1. Finding Potential for
Monetization in Social Casino
Michał Witkowski
GameDesire Ltd.
Head of Product Analytics

2. About company
• GameDesire Ltd. – est. 2004 (previously known as
Ganymede)
• The leading social casino games developer in
Poland
• Key products:
• Snooker Live Pro
• Pool Live Pro
• Poker Live Pro
• GameDesire.com
• Key markets:
• Poland
• Brasil
• USA

3. About me
• Game data analyst by profession
• Head of Product Analytics at GameDesire Limited
• Psychologist and researcher by education
• Jagiellonian University in Krakow
• LangUsta Lab for Psychology of Language
• Gamer by passion
• PC games (rouglikes, RPG, turn-based strategy)
• Narrative RPGs (World of Darkness, Neuroshima)
• Modern board games
• Chess

4. Introduction
• Main goal:
• Social casino vs other casual games – similarities and differences
• Deriving possible strategies from data
• Presentation plan:
1. Information about analysed games and data scope
2. Engagement metrics
3. Basic financial metrics
4. Lifetime value analysis

5. Social casino games
In-house data from the following
games:
• Poker Texas Hold’em („Poker Texas”)
• Golden Reels Casino Slots („Slots”)
• Bingo

6. Non-casino casual games
In-house data from the
following games:
• Pool Live Pro
• Snooker Live Pro
• Mahjong
• Last Temple

7. Design of the analysis
• Players group: Web (non-mobile) players and their payments
only. Extra focus on FB
• Data sources:
• In-house analytical system
• Engagement and monetisation: representative time period from 2015,
allowing for comparison across the products
• Lifetime value analysis: 2014-2015 payments data

8. Traffic metrics
0.0% 100.0%
Slots
Poker Texas
Non-casino
Bingo
Traffic metrics
FB / all partners comparison
Reactivations New registrations
Commentary:
• Observation: Newly registered players overflow in Slots and Poker
• Possible action: Take 1st session funnel to perfection
• Observation : Little new registrations on Bingo
• Possible action : Work on reactivations and loyalty of your Bingo
players
New regsitrations / MAU
Reactivations / MAU
New regsitrations / MAU
Reactivations / MAU
FBAll
Traffic metrics
Benchmarked against non-casino games
Poker Texas Slots Non-casino Bingo

9. Cohort retention
D7 / D1 retention ratio
D28 / D1 retention ratio
D7 / D1 retention ratio
D28 / D1 retention ratio
FBAll
D1 / 7 / 28 cohort retention comparison
Split per game and affiliate
Slots Poker Texas Bingo Non-casino
• Cohort vs rolling retention
• 100/50/25 rule
• Is the rule present in social
casino?
• Repeatability
• Many similar games on the
market
• Competitive market

10. Cohort retention
D1 retention
D7 retention
D28 retention
D1 retention
D7 retention
D28 retention
FBAll
D1 / 7 / 28 cohort retention
Benchmarked against non-casino games
Non-casino Slots Poker Texas Bingo
Commentary:
• Observation: low long-term retention
• Possible action: long-term retention mechanism
• Observation: low FB retention
• Possible action: dedicated websites? Notification
mastery?
0.0% 100.0%
Non-casino
Poker Texas
Bingo
Slots
Players' retention
FB / all partners comparison
D1 retention D7 retention D28 retention

11. Game sessions metrics
Session time
Sessions played daily per user
Session time
Sessions played daily per user
FBAll
Game sessions metrics
Benchmarked against non-casino games
Poker Texas Slots Bingo Non-casino
0.0% 100.0%
Non-casino
Bingo
Poker Texas
Slots
Game sessionmetrics
FB / all partners comparison
Sessions played daily per user Session time
Commentary:
• Observation: Short Slots and Bingo sessions
• Possible action: Show as much as possible, as quickly
as possible (Tradeoff: information overflow / content
hype)

12. Basic financial metrics
Conversion rate
ARPPU
ARPU
All
Basic financial metrics
Benchmarked against non-casino games (all partners)
Non-casino Slots Poker Texas Bingo
Conversion rate
ARPPU
ARPU
FB
Basic financial metrics
Benchmarked against non-casino games (FB only)
Non-casino Slots Poker Texas Bingo

13. Basic financial metrics
0.0% 100.0%
Conversion rate
ARPPU
ARPU
Basic financial metrics
FB / all partners comparison
Non-casino Slots Poker Texas Bingo
• Observation: Social casino relatively
less profitable on FB
• Possible action: Use dedicated
gaming websites / platforms

14. Lifetime value analysis
Probability of first payment per lifetime day
Benchmark against non-casino games
Bingo Non-casino games Poker Texas Slots
Bingo
Non-casino games
Slots
Poker Texas
Days to first payment (median)
Commentary:
• Observation: payers in social casino are more
impulsive
• Possible action: Unethical and short-term, but
possiblyprofitable high early monetisation pressure
• Observation:Most Poker payers emerge by D2
• Possible action:Early VIP offers foryour payers

15. Lifetime value analysis
DAY 0 DAY 30 DAY 60 DAY 90 DAY 120 DAY 150 DAY 180
LTV realisation curve
Bingo Slots Poker Texas Non-casino games
• Observation: Poker players tend to
realise their LTV much earlier than
other games
• Possible actions: Experiment with
1st session monetisation
mechanisms; create attractive early
payments offers
• Observation: Bingo and Slots
payers realise their LTV like casual
payers
• Possible action: create offers that
would be attractive for mid- and
long-term players

16. Lifetime value analysis
Probability of becoming a multiple-paying user per day
of first transaction made
Bingo Non-casino games Poker Texas Slots
Average amount of payments per first payment date
Benchmarked against non-casino games
Bingo Non-casino games Poker Texas Slots
Commentary:
• Observation: The most valuable players in Bingo deposit first after 6-9
months of playing
• Possible action: treat your acquired traffic as a long-term investment,
work on players’ loyalty and long-term retention
• Observation: Tradeoff in payments - pay early or pay frequently
• Possible actions: choose which one works best for you

17. Lifetime value analysis
DAY 1+ DAY 11+ DAY 31+
Percentage of a multiply-paying users yet to make their first payment
Non-casino games Bingo Slots Poker Texas
Commentary:
• Observation: Multiple Poker
payers emerge very early on
• Possible actions: VIP clubs and
special offers; quick contact with
paying users
• Observation: Multiple Bingo
payers emerge relatively late
• Possible action: Focus on loyalty
rather than quick monetisation

18. Summary - Bingo
• Traffic characteristics:
• Loyal and conservative users
• The most casual of the three social casino games
• Engagement strategy:
• Think and plan in a very long term
• Work towards loyalty and reactivations
• Monetisation strategy:
• Don’t pressure monetisation too early, focus on retention
• After the critical mass is reached, the game will produce steady income

19. Summary – Poker Texas
• Traffic characteristics:
• Impulsive, highly-monetising users
• Unstable, leaving often and usually early
• Engagement strategy:
• Workingon short-term retention will ensure the quickest and highest profit of all
analysed games
• Workingon long-term retention might be very difficult (competition), but will help in
keeping long-term payers satisfied and faithful
• Monetisation strategy:
• Important tradeoff: quick payments vs multiple payments
• D0 is critical in Poker Texas in generating LTV
• BUT most of the early-paying users don’t deposit again, as opposed to D1+ first-time
payers
• Rely on VIP clubs and / or special offers to keep your paying users engaged
• Consider using a dedicated website for Poker

20. Summary - Slots
• Traffic characteristics:
• Moderately impulsive payers
• Play for short preiods of time
• Engagement strategy:
• Design your game to show off the important content as early and as
concisely as possible
• Focus on mid-term retention
• Monetisation startegy:
• Using monetisation pressure after a few sessions will be most
profitable
• Use mid-term retention mechanisms to get your players there

21. The end!
Thank you for attention!
Do you have any questions?