How to Build an Effective Offer System in Your Game

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How to Build an

Eﬀective Oﬀer

System in Your Game

Discover how top games structure Oﬀer System, what’s

new, and how to build a scalable system that performs.

Playliner

by Sensor Tower

Playliner
, Sensor Tower’s state-of-the-art platform for analyzing

Live Ops, enables you to dive into a rich repository of events,

updates, and monetization oﬀers across hundreds of top games.

Whether you’re designing a new oﬀering, reengaging existing

players, or optimizing your monetization tactics, use
Playliner to

secure your competitive edge in the mobile gaming world.

This report gives you a preview of the rich insights available

in-platform – use these evidence-based recommendations to

move with confidence and revamp your strategy for 2026.

ARpU growth comes from multiple levers:

Conversion, Repeat Purchases, Average Transaction Value, Demand

While Shop covers basic, always-on needs

Oﬀers deliver higher value, urgency, and personalization

Adaptive oﬀers respond to player behavior, needs, and capacity to pay

LTV Maximization

Segmentation & Personalization

The goal is not just to sell

WHY Do You Need an Oﬀer System?

A good oﬀer system:

●
Helps players overcome friction

●
Supports progress at critical moments

●
Feels like ‘this is exactly what I need right now’, not pressure

The more oﬀers you have, the better they perform

Players don’t want
more
oﬀers → They want the right oﬀer, at the right moment, for the right reason

Cheaper oﬀers always mean a better conversion strategy

Low entry price boosts conversion

However, some players are ready to pay more from the start

Starting too cheap hides willingness to pay and caps future spend

Players buy only emotionally – because of visuals and excitement

Players buy only rationally – by calculating value and eﬀiciency

Purchases happen at the intersection of emotion + calculation + context.

Visuals and presentation attract attention, while value is evaluated
intuitively
, not through deep calculations.

So HOW do you avoid

these mistakes,
and build an

eﬀective oﬀer system?

Common Myths

About Oﬀers

Oﬀer Types & Visualization

What types of oﬀers exist and

what problem each one solves

Core Building Blocks of an Oﬀer System

Triggers & Timing

When and why an oﬀer appears

Segmentation & Personalization

Adapting oﬀers to player behavior,

context, and capacity to pay

Oﬀer Compatibility

Oﬀers don’t compete or cannibalize

each other

Clear comparison and value diﬀerentiation

Economic Balance

Economy-aware design

Controlled inflation and sustainable value

Pricing Strategy

Increase when players are ready

Roll back when they are not

Oﬀer Types

When choosing oﬀer types, focus on:

There are dozens of oﬀer types on the market today.

But that doesn’t mean you need ALL of them.

Next, we’ll break down the
TOP-performing oﬀer types
and what makes them work.

More oﬀers ≠ better performance.

A strong system is built on a few

types that work well together.

Your KPIs
– what exactly you want to

improve (conversion, repeat rate, ARPPU)

Visual variety
– to keep oﬀers feeling fresh

and ‘new’

System synergy
– oﬀers should

complement each other, not compete

The simplest and most stable oﬀer type

Simple & focused

●
One clear bundle

●
No visual noise

●
No competing choices

Why it works

●
Extremely eﬀective with
Segmentation

●
Perfect fit for
Starter Packs

How it’s used

●
Shown at session start

●
Or triggered by key moments
(return, cooldown,

progression)

Role in the system

●
Acts as the
baseline oﬀer

●
A reference point for: pricing and value

One of the top monetization touchpoints in

most games

Right place. Right moment:

●
Appear exactly when the player hits friction

Typical triggers:

●
Level-based games → Revive / Play On

pop-up

●
Other genres → Out of Сurrency pop-up

Why they work:

●
Oﬀer exactly the resource needed to

continue progress

●
No choice overload – immediate solution

Price and Value:

●
Price and value must match the real cost of

continuation

●
These oﬀers rely more on
context and need
,

not on aggressive price scaling → no extreme

price points (e.g. $50+)

Triggered Oﬀer

Revive Pop-up

Out of Spins

Always something to buy

●
Endless Oﬀer works like a
’10-in-1′
deal

●
Buy one – another is already waiting

Built-in self-segmentation

●
Players choose how deep they go

●
Some stop at 2, others at 5 or complete the

whole chain

Strong ARPPU driver

●
Depth is defined by player willingness, not

forced pricing

●
Start cheap, scale smoothly: Low entry price
→

repeat purchases → gradual check growth

Two scaling strategies

●
Price increases step by step

●
OR: Same price, but higher value (better for

repeat purchase)

Endless Oﬀer

Strong visual hook

●
Each purchase unlocks
‘
extra rewards for FREE
‘

●
6-slot or 3-slot Oﬀers – great for rotation. Same Structure, new Visuals

1+X Oﬀer

Common Visual & Reward Variations

1. 1+1 format

Buy one – get the same pack for free

Classic, easy to read, triggers discount instincts

2. Mirror format (1 + half + half)

FREE rewards visually match the paid pack

Identical tiles = strong feeling of high value

3. 1+6 or 1+12 formats

Sheer number of FREE rewards feels stunning

Creates a hypnotic ‘too good to ignore’ eﬀect

4. 1+X with a focus on rare currency

Diﬀerent rewards, one clear highlight

Premium or rare items drive perceived

generosity

This oﬀer type is all about visuals and variety

Same core value – completely diﬀerent perception

Psychology over math

Multiple FREE tiles create an illusion of overwhelming

value. Rational thinking switches oﬀ

(+highlighted by twin chipmunks)

1+1 Oﬀer

1+half+half Oﬀer

1+12 Oﬀer

Focus on Rare

Currency (Pearl)

2-3 Bundles in One Oﬀer

Common Design Patterns:

1. Anchor the higher-priced options

●
First bundle is intentionally weak

●
Visually smaller and less attractive→

Makes the next 1-2 options look

significantly better

2. Comfort vs premium contrast

●
1-2 options sit in a ‘comfortable’ price
range

●
3rd option is expensive but clearly more

valuable

●
Visual emphasis + attention to the premium

choice

3. ‘Buy Them All’ option

●
Multiple balanced bundles OR Buy them

ALL with
20-40% OFF

●
Converts indecision into a higher total

check

Choice architecture as a monetization tool

●
Adds
built-in Segmentation
inside a single oﬀer

●
Side-by-side options enable
controlled

comparison
and intentional emphasis
(use

Anchoring, Decoy eﬀect)

Clearly highlighted by color and size.

More than 2Х value without a 2Х price.

Smoothly guides the player toward the highest-priced

purchase – both visually and through balance.

The ‘Buy All’ price is only slightly higher than the most

expensive oﬀer: a $2 diﬀerence – while the value gain is $10+2

Battle Pass+

Common Short-Term Variations (2-3 days)

●
Endless Oﬀer + Currency from activity

a.
Progress generates currency –

abandoning it feels wasteful

b.
Purchase unlocks accumulated value

●
Chain Activity Pass

(Royal Match)

a.
Goals progress simultaneously and

stack

b.
Next milestone always feels ‘just a bit

away’

How to Balance It Properly

●
Base activity → average oﬀer Bonus

●
Above-base activity → higher Bonus Value

Why Battle Pass Works

●
‘I’ve already progressed so much –

buying feels logical’

●
And vice versa – Buying unlocks the desire to

finish the pass and maximize rewards

However,
Main Limitation of Battle Pass: Low

purchase frequency
: 1 purchase per 14-30 days

Battle Pass

Endless Oﬀer +

Currency from Activity

Chain Activity Pass

Stamp It

A short-term
purchase-loyalty mechanic

Inspired by midcore & café loyalty systems – but

compressed in time

Why It Works

●
Drives repeat purchases

Rewards are unlocked only after
multiple
buys

●
Controls minimum check

Stamps are granted only for selected oﬀers or

price tiers

●
Turns spending into a goal

‘I’m just one purchase away from the reward’

How It Increases LTV (Example)

●
Player baseline:

~$10 per purchase
, 1-2 buys in 3 days

●
Stamp event rule:

3 purchases,
min $7 each

●
Result:

To unlock all rewards →
$21+ total spend

Don’t set the bar too high –
too much friction kills motivation

Other

Milestone Oﬀer

Shop: Special Oﬀer / Coupon

Disco Wheel

Starts with low-priced spins, but both price and

attraction grow as valuable slots remain.

Strong driver of
repeat purchases

Triggered by reaching a meaningful milestone –

players buy on
positive emotions
, even without

currency deficit.

Coupon store bonuses (e.g.
‘+100% value on all

purchases’
) are an
underrated
but powerful

monetization lever.

Piggy Bank

Starter Pack

Event Currency Oﬀer

Sells event-specific currency, often combined

with core currencies. Especially
eﬀective near

the end of events
– albums amplify urgency.

Leverages the
‘already earned value’
eﬀect
– you

just need to unlock it. Still psychologically strong,

even if newer oﬀer types have partially replaced it.

Strong visual presentation paired with

significantly higher value. Designed to secure

the
1st Purchase
fast and confidently.

Other

Oﬀer Triggers

and Placements

A purchase usually requires multiple touches – or

one exceptionally strong trigger

This is how most marketing campaigns work: the

idea needs time to
mature
before it turns into a

purchase.

An oﬀer needs to be:

●
Introduced
(‘seen’ by the player)

●
Triggered
at the right moment (out of Currency,

lose the progression of the event, etc)

●
Easy to access
(lobby, shop)

If players can’t quickly find an oﬀer,

it eﬀectively doesn’t exist.

Oﬀer Exposure: One

Touch Is Rarely Enough

The Road to

Repetitive Marketing

Studies show that a prospect customer needs to

see/hear an advertiser’s message
at least 7 times
,

before they take actions

Core Oﬀer Triggers

Game Entry (Login / Opening the Game)

●
Many companies fear pop-up overload

●
In reality, this is one of the most eﬀective

exposure points

‘Out of Currency’ Moment

●
Especially strong for
Merge, Solitaire, Casino

genres

●
The same oﬀers can be shown here and on login

●
Sequential exposure works well

Level Failure → Continue Level Oﬀer (Revive)

●
Core trigger for level-based games

●
Clear friction → clear solution

Positive Moments (Momentum Triggers)

●
Purchases don’t happen only in deficit

●
Players buy
‘on a high’
– before friction appears

●
Especially eﬀective when oﬀers are:

○
clearly valuable

○
limited in time (5-10 minutes / now

or never)

Revive Oﬀer

Out of Currency

Positive Moment

Oﬀers Pricing

& Segmentation

Why you may need Segmentation:

●
Players have
diﬀerent willingness to pay

●
One price cannot fit everyone

●
Without segmentation, you either:

○
under-monetize high-potential players

○
or push low-payers away

Segmentation helps you:

●
scale prices only
when players are ready

●
protect conversion for low spenders

●
maximize long-term value

What segmentation is
not

●
Not ‘make oﬀers more expensive for payers’

Segmentation:

Start With Why

Quantity

Price

One Oﬀer

Segmented Oﬀer

Quantity

Price

Segmentation is powerful – but it’s not always the first priority.

You can wait if:

●
The project is at an
early stage

●
Oﬀer types, pricing, or economy are
not yet stable

●
You’re focusing on
1st Conversion oﬀers

(here segmentation by country, UA source, or device matters more)

●
You don’t have enough data to read player behavior reliably

●
Your features already include
self-segmentation

(Endless oﬀers, Chain oﬀers, 2-3 Bundles in one oﬀer)

●
Your oﬀers are
not segmented
or only
minimally segmented

(Revive oﬀers, Battle Pass)

●
The game relies on
cheap traﬀic + ad monetization

(segmentation is still important – but mainly for ads, not oﬀers)

When Simple Pricing Is Enough:

Don’t Over-Segment Too Soon

Simple Pricing Logic

You don’t need a complex monetization system

from day one

●
A simple
step-up
/
step-down

logic works

well both for new projects

●
And even for Top games (e.g. Royal Match –

for selected oﬀer types).

Simple Pricing Logic

How It Works (Basic Logic)

●
Oﬀers are split into
Tiers

(each Tier = higher price + higher value)

●
Player
BUYS
the oﬀer → move

+1 Tier Up

(shows readiness to pay more)

●
Player does
NOT BUY
for some time →

move
-1 Tier Down

(price pressure is

reduced)

●
Rollback speed can be tuned via

Recency

(higher tiers → slower rollback)

This creates a self-adjusting price ladder.

Start

+1 Tier

-1 Tier

BUY

DON’T

BUY

Tier 1

Tier 2

Tier 3

Tier 4

Tier 5

Tier 6

●
The
Travel Deal

oﬀer changes depending on how many times the player has purchased it. The cost of this

oﬀer increases sequentially with each new purchase.

●
However, if the player stops purchasing the oﬀer, its price does not remain high. When the oﬀer next

appears, its cost is reduced by one step – to the previous level.

Royal Match: Example

The system follows the same tier logic as

described above.

●
The oﬀer has
7 Tiers
, priced from
$0.99 to

$99.99

●
The player starts from
Tier 1
– the cheapest

pack (
$0.99
)

●
From there, the oﬀer moves

Down

the

ladder based on player behavior:

○
purchase → Tier goes
+1
Up

○
no purchase during the period → Tier goes

-1

Down

●
The rollback is intentionally slow –

prices usually drop only after a long pause
(e.g.

~1 week without purchases)

Royal Match: Example

Start

+1 Tier

-1 Tier

BUY

DON’T

BUY

$0.99

$2.99

$6.99

$19.99

$44.99

$79.99

$99.99

Purchase #

Oﬀer price, $

Before purchase

0.99

After 1st purchase

2.99

After 2nd purchase

6.99

After 3rd purchase

19.99

After 4th purchase

44.99

After 5th purchase

79.99

After 6th purchase

99.99

Segmentation

Important Parameters

Recency

●
Time since last purchase

●
For non-payers:
lifetime recency
is even

more important

→ Shows
how warm
the player is right now

Segmentation Parameters: What We Look At First

Average Transaction Value

●
One of the most underrated parameters

●
Often more predictive than Total Spend

→ Defines the player’s
comfortable price

zone

Max Payment

●
Shows upper
potential
, not behavior

baseline

●
Usually used as a supporting signal, not

a primary one

Frequency

●
How often the player buys

●
Strong indicator of habit vs impulse

spending

●
Shows upper
potential
for repeat

purchases

Contextual Parameters (Deeper Layer)

●
Country

●
UA source

●
Device

●
Player Engagement and Turnover

●
etc

Total Money (Spend per Period)

●
Good for broad grouping

●
Weak alone – strongest when combined

with Recency & Average Transaction

Value

Recency

●
For payers
→ time since last purchase

●
For non-payers
→ lifetime recency

(how long the player has been in the game

without converting)

What it tells you
:
How long the player hasn’t paid.

Whether it’s time to act – or step back

Why it’s critical

●
One of the main signals for
price rollback

●
Especially useful when prices were pushed

above the player’s comfort zone

Typical use cases: High Recency → reduce pressure,

lower price, reintroduce value

Frequency

●
Less Important at the beginning

●
Strong indicator of
repeat purchase potential

●
Paying frequency =
habit formation.
And habit

is one of the strongest monetization driver

RFM: Recency, Frequency

Recency

Frequency

The freshness of the customer

activity, be it purchases or visits.

E.g. Time since last order or last

engaged with the product

The frequency of the customer

transactions or visits.

E.g. Total number of transactions or

average time between

trasactions/engaged visits.

Look at the data, not assumptions

Build Graphics and ask 3 Key Questions:

1.
After how many days do players
stop paying
?

2.
After how many days does a
repeat purchase
usually happen?

3.
Where does the
drop-oﬀ
become visible?

These points define your Recency buckets.

Game 1

Game 2

For example:

●
As shown in
Game 1
graph, players keep

converting up to
Day 6
and then hit a plateau

●
Whereas, in
Game 2
, the active conversion

window lasts

only
3 Days
.

That’s why
Recency

thresholds
(e.g., when to

aggressively drop Starter Pack pricing)

should be diﬀerent for these games.

How to Set Recency Thresholds

There is no universal Recency setup. Each project has
its own

purchase rhythm.

1 day
is always the starting point → From there, everything depends on

player behavior.

What it shows

●
How much the player has spent
in Total
over

their lifetime

Pros: Why it’s useful

●
Good for high-level grouping (non-payer /

payer / whale)

●
Works well as a secondary or supporting

signal

Cons: Where it fails

●
Says nothing about current readiness to pay

●
A player who spent $100 a year ago may

behave like a non-payer today

○
This can be mitigated by also tracking

Total
Spend over a recent period
(e.g.

last 30 days – adjusted to your project’s

purchase cycle)

●
Total Money reflects
spending potential,

NOT spending behavior

○
It shows
how much
a player has spent

overall,

○
but not how they usually pay – at which

price points and with what consistency.

Total Money

What Total Money tells us

●
Player’s
spending potential
+ spend
dynamics
over time

●
At a high level, our goal is to
maximize Total Money

What Total Money does NOT tell us

●
How
this Revenue is achieved: many small purchases vs a few big ones

●
What
actually changed in behavior: repeat purchases? higher price points?

Total Money (Per Period)

Total Money = $500

50 purchases × $10

Comfortable with microtransactions

Pays often, in small amounts

(often seen in casual and puzzle players)

Total Money = $500

5 purchases × $100

Prefers big, high-value deals

Pays rarely, but in large chunks

(often seen in gambling and chance-based games players)

Let’s look at 2 players with the same Total Money, but completely diﬀerent payment behavior patterns.

Player 1

Player 2

Strategy

●
Core oﬀers around
~$10

●
Few beneficial oﬀers in the
$10–20
range to gently scale the check

●
Non-aggressive upsell through: Endless Oﬀers, 3 in 1 Oﬀers

●
Main goal:
build repeat purchase habit

Strategy

●
Don’t underprice

●
If the player is comfortable with
$50
, avoid flooding them with
$3–5

deals

●
Focus on: premium bundles, high-value, clear propositions

●
However:

If the player hasn’t paid for a long time and has moved

away from their usual purchase rhythm, it’s reasonable to
gradually

lower the price
to re-engage them – without an abrupt drop

From Total Money to Average Transaction Value (ATV)

ATV ~ The player’s real comfort zone

Why it matters

●
Predicts how players react to price changes

●
Helps avoid:

○
underpricing high-potential players

○
overpricing frequent low-check buyers

Long-Term Reality

●
It’s easier to
build a habit of regular payments

at a comfortable price

●
Only after that does it make sense to carefully

increase the Average Transaction Value

Psychologically,
a lot of players are far more

willing to:

●
spend a small amount multiple times

●
than make one large payment

This is why frequency + comfort zone often

outperform one-oﬀ high prices.

Average Transaction

Value (ATV)

ATV +1

ATV

ATV -1

How to use it

●
Core oﬀers should sit close to the ATV

●
Upsell oﬀers should be:

○
slightly above it

○
clearly more valuable

○
non-aggressive

Comfort Zone

Higher Price

Lower Price

Smooth out Outliers

●
Abnormal purchases (e.g. a payment that

diﬀers from the average by X%) should have

lower weight

Exclude non-representative purchases

For example:

●
non-segmented oﬀers: Battle Pass, etc

●
the cheapest Shop purchases

Weighted Purchase History

●
Recent purchases should influence

segmentation
more strongly

●
Example weighting:

○
4-5
for recent purchases

○
2-3
for older ones

The goal is to capture typical behavior, not noise.

ATV: Practical Notes

Sometimes You Should Step Down

Should you always oﬀer only the comfort price and higher?

Not always.
Especially at high price points

–
After a higher-than-usual purchase,

the old price may still feel
emotionally comfortable

while the new one felt like a stretch.

–
Giving a temporary step-down option

What Happens If You Don’t Step Down

If the price stays too high for too long:

Scenario 1 (Best case)

–
Player loves the game, keeps playing

–
Eventually converts again (even with higher price)

–
Important:
don’t raise the price further
– hold the tier, especially at

high values

Scenario 2 (Risk)

–
Player stops buying

–
Engagement drops

–
Higher churn risk

MaxPayment – potential signal, not a baseline

Don’t Overreact to One High Purchase

What it shows

●
The highest single payment a player has ever made

●
Indicates upper spending potential, not regular

behavior

How it should be used

●
Signals how expensive an ‘Premium’ oﬀer
can
be

○
Complements the core pricing logic, not

replaces it

●
Core oﬀers should still be priced
around the

player’s Average Transaction Value

●
Expensive oﬀers should appear
in parallel
, not

instead of core ones

Max Payment

Example

A player usual pattern is $10–15 purchases

Their once paid $30 during a holiday event.

→ Max Payment shows potential

→ ATV + Frequency show reality

Core Oﬀers =
$10-15 (according to Average Transaction Value)

Premium Oﬀer =
$30 (because of the MaxPayment – potential)

Average

Transaction Value

$10-15

Lower Price

Max

Payment

$30

Basic

Core Main Oﬀer

~$15

Expensive

ATV + Oﬀer

up to $30

Country / Tier

●
Lower-tier countries often monetize worse

●
Conversion and ARPU expectations should be

adjusted accordingly

UA Source

●
Higher-quality traﬀic:pays more often, shows higher

loyalty

●
Cheaper traﬀic: lower conversion, weaker repeat

behavior

Player Progress (Level)

●
Early-game players convert easier on cheap oﬀers

●
Mid / late-game players accept higher prices and

more complex bundles

●
Consider inflation: at higher levels, oﬀers often need

more currency per $ to stay meaningful

Other Parameters

Device

●
iOS and Android often behave very diﬀerently – if we are talking about Oﬀer System

●
Oﬀer systems should be analyzed separately

●
BUT you should be ready to support 2 distinct systems

Interesting fact:

●
Expensive Android devices (e.g. premium Samsung) sometimes behave like iOS

●
Cheaper iPhones can behave closer to Android

ML & Personalization

The Highest Level of Segmentation

The most advanced form of player segmentation

is
personalized oﬀer management powered by
ML
.

No matter how strong a system is –

even one designed with deep analytics –

ML can push it further.

●
Processes more signals than any manual

system

●
Finds patterns humans miss

●
Continuously adapts to player behavior

●
At scale, you can start using even more

parameters and dependencies

Important Reality Check

●
ML still requires
A/B testing

●
Results must be
carefully analyzed

●
ML only works properly at
large scale

Without enough data, ML adds noise – not value.

ML Segmentation: Beyond Cluster

Oﬀers as a System

Designing Compatible Oﬀers

Goal:
get more players to make their purchase

What works best:

–
Low-priced oﬀers

–
Clearly выгодные, easy-to-read bundles

–
Strong visuals and marketing framing

KPI First

The ultimate
Goal
is simple:
Maximize LTV
.

But
HOW
you get there defines what kind of oﬀer system you should build.

Diﬀerent KPIs → diﬀerent strategies → diﬀerent oﬀer design.

Goal:
make paying players spend more

What works best:

●
Price ladder near the comfort zone

●
Premium oﬀers placed next to

standard ones

●
Clear extra value or unique rewards for

higher tiers

Goal:
increase purchase frequency

What works best:

●
Endless / Disco-style oﬀers

●
Stamp It or Loyalty mechanics

●
Low starting price → repeat purchases

→

higher total spend

A strong oﬀer system rarely focuses on one KPI only

The best systems
Сombine all three
– but with a clear priority depending on your game, stage, and audience.

Maximize Conversion

Average Transaction Value

Drive Repeat Purchases

Do oﬀers have diﬀerent levels of profitability?

Is the value gap between them clear and justified?

Are price steps consistent across the system?

Does pricing feel intentional – not random?

Can players
understand
why one oﬀer

costs more than another?

Do diﬀerent oﬀers serve diﬀerent player needs?

Or are they just variations of the same bundle?

Are oﬀers visually distinguishable?

Can players instantly tell what is special

and what is premium?

Main Components

Visualization

Content Variety

Pricing Logic

Value Balance

Visualization

Above, we covered diﬀerent oﬀer types.

They vary in perception – and diﬀerent players

gravitate toward diﬀerent formats.

There’s no need to launch everything at once:

●
2-3 oﬀer types
are enough for a solid start

●
4-5 oﬀer types
are ideal for a mature,

well-balanced system

Visual Tricks That Work

Highlight the winning option

●
Use
color, size, or framing
to draw attention to

the best-value oﬀer

●
One oﬀer should clearly feel like
‘Best Value’

Show bonus value

relative to the
cheapest
option

in Shop

●
Not the nearest price in Shop

Bonus VS Discount

●
‘+100%’ is much more popular, than ‘-30% OFF’

FREE parts feel like real bonuses

●
FREE items are psychologically processed as a

Gift
, not as part of the price

Animations amplify value

●
Use Animations to help emphasize large bonuses

and visualize reward application

Content Variety

If all oﬀers contain the same items with only a
10-15% value diﬀerence
– the system quickly becomes boring

Add Rare Resources. Instantly

increase perceived value

Use Chests with reward ranges

instead of exact numbers. Creates the

chance-based excitement

Diﬀerent oﬀers can lean toward

diﬀerent currencies: boosters, hard

currency, side currencies

Use Event-Specific Content: Cards

for Albums, Event currencies

Pricing Logic

Variety Beats Precision

Imagine this situation:

A player opens the game and sees

3 diﬀerent oﬀers – all priced at $19.99
.

●
Yes, looks like you hit the Average

Transaction Value

●
But:

○
oﬀers feel interchangeable

○
there’s no real choice

○
no price ladder

Why Price Variety Matters

Diﬀerent price points allow you to:

●
introduce
meaningful variety

●
give players
real choice

●
gently move players to a
neighbor price tier

(up or down, without pressure)

You typically
won’t see this exact setup in-game
– it’s a mix from diﬀerent days.

But let’s imagine these oﬀers appeared side by side.

What do we notice?

●
Diﬀerent visuals, even though the oﬀer structure is the same

●
Currency amounts vary, but not by much

a.
Each one gives roughly 1.5-2K energy, 450-700 gems, plus some extras

●
So you see ‘four oﬀers’… but there’s
barely any real choice

Pricing Logic

What You Need to Do

Build a
segmentation simulation
:

●
Input player data

(Recency, ATV, Total Money,
etc.)

●
Identify the player’s segment

●
See
exactly which oﬀers and

prices
this player will see

Value is split across diﬀerent currencies –

harder to judge, but both still feel good.

Almost identical content, but diﬀerent

prices
($7.99 is clearly a bad deal).

Same price, but the second oﬀer’s

content is noticeably worse.

The System Trap

Oﬀers are often balanced in isolation – and that’s

the trap.

A single oﬀer may look fine on its own,

but break the logic of the
entire system
.

This is especially risky when: diﬀerent oﬀers have

diﬀerent price ledder

Every new oﬀer must be checked inside the

system.

●
Not just: ‘Is this oﬀer good?’

●
But: ‘How does it look next to all other oﬀers

for this player?’

Value & Bonuses

When to Show, When to Hide

If the value is truly high – show it clearly

●
Use bold labels and highlights

●
Compare value relative to the cheapest Shop

purchase

●
Make the advantage instantly readable

If the value diﬀerence is small – make it implicit

●
Shift value into:

○
event currencies

○
chance-based rewards / loot boxes

○
boosters (harder to evaluate)

●
Reduce direct comparison

Important reminder

Players usually estimate value at a glance,

anchoring on the core currency.

It becomes the reference point for all comparisons

Even complex oﬀers are anchored to this first

impression.

Beautiful presentation shows
‘+200% More’
– but this applies only to the
$99.99
oﬀer

For the cheap
$5.99
oﬀer, the real bonus is just
+25% More

Animation and Visuals
strongly amplify perception, highlighting the bonus part as
‘2X Bigger’
,

even when the actual value increase is modest

Travel Town. Example 1

What we see here

1) Diﬀerent formats + triggers

Same economy goal, but delivered

through diﬀerent entry points:

●
Endless
(progress loop)

●
Out of Currency
(hard friction)

●
Daily Login
(session start)

2) Price variety creates real choice

Price range:
$17.99 → $34.99*

Works as a soft price ladder:

●
easy to
stay in comfort zone

●
possible to
trade up

●
possible to
step down
without

feeling ‘downgraded’

*
I usually pay
$19.99
,

sometimes
$4.99 / $22.99

and only rarely
$29.99

●
Core content is consistent:
Energy + Gems

●
But each oﬀer adds a diﬀerent ‘hook’:

○
2
= clean, standard bundle

○
1 & 4
= extra layers
(chests + timed booster)
→ harder to value, adds excitement, blurs comparison

○
3
= strong focus item
(special card pack)
→ ‘unique value’ framing, not just more currency

Travel Town. Example 2

A natural question arises:

Isn’t the $4.99 Disco oﬀer too cheap?

This is where the oﬀer mechanic matters:

●
The oﬀer is designed for a
series of

purchases

●
Once a reward is claimed, it’s removed

from the pool → only the most

attractive rewards remain

●
Each next spin
costs more

The common trap:

●
You start with ‘cheap’ purchases:

$4.99 → $5.99 → $6.99 → $7.99

●
Each step feels well below your usual

$19.99 comfort price

●
But after just 4 purchases, you’ve

already spent:
$25.96

As a Result: Cheap ≠ bad

The principle here is similar:

●
Diﬀerent visual presentations

●
Wide price range from
$4.99 to $29.99
(with a clear anchor around
$19.99)

●
Non-standard content: chests, merge-chain items

Disney Solitaire. Example 1

Same Principles in Action

Wide price range:

$6.99 → $22.99

Closely matches real behavior:

●
frequent purchases
up to $14.99
,

●
occasional
$22.99

Looks like a mix of
ATV logic + Max Payment

potential

Low entry price ($6.99)

●
Designed for
a purchase sequence
, not

a one-oﬀ deal → total value is unlocked

through repeat buys

Multiple Visualization formats

●
1+X FREE

●
Endless + Currency

●
Chain Activity

●
Simple Login Oﬀer

Smart value distribution

●
High value is anchored in coins

●
Other currencies let players choose

which event or feature to focus on

Another Example – Wide Range, Clear Anchors

●
Completely
diﬀerent presentation
across all 6 oﬀers

→ each feels like a unique option, not just a variant

Disney Solitaire. Example 2

Wide price range:

$4.99 → $36.99

●
$4.99
is a low-friction entry, designed for
repeat purchases

●
$36.99
looks premium and convincing:

○
1.2M coins
– a strong psychological anchor
(1M+ feels ‘Big’)

○
at first glance, it appears
much more valuable
than nearby oﬀers

Economy & Balance

Start With a Clear Baseline

●
Always define a
reference point

●
All value comparisons happen
relative to

this Anchor

How to Define the Anchoring Point?

1. Cheapest Shop Pack –
a single, universal reference

point

●
Used to compare all oﬀers across the system

●
Ensures internal consistency: This is your

internal measuring unit.

2. Nearest Price Point
– player baseline

●
The closest Shop pack or Bundle by price

●
Reflects
how players
actually compare oﬀers

How players think:

●
‘I’m ready to pay
$10
‘

●
‘Which option around
$10
is better?’

●
Comparison happens against:

○
similar-priced oﬀers

○
cheaper alternatives

How to Calculate

Value and Bonuses

The image clearly shows a
Сomparison Trap
.

●
When compared to the
Cheapest purchase
, all oﬀers look good →
+12%
to
+50%
bonus

●
BUT when compared to the
Nearest price point
, the picture changes:

○
the
$7.99
oﬀer becomes clearly
bad
(-10%)
– it gives only
4.5K
coins instead of
5K

○
the
$14.99
oﬀer is just
neutral

(0%)
– no real advantage

Result:

Out of 3 oﬀers that look ‘valuable’ vs the cheapest pack – only 1 out of 3 is truly good in real

player comparison.

Oﬀer Systems Are Never Static

●
An oﬀer system is
not
something you set up once and forget.

●
It’s
alive and dynamic
.Every new: oﬀer, event or feature –
reshapes the

system
, even if you didn’t touch existing oﬀers.

Balance: Anchoring Point

Example: Real Case

Initial setup

●
Oﬀer value calculated vs cheapest Shop pack

●
Average oﬀer value:
100-200%

What changed

●
A global +100% bonus to the entire Shop started running on

~95% of days

What This Actually Means

●
The baseline instantly became +50% stronger

●
Your ‘1’ – the reference point – moved

●
Oﬀer value eﬀectively dropped

●
Even though:

○
oﬀer balance didn’t change

○
prices didn’t change

The system changed – because the base changed.

Balance:

The Real Value of the Purchase

Currency value is multi-dimensional
– and players subconsciously compare it across money, time, and progress.

●
$ value

●
Price point / ATV

●
Comparison with real-life spend

(‘cup of coﬀee’ logic)

Money (IAP

Time

Game / Event Progress

FREE Income

●
Waiting time skipped

●
Gameplay time saved

●
Fewer retries / faster completion

●
Number of potential levels

●
Number of boosters / attempts

●
Amount of Event Currencies

●
How many levels to beat for the

same reward

●
How many bonuses to collect

Recalculate Your ‘1’ Regularly

With every new oﬀer, event, or bonus:

●
re-evaluate your
baseline reference

●
recheck value ratios across the system

A stable balance can break silently.

New Oﬀer: Growth or Cannibalization?

Every new oﬀer should answer one question:

●
Is this
incremental revenue

●
or just
redistribution
?

If it only steals conversion from existing oﬀers –

it’s not growth.

Track Currency Burn Rate

Always monitor
how fast players burn purchased currency
.

If burn is too slow, you have two options:

●
Increase sinks
(diﬀiculty, costs, progression friction)

●
Reduce oﬀer value

Both work – but have very diﬀerent side eﬀects.

Store Order Matters

Changing the
order of oﬀers in the Shop
:

●
shifts attention

●
changes perceived value

●
aﬀects conversion – even if prices stay the same

Never treat order as cosmetic.

Other Life Hacks

Every change has a system-level impact.
If you don’t check the system – the system will check you.

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