evidence that gender is associated with whether participants had played bingo in the last twelve months.
Player Expenditure on Bingo
Expenditure and Gender
Analysis of the data shows that females who had played bingo in the last twelve months lost a mean of £319 over the previous 7 days, with standard deviation 506. In contrast, males lost a mean of £60.50 over the 7 days, with a standard deviation of 4.95. An independent samples t-test revealed that there is evidence at the 0.95 significance level against the null hypothesis of no association. This therefore indicates that there is a significant difference between the amount lost by males and females (p = 0.045).
Expenditure and Age
Figure 5 shows a scatter-plot of the total amount which each respondent reported losing at bingo in the last seven days plotted against their age. The black dots represent female participants while red dots represent males. It would appear from this plot that there is no association between the amount of money lost at bingo and the age of the player, for either males or females. A bivariate regression analysis of this data confirms this. It indicated that there was no evidence against the null hypothesis of no association, and so age was not found to be a significant predictor of the amount lost at bingo over the seven day period (p = .489).
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Figure 6 presents a scatter-plot of age against expenditure on bingo over the previous seven days when winnings are also taken into account in addition to losses. There was far more data available for this analysis, but it would still appear that there is no particular association between age and expenditure on bingo, for either males or females. This was confirmed by regression analysis, which indicated there was no evidence against the null hypothesis of no association (p = .187). Therefore it was concluded that there was no association between age and overall expenditure on bingo over the previous week.
Expenditure and Frequency of Playing Bingo
Figure 7 presents a box-plot of the amounts lost at bingo according to the frequency of playing bingo in the last twelve months. It would appear from this graph that those playing twice a week lost less than those who played less often, as the mean is lower and the variation is less. An ANOVA analysis however indicated that there was no evidence against the null hypothesis of association. This indicates that there is therefore no association between the frequency of playing bingo over the last twelve months and the amount lost at bingo (p = .925).
Figure 8 presents a similar box-plot analysis which takes account of the winnings of players in addition to losses. When comparing the different frequency of playing groups it would appear that overall expenditure appears to be relatively consistent. The variation in expenditure does however appear to be somewhat larger in the group which play two times or more a week. One-way ANOVA analysis of this data confirms that there is no evidence against the null hypothesis of no association (p = .731). Therefore it may be concluded that frequency of playing bingo over the last twelve months did not impact on the overall expenditure of the player on bingo over the last week.
Online Gamblers and Bingo
Table 3 presents a cross-tabulation of the number of participants who reported having played bingo over the previous twelve months and those who reported having gambled online over the previous twelve months. This shows that of those who had reported playing bingo in the last twelve months, only 8% reported gambling online during that time (Figure 9). In contrast, 26.3% of those who had gambled online over the last twelve months reported that they had also played bingo during that period (Figure 10). A chi-squared analysis of this data indicates that there is strong evidence against the null hypothesis of no association (p < .000). It may therefore be concluded that there is an association between gambling online and playing bingo over the previous twelve month period.
Table 3. The number of respondents who had gambled online over the previous twelve months and the number who had played bingo over the previous twelve months.
|Played bingo in last twelve months|
|Gambled online in last twelve months||Yes||54||151||205|
Figure 11 presents a scatter-plot of the net expenditure on bingo in comparison to the net expenditure on online gambling, both over the previous seven day period. From this chart it would appear that there is no association between the two. A regression analysis confirms that there is no evidence against the null hypothesis of no association (p = .882). Therefore it must be concluded that there is no significant association between the total expenditure of the respondents on online gambling and their total expenditure on bingo over the same time period.
From this study it may be seen that there are certain demographic characteristics which are associated with playing bingo. It would appear from the results that bingo players are in the minority, with only 7.5% of the population estimated to have played in 2007, and only 2.7% of the population playing once a week or more. As hypothesised at the beginning, there was no particular age group which was associated with playing bingo. There is however evidence that it is an older age group which is associated with playing bingo regularly, with the mean age being in the mid- to late-fifties for those playing once a week or more. Despite this, there was no association between age and the amount spent on bingo in a week. This indicates that older people are still the main demographic for the industry but that they are not particularly more valuable than younger people. They may however be more valuable if their spend is consistent week after week, when compared to younger players who may play only once a month or less.
There was also no association between frequency of playing and expenditure within the week. This is an important implication as it suggests that customers who can be attracted to regularly play bingo will be consistent with their spending, and not reduce spending as they play more over the year.
Almost three quarters of players were female, and it was also females who were associated with much larger expenditure on bingo. This finding is however somewhat limited, as only information on the expenditure of two males was available for analysis. Therefore this finding may not have a high statistical power.
Finally, there is evidence that there is an association between bingo players and those gambling online. This is important as it suggests that there is potential in both advertising for bingo online and also in online bingo rooms. The extent to which an individual gambles online does not however appear to be associated with how much they spend on bingo. Therefore online players may not be more valuable than those attracted through offline methods.
Although this study has provided some useful insights for those interested in the demographics of bingo players, there are some limitations. One of the main limitations is that there was little social information available in the study which related to income and social status of the respondents. It is possible that this may impact on gambling habits, including playing bingo (Barry et al., 2007). This may be important in areas where there are either large levels of population in higher or lower social classes. Therefore further market research in particular areas may be useful in determining the potential for marketing in that particular area.
Aaker, J.L., Brumbaugh, A.M. & Grier, S.A. (2000) Nontarget markets and viewer distinctiveness: The impact of target marketing on advertising. Journal of Consumer Psychology, 9(3), 127-140.
Barry, D.T., Maciejewski, P.K., Desai, R.A. & Potenza, M.N. (2007) Income differences and recreational gambling. Journal of Addiction Medicine, 1(3), 145-153.
Cousins, S.O. & Witcher, C.S.G. (2007) Who plays bingo in later life? The sedentary lifestyles of ‘little old ladies’. Journal of Gambling Studies, 23(1), 95-112.
Dixey, R. (1987) It’s a great feeling when you win: Women and bingo. Leisure Studies, 6(2), 199-214.
National Centre for Social Research (2007) British Gambling Prevalence Survey 2007. Available [online] from: //www.data-archive.ac.uk/findingData/snDescription.asp?sn=5836 [Accessed 12/12/2008].