First presented at the 12th GIAF at https://deltadna.com/giaf/uk/?utm_source=slideshare&utm_medium=company_page&utm_content=GIAF_London&utm_campaign=GIAF Learn how tweaking randomness in social casino games can help improve both retention and engagement of new players and monetization of the high roller ones. With this model, new players have a new better randomized experience based on how did they perform in the past, and the most experienced ones have better challenges to keep engaged to the game.

1. Tweaking Randomness
in Social Casino
Juan Gabriel Gomila Salas
Business Intelligence Consultant
CEO at frogames.es
/joanby @Joan_Byhttp://juangabrielgomila.com

2. Outline of the talk
A little bit about me
The problem
The first idea
The improved solution: Slot Machines
And does it work?

3. A little bit about me

4. A little about me
HL +Master Degree in
Mathematics (2006-2011)
Data Scientist & Game
Designer at PlaySpace from
2012-2015
Developing 40+ apps & games
at frogames.es
Teaching Mathematics at UIB, DB
& Game Design at EDIB and
online instructor at Udemy

5. The problem

6. High
Acquisition
Costs
Users cost now up to 5 times
than before (1-5$)
Marketing teams need to
optimize their budget

7. Need to retain
new players
With these costs, players need
to be well retained to reach
CPI < LTV

8. New players distribution
tends to look like this

9. but we’d like to see something like that

10. A little story
Back in 2012, when I was Data Scientist at PlaySpace, we were
analyzing ARM problems in their video games.

11. A little story
At Playspace, we were looking for ways to improve their
best games: Ludo & Bingo

12. A little story
And we indeed discovered one big fact

13. PEOPLE DON’T
WANT TO LOSE

14. People don’t want to lose
New players who won their first match:
Came back to the game up to 3 times more
Were retained more time than the rest
Payed later but more often and with higher ARPPU

15. A little story
On the other hand, people started paying as soon as
they ran out of coins

16. A little story
And kept paying each time they weren’t able to start a
new game

17. A little story
So we found the second big fact

18. WE NEEDED TO MAKE PEOPLE
LOSE THEIR VIRTUAL GOODS

19. We want
to win
We want
you to lose

20. How to combine them both?
People want to win
We need to make them lose
How can this be fit together in a multiplayer real time
environment?

21. The first approach

22. Segmentation
We segmented our DB depending on different
parameters to classify people int0 different groups
High
rollers
25%
Fighters
30%
Losers
45%

23. Segmentation
Our machine learning had, among other data:
Win Ration
RTP
Time between games
Games per week
Time to 1st payment
Number of shares
Number of friends

24. The ideal world
Once people are classified, the idea is to move them
from the borders of the distribution to the middle:
High
rollers
-
Fighters
100%
Losers
-

25. The real world
But things are never perfect:
losers vs losers make the loser even more loser
losers with little games go to high rollers with 1 win
But we’re able to normalize our players distribution
High
rollers
-
Fighters
100%
Losers
-
High
rollers
10%
Fighters
80%
Losers
10%

26. There is a spot for new
players
Of course, new players are initially
marked as losers by the algorithm.
The longer they play for, the better
set up they become.
This keeps going on until we have a
normal distribution.

27. So,
how can we do it?

28. On dice games
p(X=x) = 1/n
where x in {1,2,…n}
and n = #faces of the dice

29. On dice games
For losers, if they need a x
to make a good move, we
tweak uniform distribution:
p(X=x) = 2/n

30. On dice games
For high rollers, if they need
a x to make a good move, we
tweak uniform distribution:
p(X=x) = 1/2n

31. On card
games
p(X=x) = 1/n
where x in the set
of available cards
and n = #cards

32. On card
games
Let S be any subset of
suitable cards for the next
move from all the
remaining ones of X

33. On card
games
For losers, if they need a
s€S, we make this subset
bigger:
p(X=s) = 2/n

34. On card
games
For high rollers, if they
need a s€S, we make this
subset smaller:
p(X=s) = 1/2n

35. Conclusion
As Sir Francis Galton said on 1870,
It all regresses to the mean
Losers improve their winning ration and thus their
experience,
High rollers find the challenges they need to keep
themselves on the wave.

36. The improved solution:
Slot Machines

40. What about Slots Machines?
Let S = set of normal symbols of the slot machine,
Let W = WILD, S = SCATTER and B = BONUS.
Let M and N be the number of rows and reels of the
slot machine

41. What about Slots Machines?
For every s in S, lets define:
- w(si) = the weight of s on i-th reel
- x(si) = the reward of i equals s symbols (aka paytable)
- wi = the total weight of i-th reel

42. This time is not so easy
p(x(si)) = ————————————————————————-
w(s1)*(w(s2)+w(W2))… (w(si)+ w(Wi))
w1*w2*…*wi
For s in S, the chances of having exactly i
s symbols on a paying line is

43. So, what can we do to
improve our chances?

44. I’m sure you thought about
that!
We double the weight of the wild symbol w(Wi), so
now we have
w(s1)*(w(s2)+2w(W2))… (w(si)+ 2w(Wi))
w1*(w2+w(W2))…*(wi+w(Wi))
p(x(si)) = ————————————————————————-

46. I’m sure you thought about
that!
We half the weight of the wild symbol w(Wi), so now
we have
w(s1)*(w(s2)+w(W2)/2)… (w(si)+ w(Wi)/2)
w1*(w2+w(W2))…*(wi+w(Wi))
p(x(si)) = ———————————————————————————-

47. Is the denominator a
problem?
Not really…
w(Wi) << wi ——> wi+w(Wi)~wi
w(Wi) ~ w(si) ——> w(si)+2w(Wi) increases
This means the chances of getting that
combination on a pay line increases exponentially!

48. Are there other options?
For sure, we can simply:
Double the weight of the Scatter (really increases
wins due to its nature!)
Double the weight of the Bonus to engage the user
Randomly put on scene sticky or stacked Wilds

49. And now we can make a
web of slot machines
High
rollers
-
Fighters
100%
Losers
-
High
RTP
>100%
Normal
RTP+Hit
95.5%
Low RTP
<80%
Low Scatter
Hit Rate
<2%
Low Bonus
Hit Rate
<1%
High
Scatter Hit Rate
>5%
High
Bonus Hit Rate
>3%

50. And does it work?
Well, see it for yourself!

53. Conclusions
Reduced outliers in our players distribution
Modeled behaviors to the mean
Made new users perform better (against other users!)
so we improved their retention and engagement
Experts are now challenged and they are now more
engaged (and probably well monetised!)

54. Improvements
Take it further to massive social casino games like a
bingo room or multiplayer social slots
Smooth the results with players that have little data
(it can go from loser to high roller in just one
game). Law of large number applies!
Not just play with doubles and halves! Tune the
algorithm with AB testing! Why not use sqrt(2)??

55. Who could use this model?

56. Any questions?
– Juan Gabriel Gomila Salas

57. Thanks for your time
/joanby
@Joan_By
Juan Gabriel Gomila
joanby
http://juangabrielgomila.com http://frogames.es
@frogames_sl
/froggames