'Analytics In Gaming' and how you can use it to improve the game's acquisition, retention and engagement' by Rajdeep Gumaste, Product Manager - Tiny Mogul Games.

1. Analy&cs
in
Gaming
Rajdeep
Gumaste
Product
Manager
-­‐
TMG

2. What
is
Analy;cs?
• Wiki
says
– “The
process
of
discovery
and
communica;on
of
paBerns
in
data”
–
We
agree
• Metrics
to
measure
different
quan;;es
• Some
metrics
considered
KPIs
for
benchmarking
• Metrics
captured
directly
by
third
party
services
(like
GA)
and
by
tracking
in-­‐game
ac;vity
of
users
on
servers

3. GeKng
Started
–
What
happens
if
you
do
not
‘Analy;cs’
?

4.
Who
decides
what
goes
in
?
No
..really
..
Why
Do
We
need
it?
• To
be
aware
of
what
works
and
what
doesn’t
• To
target
users
in
a
smarter
way
–
for
promo;ons,
acquisi;ons
etc.
• To
roll
out
features
that
maBer
• Anybody
involved
in
the
project
can
contribute
to
the
process
of
seKng
up
an
in-­‐game
analy;cs
system
• The
design
and
product
teams
work
in
tandem
as
they
define
the
iden0ty
of
the
game
and
need
a
set
of
specific
metrics
to
validate
the
same

5. How
should
it
behave
(or
ideally
should
behave)?
Be
Accurate
Be
Quick

6. How
does
the
system
work?
Product
Goals
Translate
into
data
requirements
Incorporate
into
the
system
Analyze
results

7. Pillars
of
Life
(
in
this
scenario)
• Acquisi&on
–
New
installs
• Reten&on
–
%
people
installing
on
day
X
,ac&ve
on
your
app
today
• Engagement
–
Time
based
%
of
returning
users
(weekly
or
Monthly)
• Mone&za&on
–
Show
me
the
money
!

8. A
Case
Study

9. Some
thumb
rules
• Always
have
a
hypothesis
(or
hypotheses)
for
a
problem
– Don’t
fret
about
geKng
this
(them)
wrong.
Its
what
we
are
going
to
prove/not
prove
using
the
analysis
• Make
sure
you
use
the
right
amount
of
data
for
your
analysis
– Too
Much
:
Needlessly
cumbersome
and
maybe
inconclusive
– Too
liBle
:
Inconclusive
• Explore
all
possible
reasons
for
a
par;cular
finding
in
the
data
• There
may
not
always
be
a
problem
to
solve.
It
can
be
used
to
do
usual
tasks
in
a
beBer
way.

10. Situa;on
1
111
129
120
125
157
136
130
122
105
148
121
141
154
154
134
122
126
127
150
0
20
40
60
80
100
120
140
160
180
New
installs
New
installs
• In
talks
with
vendors
for
acquisi;on
drive
• Very
important
to
acquire
the
right
kind
of
users
if
you
want
to
make
the
game
have
stable
numbers
ajer
the
acquisi;on
has
happened

11. Hypothesis
1
:
Acquisi;on
:
A
discovery
driven
approach
I
think
a
geographical
skew
exists
in
my
user
base.
I
need
to
target
my
acquisi;ons
in
a
smarter
way
Approaches
:
• Look
at
Geographical
skew
in,
Ac;ve
users,
New
users,
Engagement
• Depending
on
whether
we
want
the
maximum
people
to
download,
or
s;ck
around
ajer
downloading
,we
can
choose
the
target
loca;on
• Further
Analyses
:
• Deep
dive
into
the
data
to
see
if
a
carrier
skew
exists
City
DAU
WAU
Installs
Weekly
Eng.
Delhi
500
3500
300
14%
Pune
200
2000
200
10%
Mumbai
150
2500
180
6%
Bangalore
550
1000
50
55%
Chennai
400
1500
50
27%
1800
10500
17%

12. Problem
2
:
Reten;on
is
dipping
very
low
from
D3
to
D4
resul;ng
in
very
low
D7
–
mul;ple
reasons
–
content,
Game
features,
player
aspira;on
45
40
35
22
20
18
16
0
5
10
15
20
25
30
35
40
45
50
D1
D2
D3
D4
D5
D6
D7
Dx
Reten;on
• The
D1
,
D7
numbers
are
respectable
on
their
own
• This
is
not
a
problem
that
will
break
the
game
• Do
we
s;ll
want
to
go
ahead
and
run
stats?
–
Of
Course!

13.
Hypothesis
:
My
users
are
exhaus;ng
the
content
very
fast
Findings/insights
:
• Ini;al
hypothesis
was
wrong
–
ques;ons
unanswered
• Further
Analyses
:
• CIR
also
low
for
certain
topics
–
ques;on
difficulty
problem
• #
of
people
playing
each
topic
–
Is
nature
of
the
topic
a
problem?
Topic
Name
Total
ques;ons
Ques;ons
answered
CIR
Ques;ons
unanswered
Topic
1
200
78
1
122
topic
2
200
165
0.8
35
topic
3
200
50
0.5
150
topic
4
200
44
0.3
156
topic
5
200
150
1
50

14. Problem
3
:
Low
Weekly
Engagement
:
%
of
weekly
ac;ve
users
playing
today
(DAU/WAU)
20
25
28
15
26
21
20
0
5
10
15
20
25
30
06/02/15
06/01/15
5/31/2015
5/30/2015
5/29/2015
5/28/2015
5/27/2015
Weekly
Engagement
Weekly
Engagement
• Avg.
Weekly
engagement
is
around
20%,
meaning
only
1/5
of
the
users
in
the
last
week
have
returned
to
play
the
game
• Problem
:
In
the
presence
of
content
and
adequate
player
aspira;on,
the
features
in
the
game
are
the
likely
cause

15.
Hypothesis
:
Users
are
only
interested
in
playing
with
friends
as
opponents
Findings:
• The
finding
seems
to
be
inline
with
the
data
extracted.
Hypothesis
confirmed
• Dis;nct
difference
in
the
behavior
of
users
playing
challenges
with
friends
and
random
players
• Further
Analyses
:
• Look
into
why
people
are
sending
such
few
challenges
to
friends
• Check
if
a
loca;on
skew
exists
for
players
matchmaking
Random
Topic
Name
Challenges
sent
Challenges
completed
Comple;on
Rate
Topic
1
250
78
31%
topic
2
125
65
52%
topic
3
450
150
33%
topic
4
500
44
9%
topic
5
650
150
23%
Friends
Topic
Name
Challenges
sent
Challenges
completed
Comple;on
Rate
Topic
1
35
34
97%
topic
2
60
50
83%
topic
3
55
50
91%
topic
4
20
16
80%
topic
5
80
65
81%

16. Is
that
all?
–
Not
by
a
long
shot

17. Ques;ons?