4. #10. Focus on the player
• Build a player-centric view of game design
• Model your expected user base
• Understand how different players interact with the
game
• Build a player level revenue forecast
5. #10. Focus on the player
• Aim to make the players enjoy the gameplay – in the
different ways they play – sociable, completers,
explorers
• Length of gameplay is generally a good indication of
monetisation
• Analytics allows the game to be tailored to individual
types of players
7. #9. Collect the right data
• Understand the information you want to analyse
• Focus on player level, not game level information
• Identify significant events in the game
• Build good data integrity
10. #8. Understand the metrics
• Metrics generally means dashboards
• This provides historic information
• It tells you the health of your game
• Use metrics to identify the areas of the
game that need focus
11. #8. Understand the metrics
• Stickiness
• ARPU / ARPPU
• Payment Size
• Time to First & Second Payment
• Virality
• Demographics
• Paying Players by Country
16. #6. Social interaction
• Identify player ‘bridges’
• Isolated players
• Gaps and holes, leading to group fragmentation
• Reward highly influential players
• Manage cohesion, caused by influential players
• Reconnect isolated players
• Build bridges and connect sub networks
18. #5. Identify player value
• Total lifetime value
• This is a factor of both the revenue directly and
indirectly attributed to the player
• This can be predicted to identify potential value
19. #5. Identify player value
• Time to first payment (2-4 weeks drive high LTV)
• Payment patterns (regular, increasing, decreasing)
• Triggers for first spend (why now?)
• Time lag to second spend (be quick)
• Reasons for reactivation (paying players stick around)
• All actionable to raise LTV
• Overlay profiles to refine targets
21. #4. Pattern analysis
1st Event 2nd Event 3rd Event
61%
12%
9%
Challenge Start
Intervention here Gifted item
Use event order to predict and
encourage next action Visited Home
Invite Neighbour
Bought Item
22. #4. Pattern analysis
• Pattern analysis is a powerful technique
• Using it allows behaviours to be tracked and
identified
• This can be used to react to next best option
• This can also be used to identify actions that precede
abandonment
24. #3. Predictive Modelling
• Ability to predict player behaviour
• Identify players likely to undertake an action if
encouraged
• Provide the means to deliver a marketing
intervention that is:
– Timely
– Personal
– Appropriate
26. #2. Actionable results
• The key to Analytics is to provide the tools to
improve the game
• This can be:
– Improve Gameplay
– Increase Revenue
– Reduce Abandonment
– Increase Retention
– Reward Loyalty
27. #2. Actionable results
• Analytics provides the means to identify these traits
• To group players into manageable segments
• To predict their future behaviour
• To intervene to change behaviours and move the
graph
29. #1. Analytics is a tool
• Analytics is a tool
• Like a good 3D engine, used well it can improve a
game
• But turn the findings into actions
• And measure the results – “TEST & LEARN”
• You can raise your game revenues by more than 30%!