LTV predictions for Growth and UA activities

Understanding, optimising and
predicting LTV based on data in
mobile gaming
Robert Magyar
Head of Data Science
for GameCamp
August 13th 2020

Who am I?
I am passionate about helping game studios all
around the world to grow their games.
Across different genres (match3, RPG games, racing
games, etc) we have:
● Analyzed data of 1B+ players.
● Optimized millions of dollars in media spend.
● Brought millions of dollars in revenue uplift.
Robert Magyar
Head of Data Science & General
Manager at Superscale

What is SuperScale?
We are the growth partner for
world's top game developers
focusing on UA, business
analytics & monetization.

Usual problem that game studios have
Day
Situation
1. studio does UA
2. it works well

Day
Situation
1. studio does UA
2. it works well
3. spend is increased
4. performance drops

Situation
1. studio does UA
2. it works well
3. spend is increased
4. performance drops
5. recoup too far away
6. UA cannot scale further
Day
Increase in spend
→ lower quality players and higher CPI

How to combat scaling issues in your game?

How can games really scale their revenue?

Optimize LTV of your
playerbase
Optimize engagement
metrics (retention etc)
Continuous
Scaling
Bring quality
players through
User Acquisition

playerbase
Optimize engagement
Continuous
Scaling
Bring quality
players through
User Acquisition
… without development of new game features / game modes & without adding
unique things to the game

1. Measure
2. Predict
3. Optimize
playerbase
Optimize engagement
Continuous
Scaling
Bring quality
players through
User Acquisition
… without development of new game features / game modes & without adding
unique things to the game
1. Measure
2. Predict
3. Optimize

2 areas to make the biggest impact using data
1. UA optimization through data - Marketing & Analytics working together
● predict results of UA actions, enable seeing UA trends and patterns
● target better quality players (lookalikes creation based on playerbase
segmentation)

segmentation)
2. IAP LTV optimization through data
● Optimization of rotating / seasonal / progression offers
● If IAP makes 5% revenue -> you need 300% improvement to have
signiﬁcant impact on overall revenue

segmentation)
2. IAP LTV optimization through data
● Optimization of rotating / seasonal / progression offers
● If IAP makes 5% revenue -> you need 300% improvement to have
signiﬁcant impact on overall revenue
Doing both at the same time is the key (synergizing).

Improving LTV of attributed
players
Marketing & Analytics working together

Marketing without proper
analytics support?
Issues that we frequently see:
- Many different sources of data
- Hard to drill down (campaign, cohorts, creatives, adsets,
countries, platforms etc)
- No accurate prediction system

Marketing & analytics working together
Short term benefits
- better informed day-to-day decisions about changes in creatives, campaigns, ad sets etc
Mid term benefits
- understand if weekly and monthly UA strategy regarding targeting and creatives work
Long term benefits
- de-risking scaling - spend budgeting and understanding recoup/breakeven day

How are your creatives doing?
Improving creative targeting => bringing higher quality players => improving LTV
Creatives with higher D3 and D7 ROAS brings higher quality players to your game. It is important to spot them as
soon as possible.
Important to see outlier as soon as
possible and act on that

How are your strategies performing?
Quickly identifying better strategies => bringing higher quality players => improving LTV
Comparison of ROAS (Return On Ad Spend) benchmarks can give you great idea how your weekly strategies perform.
Starting to see decline in D7 and D28
ROAS

What is actually UA recoup/breakeven day?
Identifying the most long-term proﬁtable campaigns and reallocating spend to them → improving LTV
Yearly prediction can help with:
- Spend budgeting
- Spend allocation (prediction on the campaign, adset or creative level)
CPI
Break-even day
- 16th day
Proﬁt Threshold

Connecting LTV with spend strategy
Understanding possible spend increase to achieve desired recoup → improving LTV
Increase in spend means increase in CPI. You can estimate what CPI you get when you increase your spend and
compare it to your LTV to understand UA payback.

LTV/ROAS predictions & decision making
[Careful] Your prediction model can overestimate the revenue
● This can result in overconﬁdence in the current performance and overspending, thus affecting UA manager
decision making
Model error (%, * 100)
Numberofcohorts
Underestimating
real revenue
Overestimating real
revenue
Leads to wrong decision making
Leads to missing opportunities

Creating lookalikes based on player data
Targeting most proﬁtable players for your game → increasing LTV
Our experience from different games : Creating your own lookalikes based on player segments can improve ROAS
relatively by more than 20%. [changes coming to iOS, still great for Android]

1. You need to ﬁnd the best possible representation of your top players

2. You need to ask at least these question:
- Is this group great at buying IAPs, do they do it frequently?
- Is this group heavily engaged, does their engagement grow over time?
- Does this group of players watch ads frequently?

2. You need to ask at least these question:
3. A/B test this group against your best lookalike / audience to date (your benchmark)

2. You need to ask at least these questions:
3. A/B test this group against your best lookalike / audience to date (your benchmark)
4. Evaluate and Proﬁt

Creating lookalikes - Player segments in your game
Example overall LTV of the game
LTV
LTV model predictions
Legend
Real LTV numbers

Convex-like development
(Cluster 2)
Faster early growth, closer to straight line
(Cluster 1)
Logarithmic-like development, earlier ﬂattening
(Cluster 3)
LTV
LTV
LTV
LTV
Legend
Real LTV numbers

You need to ﬁnd those
player segments
LTV
LTV
LTV
Convex-like development
(Cluster 2)
Logarithmic-like development, earlier ﬂattening
(Cluster 3)
Legend
Real LTV numbers
Faster early growth, closer to straight line
(Cluster 1)
LTV

Do you have enough data for LTV predictions?
Unusual growth of revenue in the cohort can be the clue of not having enough
players to work with.
=> Number of players needed is based on conversion and amount of spend.
LTV

Improving IAP LTV
Maximizing revenue from your special offers

Are you leaving money on the table?
Imagine having 10000 special offers
in your favourite game, which one
would you want to see in your next
session?
Would you be happy with any
random offer?
Improving IAP LTV

Imagine having 10000 special offers
in your favourite game, which one
would you want to see in your next
session?
Would you be happy with any
random offer?
Improving IAP LTV
- Would you want $5 or $100 price?
- What amount of coins?
- How much discount?
- What visual aspects would offer have
that would impress you?
- ….

Many games are showing offers to players that players simply don’t want.
Either are offers random or picked only by simple rule-based systems.
How do you battle this?
Improving IAP LTV

Many games are showing offers to players that players simply don’t want.
Either are offers random or picked only by simple rule-based systems.
How do you battle this?
Personalize offers better.
Many other industries do personalization of content which helps them
improve monetization experience for each customer.
Improving IAP LTV

Showing relevant price & content at the right time
=
increasing LTV of your playerbase.
Improving IAP LTV

3. Dynamic personalization
- 1000s of dynamically created offers
- Targeting based on many different
parameters (even complex ones like
purchase aggressivity)
- Use of AI / Machine learning or
probability model
1. Random offers
- 100-1000s predefined offers
- No targeting
- No filtering of offers
2. Rule-based system
- 100-1000s predefined offers
- Targeting based on few “ifs”,
e.g.:
- Previous purchase price
point
- Conversion on certain point
- Amount of currency
Special Offer Systems - Usual Types
Potentially missing IAP revenue using this system:
Missing 50-100% IAP revenue Missing up to 50% IAP revenue Maximizing IAP revenue
Improving IAP LTV

Personalization results in signiﬁcant IAP LTV uplift
Works for any IAP focused
game genre:
From 20% => 100%
(match 3 => RPG game)
Better for complex games
Different game modes
+ Lots of different items to sell
Can be done safely
Start with 5% of players then
expand
Case study:
Observed IAP LTV growth from personalization system.
Day
TRUE LTV UPLIFT
Legend:
LTV curve - dynamic personalization
LTV curve - rule-based system
Improving IAP LTV
+35% LTV

Showing only relevant offers is the key
We use Machine Learning Models to pick all aspects of an offer based on data for
each player in a game using ONLY existing content.
Amount of
resources
Additional value
Offer price
Availability
Type of chests
Visuals & copy

Your special offers are great if you can ….
Increase
revenue per
user
Improving IAP LTV
Minimize
discount/
value multiplier

Your special offers are great if you can ….
Minimize
discount/
value multiplier
Increase
revenue per
user
Price
Content distribution
Value
Availability
Offer sets and their
sequence
Visual aspects
Optimization:
=> The value is in the personalization!
Improving IAP LTV

Personalization process
1. DATA
GATHERING
2. LEARNING PREFERENCES
OF PLAYER SEGMENTS
3.NEW OFFERS
GENERATION
4.DELIVERY &
INCREASE IN LTV
● Preprocessing Data into
model-ready state
● Analyzing of player
behavior (behavioral
parameters)
● Learning preferences about
segments of players
● Taking into account the Player’s
browsing behavior along with their
friends’ and their segment
colleagues + interaction with
customizations
● Creating offers based on
available Cosmetics, chests
and resources in the
Inventory and the Shop slots
layout
● Each player receives
personalized offer he is most
likely to buy
● Time Limited Offers only
● From the response (buy or not)
we strengthen understanding
of players’ preferences
Improving IAP LTV

Special Offer delivery system - Infrastructure
RAW
DATA
PLAYERS
EACH PLAYER/SEGMENT GETS THEIR
UNIQUE PERSONALIZED OFFERS
DATA
GATHERING
MACHINE LEARNING
(LEARNING PREFERENCES OF PLAYER SEGMENTS) OFFER ASSETS
INCREASE LTV
PLAYERS DATA
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
DATA
GATHERING
MACHINE LEARNING
(LEARNING PREFERENCES OF PLAYER SEGMENTS)
INCREASE LTV
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
DATA
PROCESSING
DATAFLOW
SCHEDULER
CLOUD FUNCTIONS
DATA
GATHERING
MACHINE LEARNING
INCREASE LTV
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
Behavioral
Modeling:
Understanding
players
preference
through the set
of behavioral
parameters
Price Modeling:
Understanding
monetary
potential of
players
DATA
PROCESSING
DATAFLOW
SCHEDULER
CLOUD FUNCTIONS
DATA
MODELING
ML ENGINE
DATA
GATHERING
MACHINE LEARNING
INCREASE LTV
MODEL
STORAGE
GOOGLE CLOUD
STORAGE
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
Behavioral
Modeling:
Understanding
players
preference
through the set
of behavioral
parameters
Price Modeling:
Understanding
monetary
potential of
players
DATA
PROCESSING
DATAFLOW
SCHEDULER
CLOUD FUNCTIONS
DATA
MODELING
ML ENGINE
OFFERS
CREATION
OPTIMIZED FOR
BEST ARPU
DATA
GATHERING
MACHINE LEARNING
INCREASE LTV
MODEL
STORAGE
GOOGLE CLOUD
STORAGE
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
Behavioral
Modeling:
Understanding
players
preference
through the set
of behavioral
parameters
Price Modeling:
Understanding
monetary
potential of
players
DATA
PROCESSING
DATAFLOW
SCHEDULER
CLOUD FUNCTIONS
DATA
MODELING
ML ENGINE
OFFERS
CREATION
OPTIMIZED FOR
BEST ARPU
OFFER
DELIVERY
UNIQUE TO PLAYER
SEGMENT
DATA
GATHERING
MACHINE LEARNING
INCREASE LTV
MODEL
STORAGE
GOOGLE CLOUD
STORAGE
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

RAW
DATA
PLAYERS
DATA WAREHOUSE
GOOGLE BIGQUERY
Behavioral
Modeling:
Understanding
players
preference
through the set
of behavioral
parameters
Price Modeling:
Understanding
monetary
potential of
players
DATA
PROCESSING
DATAFLOW
SCHEDULER
CLOUD FUNCTIONS
DATA
MODELING
ML ENGINE
OFFERS
CREATION
OPTIMIZED FOR
BEST ARPU
OFFER
DELIVERY
UNIQUE TO PLAYER
SEGMENT
CONTINUOUS
IMPROVEMENTS
DATA
GATHERING
MACHINE LEARNING
INCREASE LTV
MODEL
STORAGE
GOOGLE CLOUD
STORAGE
PLAYERS DATA
OFFER ASSETS
Improving IAP LTV

Personalization results in signiﬁcant IAP LTV uplift
Works for any IAP focused
game genre:
From 20% => 100%
(match 3 => RPG game)
Better for complex games
Different game modes
+ Lots of different items to sell
Can be done safely
Start with 5% of players then
expand
Case study:
Observed IAP LTV growth from personalization system ($15M+/year game)
Day
TRUE LTV UPLIFT
Legend:
LTV curve - dynamic personalization
LTV curve - rule-based system
Improving IAP LTV

Summary
1. Struggling with the UA after increasing the spend to the levels
when CPI gets higher than LTV is a common issue.
2. Leveraging your game’s data to the full extent can signiﬁcantly
improve LTV of your game, which can unlock further spend
scaling
● Special offer personalization utilizing existing game content can bring
+20%-100% extra IAP revenue
● Optimizing your UA decision making using data can result in additional
LTV/ROAS increases:
○ Spend reallocation
○ Spend budgeting
○ Lookalike creation
○ Creative strategy testing ...

Thank You
robert.magyar@superscale.com

LTV predictions for Growth and UA activities

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