Evaluating responsible gambling tools using behavioural tracking data.

Full text

EVALuATING RESPONSIBLE
GAMBLING TOOLS
uSING BEHAVIOuRAL
TRACKING DATA
ustomer data is the lifeblood of any company and
online gamblers provide tracking data that can be
used to compile customer profiles. Such data can tell
gambling operators which games their customers are
gambling on, for how long, how much money they are spending,
and what games are the profitable. This informaon can help in
the retenon of customers, and can also link up with exisng
customer databases and operang loyalty schemes.
Consequently, gaming companies can tailor its service to the
customer’s known interests.
On joining loyalty schemes, players supply lots of informaon
including name, address, telephone number, date of birth, and
gender. Those who operate online gambling sites are no different.
Basically, gambling operators can track the playing paerns of any
gambler. They arguably know more about the gambler’s playing
behaviour than the gamblers themselves. They are able to send
the gambler offers and redempon vouchers, complimentary
accounts, etc. These are done to enhance customer experience
(Griffiths & Wood, 2008a). However, more unscrupulous
operators have the means to ence known problem gamblers
back onto their premises with tailored freebies (such as the
inducement of “free” bets in the case of internet gambling).
However, it has been long argued that behavioural tracking data
can potenally be used to help idenfy problem gamblers rather
than exploit them, and to use behavioural tracking data for
research purposes (Griffiths & Wood, 2008b; Griffiths, Wood,
Parke & Parke, 2007).
Over the past decade, behavioural tracking has increasingly
been used in innovave ways by researchers. For instance, the
use of behavioural tracking data has been used to examine the
influence of structural characteriscs in slot machine gambling
(Leino et al., 2015), examine the amount of gambling behaviour
Dr. Mark Griffiths
Professor of Behavioural Addiction,
International Gaming Research Unit
Nottingham Trent University
41
CGiMAGAZINE.COM
SOCIAL RESPONSIBILITY
::
C

::
SOCIAL RESPONSIBILITY
engaged in when comparing gambling behaviour in alcohol and
non-alcohol serving venues (Leino et al., 2017), develop and
evaluate new measures of gambling intensity (i.e., theorecal loss
which is the amount of money staked by gamblers mulplied by
the probability of winning on a specific gambling acvity) (Auer
& Griffiths, 2014), idenfy behavioural markers of high-risk online
gambling (Braverman & Shaffer, 2012; Braverman et al., 2013;
Gray et al., 2012), compare online gamblers who self-exclude with
those that do not (Dragicevic et al., 2015), and test classic
psychological theories such as cognive dissonance (Auer &
Griffiths, 2017b). Other studies have used tracking data to
demonstrate that what money individuals say they have spent
gambling is different from their actual gambling behaviour with
all studies showing that the more someone gambles, the less
reliable they are about esmang what they have financially
spent gambling (Auer & Griffiths, 2017a; Braverman et al., 2014;
Wohl, Davis & Hollingshead, 2017).
Evaluaon of responsible gambling tools using tracking data
Another innovave use of behavioural tracking data is in
evaluang responsible gambling tools (e.g., limit-seng tools,
pop-up messages, personalized feedback, temporary self-
exclusions). Responsible gambling tools are a way of facilitang
players to gamble in a more responsible manner (Harris &
Griffiths, 2017). However, very few of these tools have been
evaluated empirically in real gambling environments. The next
secons examine the studies that have used behavioural tracking
data to evaluate limit seng, pop-up messaging, personalized
feedback, and specific behavioural tracking tools (i.e., PlayScan
and mentor).
Limit seng: Broda et al. (2008) examined the effects of
player deposit limits on Internet sports beng by customers of
bwin Interacve Entertainment. Their study examined 47,000
subscribers to bwin over a period of two years and compared the
behaviour of players who tried to exceed their deposit limit with
all other players. Deposit limit referred to the amount of money
deposited into a player’s spend account excluding any
accumulated winnings. At the me of inial data collecon in
2005, bwin set a mandatory deposit limit of no more than €1000
per day or €5000 per 30 days. Players could also set their own
deposit limits (per 30 days) below the mandatory limits. Overall,
the study found that less than 1% of the players (0.3%) aempted
to exceed their deposit limit. However, Wood and Griffiths (2010)
argued that the large mandatory limit may be the main reason
for this finding as LaPlante et al. (2008) noted that the majority
of online gamblers never reached the maximum deposit limit. In
fact, 95% of the players never deposited more than €1050 per 30
days (i.e., one-fih of the €5000 maximum). Furthermore,
LaPlante and colleagues did not disnguish between those who
aempted to exceed either their own personally set deposit limits
or mandatory limits.
Using the same dataset, Nelson et al. (2008) examined online
gamblers that voluntarily set limits on the bwin gambling website
over an 18-month period. A total of 567 online gamblers (out of
more than 47,000) used the voluntary limit-seng feature and
the findings demonstrated that limit-seng gamblers bet more
heavily and played a wider variety of games prior to seng limits.
Aer seng voluntary limits, these online gamblers reduced their
gambling acvity, but not the amount wagered per bet.
42
CGiMAGAZINE.COM

SOCIAL RESPONSIBILITY
::
A study by Auer and Griffiths (2013a) used behavioural
tracking data to evaluate whether the seng of voluntary me
and money limits helped players who gambled the most (i.e., the
most gambling intense individuals using ‘theorecal loss’ [Auer &
Griffiths, 2014]). Data were collected from a representave
random sample of 100,000 online players who gambled on the
win2day gambling website during a three-month test period. This
sample comprised 5,000 registered gamblers who chose to set
themselves limits while playing on win2day. During the
registraon process, there was a mandatory requirement for all
players to set me and cash-in limits. For instance, the player
could limit the daily, weekly and/or monthly cash-in amount and
the playing duraon. The laer could be limited per playing
session and/or per day. In the three-month test period, all
voluntary limit seng behaviour by online gamblers was tracked
and recorded for subsequent data analysis. Changes in gambling
behaviour were analysed overall and separately for casino, loery
and poker gambling.
The results of this study clearly showed that voluntary limit
seng had a specific and stascally significant effect on high
intensity gamblers (i.e., voluntary limit seng had the largest
effect on the most gaming intense players). More specifically, the
analysis showed that (in general) gaming intense players
specifically changed their behaviour in a posive way aer they
limited themselves with respect to both me and money spent.
Voluntary spending limits had the highest significant effect on
subsequent monetary spending among casino and loery
gamblers. Monetary spending among poker players significantly
decreased aer seng a voluntary me limit. Studies such as this
highlight the advantageous way in which behavioural tracking
methodologies can be used to provide results and insights that
would be highly difficult to show using other more tradional
methodologies.
Pop-up messaging: Auer, Malischnig and Griffiths (2014)
invesgated the effect of a pop-up message that appeared aer
1,000 consecuve online slot machine games had been played by
individuals during a single gambling session. The study analysed
800,000 gambling sessions (400,000 sessions before the pop-up
had been introduced and 200,000 aer the pop-up had been
introduced comprising around 50,000 online gamblers). The study
found that the pop-up message had a limited effect on a small
percentage of players. More specifically, prior to the pop-up
message being introduced, five gamblers ceased playing aer
1,000 consecuve spins of the online slot machine within a single
playing session (out of approximately 10,000 playing sessions).
Following the introducon of the pop-up message, 45 gamblers
ceased playing aer 1,000 consecuve spins (i.e., a nine-fold
increase in session cessaons). In the laer case, the number of
gamblers ceasing play was less than 1% of the gamblers who
played 1,000 games consecuvely.
In a follow-up study, Auer and Griffiths (2015a) argued that
the original pop-up message was very basic and that re-designing
the message using normave feedback and self-appraisal
feedback may increase the efficacy of gamblers ceasing play. As
in the previous study, the new enhanced pop-up message that
appeared within a single session aer a gambler had played 1,000
consecuve slot games. In the follow-up study, Auer and Griffiths
(2015) examined 1.6 million playing sessions comprising two
condions (i.e., simple pop-up message [800,000 slot machine
43
CGiMAGAZINE.COM

::
SOCIAL RESPONSIBILITY
sessions] versus an enhanced pop-up message [800,000 slot
machine sessions]) with approximately 70,000 online gamblers.
The study found that the message with enhanced content more
than doubled the number of players who ceased playing (1.39%
who received the enhanced pop-up compared to 0.67% who
received the simple pop-up). However, as in Auer et al.’s (2014)
previous study, the enhanced pop-up only influenced a small
number of gamblers to cease playing aer a long connuous
playing session.
Personalised feedback: Auer and Griffiths (2016) in a study of
the efficacy of personalised feedback, examined whether the use
of three types of informaon (i.e., personalized feedback,
normave feedback, and/or a recommendaon) could enable
players to gamble more responsibly as assessed using three
measures of gambling behaviour, i.e., theorecal loss, amount of
money wagered, and gross gaming revenue (i.e., net win/loss). By
manipulang the three forms of informaon, data from six
different groups of players were analysed. The parcipant sample
drawn from the populaon were those that had played at least
one game for money on the Norsk Tipping online plaorm
(Instaspill) during April 2015. A total of 17,452 players were
randomly selected from 69,631 players that fulfilled the selecon
criteria. Gambling acvity among the control group (who received
no personalized feedback, normave feedback or no
recommendaon) was also compared with the other five groups
that received informaon of some kind (personalized feedback,
normave feedback and/or a recommendaon). Compared to the
control group, all groups that received some kind of messaging
significantly reduced their gambling behaviour as assessed by
theorecal loss, amount of money wagered, and gross gaming
revenue. The results supported the hypothesis that personalized
behavioural feedback can enable behavioural change in gambling.
However, normave feedback did not appear change behaviour
significantly more than personalized feedback.
Behavioural tracking tools: Auer and Griffiths (2015)
evaluated the effecveness of mentor (a responsible gambling
tool that provides personalized feedback to players) among 1,015
online gamblers at a European online gambling site, and
compared their behaviour with matched controls (n=15,216). The
results showed that online gamblers receiving personalized
feedback spent significantly less me and money gambling
compared to controls that did not receive personalized feedback.
The results suggest that responsible gambling tools providing
personalized feedback may help the clientele of gambling
companies gamble more responsibly, and may be of help those
who gamble excessively to stay within their personal me and
money spending limits.
Wood and Wohl (2015) obtained data from 779 Svenska Spel
online players who received behavioural feedback using PlayScan.
Feedback to players took the form of a ‘traffic-light ’ risk rang
that was created via a proprietary algorithm (red=problemac
gambling, yellow=at-risk gambling, and green=no gambling
issues). In addion, expenditure data (i.e., amounts deposited and
gambled) were collected at three me points (i) the week of
PlayScan enrolment, (ii) the week following PlayScan enrolment,
and 24 weeks aer PlayScan enrolment. The findings indicated
that those players at-risk (yellow gamblers) who used PlayScan
significantly reduced the amounts of money both deposited and
gambled compared to those who did not use PlayScan. This effect
was also found the week following PlayScan enrolment as well as
the 24-week mark. Overall, the authors concluded that informing
at-risk gamblers about their gambling behaviour appeared to have
a desired impact on their subsequent monetary spending.
Concluding Comments
When it comes to studying online gambling behaviour,
behavioural tracking methodologies are an innovave way of
collecng data. Findings presented here suggest that limit seng
and personalised feedback appear to be responsible gambling
tools with high efficacy but that further replicaon studies are
needed. The studies evaluang pop-up messaging are far from
conclusive and suggest that on their own, pop-up messages only
help a very small percentage of within-session intense gamblers.
::
CGi
References
Auer, M. & Griffiths, M. D. (2013). Voluntary limit seng and player choice
in most intense online gamblers: An empirical study of gambling
44
CGiMAGAZINE.COM
<< The results suggest that responsible gambling
tools providing personalized feedback may help
the clientele of gambling companies gamble more
responsibly, and may be of help those who
gamble excessively to stay within their personal
time and money spending limits. >>

SOCIAL RESPONSIBILITY
::
behaviour. Journal of Gambling Studies, 29, 647-660.
Auer, M. & Griffiths, M. D. (2014). An empirical invesgaon of theorecal
loss and gambling intensity. Journal of Gambling Studies, 30, 879-887.
Auer, M. & Griffiths, M. D. (2015a). Tesng normave and self-appraisal
feedback in an online slot-machine pop-up message in a real-world
seng. Froners in Psychology, 6, 339. doi: 10.3389/fpsyg.2015.00339.
Auer, M. & Griffiths, M. D. (2015). The use of personalized behavioural
feedback for problemac online gamblers: An empirical study. Froners
in Psychology, 6, 1406. doi: 10.3389/fpsyg.2015.01406.
Auer, M. & Griffiths, M. D. (2016). Personalized behavioural feedback for
online gamblers: A real world empirical study. Froners in Psychology, 7,
1875. doi: 10.3389/fpsyg.2016.01875.
Auer, M. & Griffiths, M. D. (2017a). Self-reported losses versus actual
losses in online gambling: An empirical study. Journal of Gambling Studies,
33, 795-806.
Auer, M. & Griffiths, M. D. (2017b). Cognive dissonance, personalized
feedback, and online gambling behaviour: An exploratory study using
objecve tracking data and subjecve self-report. Internaonal Journal of
Mental Health and Addicon. Epub ahead of print. doi: 10.1007/s11469-
017-9808-1
Auer, M., Malischnig, D. & Griffiths, M. D. (2014). Is ‘pop-up’ messaging
in online slot machine gambling effecve? An empirical research note.
Journal of Gambling Issues, 29, 1-10.
Braverman, J., LaPlante, D. A., Nelson, S. E., Shaffer, H. J. (2013). Using
crossgame behavioural markers for early idenficaon of high-risk
Internet gamblers. Psychology of Addicve Behaviours, 27, 868–877.
Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling:
idenfying behavioural markers for high-risk Internet gambling. European
Journal of Public Health. 22, 273–278.
Braverman, J., Tom, M. A., & Shaffer, H. J. (2014). Accuracy of self-reported
versus actual online-gambling wins and losses. Psychological Assessment,
26, 865–877.
Broda, A., LaPlante, D. A., Nelson, S. E., LaBrie, R. A., Bosworth, L. B. &
Shaffer, H. J. (2008). Virtual harm reducon efforts for Internet gambling:
effects of deposit limits on actual Internet sports gambling behaviour.
Harm Reducon Journal, 5, 27.
Dragicevic, S., Percy, C., Kudic, A., & Parke J. (2015). A descripve analysis
of demographic and behavioural data from internet gamblers and those
who self-exclude from online gambling plaorms. Journal of Gambling
Studies, 31, 105–132.
Gray, H. M., LaPlante, D. A., & Shaffer, H. J. (2012). Behavioural
characteriscs of Internet gamblers who trigger corporate responsible
gambling intervenons. Psychology of Addicve Behaviours, 26,
527–535.
Griffiths, M. D. & Wood, R. T. A. (2008a). Gambling loyalty schemes:
Treading a fine line? Casino and Gaming Internaonal, 4(2), 105-108.
Griffiths, M. D. & Wood, R. T. A. (2008b). Responsible gaming and best
pracce: How can academics help? Casino and Gaming Internaonal, 4(1),
107-112.
Griffiths, M. D., Wood, R. T. A., Parke, J. & Parke, A. (2007). Gaming
research and best pracce: Gaming industry, social responsibility and
academia. Casino and Gaming Internaonal, 3, 97-103.
Harris, A., & Griffiths, M. D. (2017). A crical review of the harm-
minimisaon tools available for electronic gambling. Journal of Gambling
Studies, 33, 187-221.
LaPlante, D. A., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008).
Populaon trends in Internet sports gambling. Computers in Human
Behaviour, 24(5), 2399–2414.
Leino, T., Sagoe, D., Griffiths, M. D., Mentzoni, R.A., Pallesen, S., & Molde,
H. (2017). Gambling behaviour in alcohol-serving and non-alcohol-serving
venues: A study of electronic gaming machine players using account
records. Addicon Research and Theory, 25, 201-207.
Leino, T., Torsheim, T., Blaszczynski, A., Griffiths, M.D., Mentzoni, R.,
Pallesen, S. & Molde, H. (2015). The relaonship between structural
characteriscs and gambling behaviour: A populaon based study. Journal
of Gambling Studies, 31, 1297-1315.
Nelson, S. E., LaPlante, D. A., Peller, A. J., Schumann, A., LaBrie, R. A., &
Shaffer, H. J. (2008). Real limits in the virtual world: Self-liming behaviour
of Internet gamblers. Journal of Gambling Studies, 24(4), 463-477.
Wohl, M. J. A., Davis, C. G., & Hollingshead, S. J. (2017). How much have
you won or lost? Personalized behavioural feedback about gambling
expenditures regulates play. Computers in Human Behaviour, 70, 437–
455.
Wood, R. T. A. & Griffiths, M. D. (2010). Social responsibility in online
gambling: Voluntary limit seng. World Online Gambling Law Report,
9(11), 10-11.
Wood, R. T. A., & Wohl, M. J. (2015). Assessing the effecveness of a
responsible gambling behavioural feedback tool for reducing the gambling
expenditure of at-risk players. Internaonal Gambling Studies, 15(2), 1-
16.
45
CGiMAGAZINE.COM
Dr. Mark Griffiths is Disnguished Professor of Behavioural
Addicon at Nongham Trent University, and Director of
the Internaonal Gaming Research Unit. He is
internaonally known for his work into gambling and gaming
addicons. He has published over 650 refereed research
papers, five books, 150+ book chapters and over 1500 other
arcles. He has won 18 naonal/internaonal awards for his
work including the US Naonal Council on Problem
Gambling Lifeme Research Award (2013).
DR. MARK GRIFFITHS