Monogamers, serial monogamers
and polygamers: which users
make a game successful?
© Copyright Slice Intelligence. 2017. All rights reserved.
APRIL, 2017 • SLICE INTELLIGENCE, INC. • WWW.SLICEINTELLIGENCE.COM
1
content
03
abstract
03
introduction
04
methodology
04
loyalty and stickiness drive spend
08
serial monogamers are the best user acquisition targets
09
making cross-game purchase data actionable
10
conclusions
11
authors
13
about Slice Intelligence Marketing Science
14
appendix
© Copyright Slice Intelligence. 2017. All rights reserved.
2
abstract
Slice Intelligence analyzed 10 million mobile game transactions from more than
600K consumers over a two-year period to find behavioral patterns of high-value
gamers and the traits of game titles that attract them. In order to understand
time-based patterns in user purchase behavior, we developed several user level
metrics including loyalty and diversity measures and share of attention and wallet.
Slice Intelligence’s unique cross-game view shed light into behavioral patterns in
context of the broader market trends and benchmark game performance.
We leveraged broader in-game purchase behavior to address the following questions:
What are the best predictors of a game’s success?
Who are the most valuable customers to target for user acquisition?
We found that:
Share of game wallet, or the fraction of total spend in mobile gaming captured
by a specific game, is the best predictor of a game’s average spend per user
Serial monogamers, or users that switch games but tend to stick with one game
before switching to another, are the best target for user acquisition
introduction
Globally, mobile gaming is a $35 billion market, larger than both the console
and PC gaming markets. Despite having existed for less time than traditional
gaming categories, mobile gaming is experiencing the fastest growth across the
entire gaming sector. The games that capture the majority of the revenues are
microtransaction based, meaning they are free to play but include items or features
that users can buy in-game.
From past Slice Intelligence research, we know that looking at a user’s purchasing
history is a better predictor of online spending than mere demographics. In this
paper, rather than making predictions from past purchases on a single franchise,
we studied the purchasing behavior of users across mobile games and found
predictors of games with high user spend.
© Copyright Slice Intelligence. 2017. All rights reserved.
3
methodology
To identify user behavior patterns in successful games, we analyzed data from 600K
users in the Slice panel who made at least 10 in-game purchases from January 2015
to December 2016. To understand the patterns of game-spend both at the user and
game levels, we examined their purchasing behavior and aggregated them by game.
We calculated 10 user-level metrics (see appendix A) based on cross-game purchase
data to identify behavioral characteristics of different types of gamers that might
identify high value users. We aggregated user populations by game and calculated
averages for those user-level metrics as well as new game-level variables (see
appendix B for the full list). These user behavior attributes were then used in
regression trees to predict user spend for each game, and to understand which user
and game-level metrics are most important in predicting game success.
loyalty and stickiness drive spend
We found that the average user made purchases in five different games in a
12-month period, with one or two games winning the lion’s share of the wallet.
At the game level, we summarized the average values of their user-level metrics.
The average game has users who play an average of 17 other games (figure 1),
and games with users that make purchases in fewer games have a higher spend per
user (figure 2).
figure 1
users of most successful games spend in fewer
other games
mean
game count
ABC
A
Candy Crush
B
Clash of Clans
C
Game of War
average number of games where users have made purchases
Several of the most successful titles have users that buy fewer games than most other games.
© Copyright Slice Intelligence. 2017. All rights reserved.
4
figure 2
game loyalty correlates with higher user spend
game-specific
user spend ($)
Candy Crush
Clash of Clans
Game of War
Games with similar number of competitors capture higher user spend if their users are more loyal.
Share of game wallet is highly correlated with spend per user. Share of game wallet
is the fraction of the total dollar spend in the category that’s spent on a specific game.
All the games we observed with a high share of game wallet also had a high spend
per user, even if the average wallet size was small (figure 3).
figure 3
share of game wallet is highly correlated with
spend per user
average game-specific spend ($)
total wallet
size ($)
Candy
Crush
0
10%
20%
30%
Clash
of
Clans
40%
Game
of
War
50%
60%
70%
80%
Even games that have users with smaller total spends can succeed if they collect a large fraction
of the wallet in mobile games
© Copyright Slice Intelligence. 2017. All rights reserved.
5
The share of game wallet is a function of user loyalty and stickiness. We define
loyalty as the fraction of purchases in a game relative to the total amount of
purchases across all games. Stickiness is the frequency of switches between
games as observed through their in-game purchases. Users with high stickiness
switch games less often than expected and users with low stickiness switch games
more often than expected. Users with a stickiness of zero have purchase patterns
indistinguishable from random switching.
Figure 4 below shows examples of purchase stickiness between users who play two
games. User A has low stickiness and is constantly shifting between games. User B’s
purchasing pattern appears random; showing no perceivable pattern. User C has
high stickiness, only experiencing one game transition, sticking to one game until
she decides to switch to a new game. Even if stickiness and loyalty are related, they
are not the same, as stickiness measures serial loyalty. The loyalty is the same in case
A and C, and very similar on user B, but the stickiness is very different.
figure 4
examples of stickiness in purchase patterns
Stickiness = -1: more purchase transitions than expected by chance
Stickiness = 0: purchasing pattern indistinguishable from chance
Stickiness = 1: fewer purchase transitions than expected by chance
* Box 1 gives a more detailed explanation of these different types of users
When we plot average stickiness and loyalty for this user set, we see that all the high
user spend games are in the upper right section of the plot (figure 5).
© Copyright Slice Intelligence. 2017. All rights reserved.
6
figure 5
loyalty and stickiness can predict high user spend
average game-specific loyalty
Clash of
Clans
Game
of War
game-specific
user spend ($)
Candy
Crush
average stickiness of game users
Games with high user spend (in red) have either high stickiness or high loyalty. Games with low
stickiness and loyalty typically have lower user spend (in blue).
Note that almost all the games with high user spend are plotted to the right of the
diagonal line in figure 5.
box 1
buyer profiles based on stickiness and loyalty
ALICE
monogamer
Alice is a devoted fan of Candy Crush. She makes purchases in the game consistently and
has made very few purchases in other games. Her loyalty to Candy Crush is very high. Alice is
a monogamer.
BOB
serial monogamer
Bob enjoys playing different games, but mostly focuses his spend on one game at a time.
After playing Game of War: Fire Age for six months, he moved on to Clash of Clans for three
months, then to Clash Royale where he has been purchasing steadily for several weeks.
Bob is a serial monogamer.
CARLA
polygamer
Carla plays Words with Friends every now and then, but she also plays Cookie Jam, My
Singing Monsters, and Two Dots, among others. Carla does not invest a lot of money or
attention in one specific game, even though her total dollar spend on games is similar to Bob
and Alice. Carla is a polygamer.
© Copyright Slice Intelligence. 2017. All rights reserved.
7
serial monogamers are
the best user acquisition targets
Why?
Monogamers like Alice are set on a specific game and are less likely to be influenced
by ads and promotions. While monogamers may start purchasing in a different game
at some point, the timing of that switch is hard to predict.
Marketer tip: Focus your efforts on engaging and retaining your existing
monogamers, and carefully monitor their behavior to catch any indication of
game switching.
Polygamers like Carla are open to new games but do not stay long enough to
generate significant revenue. Their attention and purchases are inconsistently
spread across many games, making difficult to entice a polygamer to remain loyal to
one game.
Marketer tip: Efforts to attract polygamers will likely result in high cost per
install (CPI) and low lifetime value (LTV). Prioritizing other game audiences will
generate better performance.
Serial monogamers like Bob have shown purchase behaviors that indicate they
can be influenced to play new games. They will stick with what they like and can
generate significant revenue for games.
Marketer tip: Targeting ads to serial monogamers playing games similar to
your own, then converting them to play your game and focusing on retention is
a sensible strategy to maximize return on ad spend.
Figure 6 shows a plot of loyalty and stickiness for all users in the Slice panel that
have purchased items in Candy Crush, colored by in-game spend. Most of the high
spending users (more than 200 dollars of in-game spend, marked in red) are either
users with high stickiness, high loyalty, or both. Marketers should have different
strategies to acquire and retain these users depending on their behavioral attributes.
© Copyright Slice Intelligence. 2017. All rights reserved.
8
figure 6
high spend users tend to have high stickiness
or loyalty
Candy Crush users with high spend (in red) tend to have high stickiness, high loyalty, or a
combination of both
* Each dot is a Candy Crush user
making cross-game
purchase data actionable
Distinguishing between monogamers, serial monogamers, and polygamers is
difficult for a marketer without visibility across all game purchases. Monitoring the
loyalty and stickiness of users can be proxy indicators for the performance of a
game and the effect of customer retention measures, such as new features, items,
characters, or levels.
When releasing new titles, game developers can strategically time their promotions
by targeting users whose loyalty and stickiness has fallen below a certain threshold.
High stickiness users who might be looking for a new game can be retained by
a game developer if they’re able to direct them to another one of their releases,
instead of a competitor’s.
Monitoring the same metrics for competitors also would allow game companies to
find other games that are slowing down and try to capture their loyal and sticky users
with high lifetime value.
© Copyright Slice Intelligence. 2017. All rights reserved.
9
conclusions
Gamers tend to make purchases across multiple games but, generally speaking, their
spend is disproportionately distributed to just a few games.
purchaser loyalty
Loyalty, or the share of purchases that gamers dedicate to one game, and the
amount of time that game captures their attention, are the strongest predictors of
their dollar spend. Efforts to retain these loyal buyers are worth their weight in gold.
monogamers
Gamers who have been very loyal to a single game for a long time might not be
good candidates for user acquisition, as their purchases are focused within that
one game.
serial monogamers
Gamers not loyal to just one game, but whose purchases tend to stick with a game
for a period of time before switching to a different game (low loyalty, high stickiness)
are your ideal target audience for new user acquisition.
polygamers
Gamers who make purchases on multiple games simultaneously tend to be low
lifetime value customers for a microtransaction game.
recommendation for mobile marketers
While monogamers may be a difficult audience to acquire, identifying
and focusing on monogamers who may be switching games soon
can really pay off.
Polygamers are easy to identify and more likely to test out multiple
games at a time, so initial results of targeting this audience may be
misleading. Lifetime value will decrease over time.
Targeting serial monogamers who will stick with your game consistently
is the ideal acquisition strategy. These users will help you reach your
revenue goals, just make sure your app is optimized incentivize gamers
to continue playing over time, as well as to reduce churn.
© Copyright Slice Intelligence. 2017. All rights reserved.
10
guido
núñez-mujica
data scientist
[email protected]
Guido Núñez-Mujica is a computational biologist, data
scientist, and a science communicator. Previous to joining
Slice Intelligence, Guido was the founder of Lava-Amp,
a healthtech start-up that applied data science to medical
diagnostics and epidemiology. His work has been widely
featured, including in Nature, Wired, IO9, BoingBoing,
and Biotechniques. He is also the subject of a chapter in
the book Biopunk: DIY Scientists Hack the Software of
Life by Marcus Wolhsen.
As a prolific science communicator, educator, and activist,
Guido has been recognized as a TED Fellow and the
Cornell Alliance for Science Fellow; he was also awarded
a grant from Start-Up Chile, the leading Chilean business
accelerator, in 2011 for his entrepreneurial endeavors.
Currently, he is producing a documentary called Silenced
Crops on the topic of low-cost biotechnology in Venezuela.
Guido holds an interdisciplinary degree in computational
and physical sciences, and a licentiate degree in biology
from the Universidad de Los Andes, in Venezuela.
eric
berlow
head of science
[email protected]
Dr. Eric Berlow is the co-founder and president of
Vibrant Data Inc., which was acquired by Slice in 2016.
He is an ecologist, network scientist, and data scientist
who is globally recognized for his research on ecological
complexity. He has written articles for Nature, Science,
and the Proceedings of the National Academy of Sciences
that received ISI’s Most Highly Cited Papers awards.
His talks as a Senior TED Fellow on finding hidden
patterns in complex data have received over 2 million
views. Previously, Eric was the founding director of the
University of California’s first science institute inside
Yosemite National Park, and focused on guiding datadriven decisions to protect natural ecosystems.
Eric has received numerous awards including an
Alexander von Humboldt Fellowship and a National
Science Foundation Post-Doctoral Fellowship. In 2016
he was named one of the Top 100 Creatives by Origin
Magazine. Eric holds a Ph.D. in ecology from Oregon
State University and a B.A. in biology from Brown University.
© Copyright Slice Intelligence. 2017. All rights reserved.
11
rich
williams
senior data scientist
[email protected]
Dr. Rich Williams is a data scientist who specializes
in developing analytical methods and visualization
techniques for large-scale, high-dimensional data.
He is an accomplished software engineer, computer
scientist, and a frequently published research scientist.
Prior to joining Slice Technologies, Rich was a data
scientist at Vibrant Data Inc. and Quid Inc. Earlier in
his career, Rich started a research group at Microsoft
Research in Cambridge, U.K., doing research and
developing computational methods in ecology and
environmental sciences.
Rich’s research has focused on understanding ecosystems
as complex networks and dynamical systems by using
tools and techniques that connect ecology and computer
science. He has published over 50 papers, including work
in Nature and Proceedings of the National Academy of
Sciences. Rich holds a Ph.D. in physical oceanography
from Scripps Institute of Oceanography at the UC San
Diego, and a M.S. and B.S. in ocean engineering from the
Massachusetts Institute of Technology.
Ron is the vice president, Marketing Science at Slice
Intelligence, and is responsible for launching and growing
Slice’s digital advertising business.
ron
kato
vice president,
marketing science
[email protected]
Ron brings a rich background in corporate strategy
development and operational management, with a focus
in ad tech and digital marketing. Prior to Slice, Ron led
Rakuten Marketing’s strategic corporate planning as the
Director of Corporate Strategy. He expanded Rakuten’s
digital marketing portfolio through corporate M&A,
and spearheading strategic and operational efficiency
projects. Ron’s interest in ad tech found its roots when he
was an IT consultant at Cambridge Technology Partners
in their Digital Transformation practice.
Ron has a M.B.A. with Distinction from the Stern School of
Business at New York University, and a B.A. in management
information systems from The George Washington
University. Although a Silicon Valley resident, he is a New
Yorker at heart and has an undying love for the Knicks.
© Copyright Slice Intelligence. 2017. All rights reserved.
12
about Slice Intelligence Marketing Science
With a panel of over 4 million online shoppers, Slice Intelligence directly
measures all digital commerce activity from the consumer. By collecting
and cataloging actual shopping behaviors from online shoppers in the
wild, Slice Intelligence precisely measures what others have only been
able to approximate, revealing new insights about online shoppers and
their behaviors.
Slice Intelligence’s retailer-independent methodology captures commerce
as it happens at the item level, across all merchants. While most
companies rely upon panels of online users or of people who scan or
take pictures of their receipts, Slice measures all online shopping activity
directly gathered from consumers’ purchases. This allows Slice Intelligence
to collect more data, at a higher level of quality than other methods. This
intelligence gives clients unparalleled insights about everything their
customers buy, even when shopping elsewhere, eliminating the need to
use less reliable and less actionable research products.
The Marketing Science service takes the high-definition data used in
the Intelligence product and creates custom audiences. Leveraging
Facebook’s custom audience creation process, Slice Intelligence Marketing
Science is able to generate audiences based on real purchase data, across
merchants, focusing on frequent shoppers and/or high spenders for a
given brand, category, or merchant.
Slice Intelligence comes from a methodology developed at Stanford that
extracts online purchase data from e-receipts in consumers’ inboxes.
This refined data collection method enables impeccable, near real-time
data from a global panel of 4 million people, the largest panel of online
shoppers anywhere.
Slice Intelligence is led by a team of measurement and digital marketing
industry executives who have brought some of the most innovative and
successful data products to market.
© Copyright Slice Intelligence. 2017. All rights reserved.
13
appendix
A: list of variables at the user level
B: list of variables at the game level
weekendiness
the fraction of an user’s dollar spend in
the game category that is spent
during weekends
number of users
number of people in the Slice panel
who purchased from a game
day diversity
the variety of days in the week that a user
makes purchases
loyalty
the fraction of transactions devoted to the
game where a user purchases the most
stickiness
the degree to which a user tends to stick to
one game once they start purchasing in it
number of games
number of different games that a
customer has purchased from overall
game diversity
the variety of games purchased
user span
the total amount of time between first
and last in-game purchase across
mobile games
game-specific span
the total amount of time between first and
last in-game purchase (for a specific game)
average spend per order
average dollar amount spent per
transaction (across all games)
average game-specific spend
average spend of users for a
specific game
average share of wallet
average fraction of the wallet spent
on a game by a user
average share of attention
average time between first and last
purchase in the game, divided by total
time between first and last purchase in
the game category
average transactions per user
average number of transactions per
user of the game
exclusive player rate
fraction of the users that play only
that game
repeat rate
fraction of users that make more than
three transactions in a game
top game rate
fraction of users that have the most
transactions in a given game,
e.g. their favorite game
total purchases
total number of game transactions made
by an user
total spend
total amount spent by a user across
all games
© Copyright Slice Intelligence. 2017. All rights reserved.
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